Best presentation software of 2024

Make perfect slides for speeches and talks

Best overall

Best for branding, best for marketing, best for themes, best for media, best unique.

  • How we test

The best presentation software makes it simple and easy to create, manage, and share slideshow presentations.

A person setting up files to import into Powerpoint.

1. Best overall 2. Best for branding 3. Best for marketing 4. Best for themes 5. Best for media 6. Best unique 7. FAQs 8. How we test

Presentation software runs at the heart of business sales, management, and development, so it's important to ensure you have the best presentation software for your needs. This is especially when looking to share ideas, concepts, and workflows, and the ability to present these in a simple and understandable way is essential.

However, while presentation software has been traditionally limited to text and images, it has widened its ability to work with different media such as video and audio . 

Therefore it's important for the best presentation software to not simply be easy and simple to use, but also be able to support additional media so that presentations can be more engaging, lively, and ultimately serve their purpose in educating and updating their intended audience.

Below we've listed the best presentation software currently on the market.

We've also listed the best free presentation software .

The best office software in the world is: Microsoft 365

<a href="" data-link-merchant="SkimLinks -"" target="_blank" rel="nofollow"> The best office software in the world is: Microsoft 365 There are many different office software suites out there, but Microsoft Office remains the original and best, offering an unsurpassed range of features and functionality that rivals just can't match.

Even better, <a href="" data-link-merchant="SkimLinks -"" data-link-merchant="SkimLinks -"" target="_blank" rel="nofollow">Microsoft 365 - previously branded as Office 365 - is a cloud-based solution which means you can use it on any computer, mobile device, or smartphone, without having to worry about compatibility. All your files are saved in the cloud and synced between devices, so you can begin work on a document at home or in the office, then continue working on it on the go.

<a href="" data-link-merchant="SkimLinks -"" data-link-merchant="SkimLinks -"" data-link-merchant="SkimLinks -"" target="_blank" rel="nofollow"> You can sign up to Microsoft 365 here .

The best presentation software of 2024 in full:

Why you can trust TechRadar We spend hours testing every product or service we review, so you can be sure you’re buying the best. Find out more about how we test.

Microsoft PowerPoint website screenshot

1. PowerPoint

Our expert review:

Reasons to buy

For most people, Microsoft 's PowerPoint remains the original and best of all the presentation software platforms out there. While other companies have managed to catch up and offer rival products worthy of consideration, the fact is that PowerPoint's familiar interface and ubiquitous availability means it remains a favorite for the majority of people.

On the one hand, it's long been a staple of the hugely popular Microsoft Office suite, meaning that for most users this is going to be the first - and last - presentation software they are going to need to use.

Additionally, Microsoft has made PowerPoint, along with their other office products, available as free apps (with limited functionality) on both iOS and Android for mobile use, meaning it's even harder to avoid them. And this is before we even consider the inclusion of PowerPoint in Microsoft's cloud-based Microsoft 365.

It does everything necessary that you'd expect of presentation software, allowing you to add text and media to a series of slides, to accompany a talk and other presentations. There are easy-to-use templates included to help spice things up a little, but even a general user with little experience of it is likely to find themselves able to use PowerPoint without much trouble at all.

Overall, it's hard to go wrong with PowerPoint, and although Microsoft 365 has a nominal cost, the apps are free to use even if they do have more limited functionality.

Read our full Microsoft PowerPoint review .

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CustomShow website screenshot

2. CustomShow

Reasons to avoid.

Branding says a lot about a business, and it’s something firms need to get right from day one – from a good logo to a suitable font. CustomShow is business presentation software that puts all these elements of branding first.

Using the system, you can design and present customized, branded presentations that reflect your company and the products you offer, featuring the aforementioned logo and custom fonts. As well as this, you get a slide library and analytics to ensure your presentations are a success.

What’s more, you can import presentations into the software, and use it to tweak them further. There’s also integration with SalesForce , and because the platform is cloud-based, you can access your presentations on computers, tablets, and smartphones. 

Considering the focus on branding, this offering could be good for marketing and sales teams, and it's used by major companies such as HBO and CBS Interactive.

ClearSlide website screenshot

3. ClearSlide

Just like CustomShow, ClearSlide has a niche focus for companies. The platform is targeted at firms looking to generate successful marketing campaigns, pushing sales via presentations (and more), not least through a range of analytics and metrics to work for sales and marketing.

With the product, you can upload a range of files, including PowerPoint, Keynote, PDF, and Excel. ClearSlide is integrated with other platforms, including Google Drive, Dropbox, and Salesforce.

This system is pretty complex and may offer too many irrelevant features for some businesses, but you can create customized content that reflects your company and the message you’re trying to get out to customers. There are also some good metrics and analysis features, and you can sign up for a free trial before making any decisions.

The real strength of ClearSlide comes from its focus on sales and marketing data, not least being able to track user engagement alongside other metrics.

Haiku Deck website screenshot

4. Haiku Deck

Any presentation app will allow you to personalize your slides to at least some extent, but Haiku Deck goes one step further than the competition. It comes with a wide range of themes suited to different needs, and you also get access to 40 million free images from the Creative Commons collection.

When it comes to creating a presentation, you have the option to do so on the web, which means your presentation is accessible across a range of mobile devices as well as desktops. Regardless of the device used, you’re able to select from a variety of different fonts, layouts, and filters to make the perfect presentation.

The great thing about these various customization options is that they’re categorized into different industries and use cases. For instance, you’ll find themes for teaching, cooking, real estate, and startups. Most of the features require you to be online, but hopefully, you’ll have a sturdy net connection wherever you go.

SlideDog website screenshot

5. SlideDog

It’s all too easy to end up creating a presentation that’s unappealing, and the last thing you want to do is make the audience fall asleep. SlideDog lets you combine almost any type of media to create a rich presentation that’s sure to keep the viewers’ peepers open, avoiding the ‘cookie cutter’ look that makes presentations seem dull.

Marketed as a web-based multimedia presentation tool, it gives you the ability to combine PowerPoint presentations, graphics, PDF files, Prezi presentations, web pages, pictures, videos, and movie clips. You can drag these into custom playlists and display them to your audience with ease.

You’re able to remotely control your presentations and playlists from your smartphone, the web, or a secondary computer, and there’s also the option to share slides in real-time. Audience members can even view your slide from their own devices by clicking a link. That’s a handy feature if you’re looking to create an immersive presentation experience.

SlideDog is probably the cheapest of the presentation software featured, with a free account that will cover the essential features. However, for live sharing and premium support, you need to upgrade.

Read our full SlideDog review .

Prezi website screenshot

Prezi is one of the more unique presentation tools. Instead of presenting your graphics and text in a slide-to-slide format, you can create highly visual and interactive presentation canvases with the goal of “emphasizing the relationship between the ideas”.

Presentations can also be tailored to the specific audience, as this is a flexible platform that’s capable of skipping ahead, or veering off into a side topic, without having to flip through all the slides to get to a particular bit.

For business users, there are a variety of handy tools available. By downloading Prezi , you can build and edit presentations with your colleagues in real-time, which is perfect for companies with teams based around the globe.

When you have created a presentation you’re happy with, you can present it live (in HD) and send a direct link to viewers. There are some analysis tools here, too – you can see who’s accessed your presentation, which parts of it, and for how long. The app is available for Mac and Windows devices.

Read our full Prezi review .

Other presentation software to consider

Google Slides  is part of the Google Workspace (formerly G Suite) office platform intended as an online alternative to Microsoft Office. It may seem a little limited by comparison to PowerPoint, but as it's browser-based that means cross-platform compatibility. Additionally, it allows for collaborative work, and Google Slides really works well here. On top of the fact that it integrates with the rest of the Google Workspace apps, not least Google Drive, and you have a contender. 

Zoho Show  is another of the many, many tools and apps that Zoho has made available for business use. It also operates in the cloud so it's accessible to any device with a browser, and it also allows for collaborative work. You can also easily share the link for users to download, or provide a live presentation online. The updated version has a simpler and easier to use interface and comes with a free version and a paid-for one with expanded features.

Evernote  is normally thought of as just note-taking software, but it does provide the option to create a presentation you can share online or with an audience. In that regard, it's a little more limited than the other options in not being dedicated presentation software. However, as an easy and handy way to pull together a presentation quickly, it could serve as a backup or last-minute option, especially if Evernote is already being commonly used by you.

LibreOffice Impress  is part of the open-source suite offered as a free alternative to Microsoft Office, and comes with a powerful array of tools and editing options for your presentation, not least working with 3D images. It's supported by a large community, so it's easy to find an array of additional templates. If there is a limitation it's that it's software you download and install rather than web-based, but any presentations created should be easily portable to the web if needed.

Adobe Spark  does things a bit differently, as rather than just use images it's geared toward video as well. This makes for potentially more powerful multimedia presentations, especially as Adobe also has a big selection of photos and images available for its users. There is a free tier for core features but requires a subscription for custom branding, personalized themes, and support.

Slides  comes with a lot of features in an easy-to-use interface, and involves setting up presentations using drag and drop into an existing grid. It's also internet-based so there's no software to download, and it only requires a browser to use and access. 

Presentation software FAQs

Which presentation software is best for you.

When deciding which presentation software to download and use, first consider what your actual needs are, as sometimes free platforms may only provide basic options, so if you need to use advanced tools you may find a paid platform is much more worthwhile. Additionally, free and budget software options can sometimes prove limited when it comes to the variety of tools available, while higher-end software can really cater for every need, so do ensure you have a good idea of which features you think you may require for your presentation needs.

How we tested the best presentation software

To test for the best presentation software we first set up an account with the relevant software platform, whether as a download or as an online service. We then tested the service to see how the software could be used for different purposes and in different situations. The aim was to push each software platform to see how useful its basic tools were and also how easy it was to get to grips with any more advanced tools.

Read how we test, rate, and review products on TechRadar .

We've also featured the best alternatives to Microsoft Office .

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Nicholas Fearn is a freelance technology journalist and copywriter from the Welsh valleys. His work has appeared in publications such as the FT, the Independent, the Daily Telegraph, The Next Web, T3, Android Central, Computer Weekly, and many others. He also happens to be a diehard Mariah Carey fan!

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Present Your Data Like a Pro

  • Joel Schwartzberg

presentation analysis tool

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

presentation analysis tool

  • JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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The 19 Best Data Visualization Tools and Software for 2024

Tim King

  • Best Practices ,

presentation analysis tool

Solutions Review’s listing of the best data visualization tools is an annual mashup of products that best represent current market conditions, according to the crowd. Our editors selected the best data visualization tools based on each solution’s Authority Score; a meta-analysis of real user sentiment through the web’s most trusted business software review sites and our own proprietary five-point inclusion criteria.

The editors at Solutions Review have developed this resource to assist buyers in search of the bedata visualization tools to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best data visualization tools providers all in one place. We’ve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action.

Note: The best data visualization tools are listed in alphabetical order.

The Best Data Visualization Tools

  • ChartBlocks

presentation analysis tool

Platform: ChartBlocks

Description: ChartBlocks offers an online chart-building tool that lets you import your data, design a chart, and then share it with ease. No coding is required to enable users to build charts via a simple designer. You can also choose from dozens of different chart types and pull data from almost any source. ChartBlocks supports creating charts that pull data from multiple sources as well. The tool touts a data import wizard that takes you through the entire process step-by-step.

presentation analysis tool

Platform: Cyfe

Description:  Cyfe offers a business dashboard creator that lets you monitor and visualize important data (social media, analytics, marketing, sales, support) in one place. The product features a number of pre-built dashboard templates that can be modified to meet specific needs. Users can also pull data from popular sources, and Cyfe touts more than 100 integrations and 250 metrics out-of-the-box. There are automated reports and widget alerts as well, which can be downloaded or scheduled.

presentation analysis tool

Platform: Databox

Description: Databox offers a business analytics platform for KPI dashboards that pulls organizational data into one place so users can track performance and discover insights in real-time. The product lets you mix and match metrics from different sources into one dashboard. Databox features a DIY Dashboard Designer that enables users to pull the metrics they need, visualize KPIs in a number of ways, and build dashboards without the need for special coding or design skills.


presentation analysis tool

Platform: Datawrapper

Description: Datawrapper provides a web tool for creating a variety of interactive charts and maps. No code or design skills are required, and charts can be embedded in your website. Users can simply copy data from Excel or Google Sheets or upload CSV files or link to a URL for live updating. The basic (free) version allows for 1 user, 1 locator map, and 10,000 chart views per month. There are also several paid offerings that include access for additional users and more advanced capabilities.

  • Easy Insight

presentation analysis tool

Platform: Easy Insight

Description: Easy Insight offers a cloud business intelligence tool that enables you to create reports and dashboards and visualize data through charts and tables. The product also enables the combination of different services in databases so users can create a single view of customers across multiple apps. Easy Insight also touts the ability to create and update data right from your reports, as well as drill from charts into detailed data and click right back to the source.

presentation analysis tool

Platform: Exago BI

Description: Exago BI is completely web-based and a prime embedded solution for software companies looking to provide ad hoc reporting, dashboards, and business analysis to their clients. Integration with web-based SaaS and or on-prem applications allows non-technical users to create reports and dashboards with no IT intervention. Exago BI offers a wide variety of advanced features including interactive data visualizations, geo-maps, drill-downs, user-defined formulas, and data merging into predefined templates.

  • Golden Software

presentation analysis tool

Platform: Grapher

Description: Golden Software’s Grapher is a full-function graphing application for scientists, engineers, and business professionals. The product touts more than 80 unique and customizable 2D and 3D graph styles. Graphs can be enhanced with legends, titles and labels, summation plots, graph magnifiers, and statistical reports. Grapher also includes extensive analysis charts, tools, and statistical features to identify trends, find new insights and provide a holistic view of data.

presentation analysis tool

Platform: Geckoboard

Description: Geckoboard offers a data dashboard product that makes it easy for you to share important data, metrics and KPIs clearly. The tool helps to streamline data from spreadsheets, databases and other tools for presentation purposes. Real-time metrics and more than 60 integrations make it easy to create live dashboards. Geckoboard also touts an unmissable summary of key performance indicators. Users can easily share dashboards with team members through email, chat, documents, or bookmark them for quick access.

Learn more and compare products with the Solutions Review Buyer’s Guide for Analytics and Business Intelligence Platforms .

presentation analysis tool

Platform: Grow

Description: Grow offers a no-code, full-stack business intelligence and data visualization tool. The product features data integration capabilities that enable users to connect, store, and blend data from hundreds of data sources. Grow then provides the ability to marry and transform disparate data sources so you can filter, slice, and explore different visualizations. The built-in data explorer defines how you want to navigate data via charts and graphs which are displayed in metrics and dashboards.

presentation analysis tool

Platform: GoodData

Description: The GoodData platform provides creation, delivery, and automated management of analytics at scale that allows organizations to deliver insights to customers, partners, and other users. GoodData is designed to support any analytical product roadmap and enables customers to offer their partners any data source, visualization, interaction pattern, or integration they need. The tool offers analytics and expertise to operationalize intelligent decisions within business applications and business processes.

  • iDashboards

presentation analysis tool

Platform: iDashboards

Description: iDashboards offers a data visualization platform for customers in a wide array of vertical industries. The product features customizable and flexible dashboard tools that integrate with popular data sources. iDashboards lets users quickly view and analyze critical performance metrics as well. Dashboards can be created on any device while the solution touts hundreds of chart options for data visualizations of any kind. Users can share dashboards internally or publicly to keep the back-end secure. Role-based permissions and other integrations are also available.

presentation analysis tool

Platform: Infogram

Description: Infogram offers an intuitive data visualization tool for users of varying skill levels. The product features ready-to-use templates and custom templates with your brand, colors, fonts, and logo. Infogram also touts interactive content via object animations like setting objects to zoom, bounce, flip and fade, as well as enhanced interactivity through tooltips, tabs, clickable legends, and linking. Teams can be organized into groups and permissions can be set by role.

presentation analysis tool

Platform: Klipfolio

Description: Klipfolio offers a custom dashboard and reporting tool that combines formulas, functions, and data modeling to help users build actionable reports. Customers can choose from pre-built visualizations in the Klip gallery or incorporate proprietary branding with custom themes. Klipfolio enables the sharing of dashboards and reports via view-only links, PDFs, scheduled emails, or Slack messages. The solution provider offers free add-on products like PowerMetrics (lightweight data analytics) and MetricHQ (online dictionary of metrics and KPIs) as well.

Platform: Power BI

Related products: Power BI Desktop, Power BI Report Server

Description: Microsoft is a major players in enterprise BI and analytics. The company’s flagship platform, Power BI, is cloud-based and delivered on the Azure Cloud. On-prem capabilities also exist for individual users or when power users are authoring complex data mashups using in-house data sources. Power BI is unique because it enables users to do data preparation, data discovery, and dashboards with the same design tool. The platform integrates with Excel and Office 365, and has a very active user community that extends the tool’s capabilities.

  • Panintelligence

presentation analysis tool

Platform: pi

Description: pi is a three-in-one solution that combines business intelligence with simple reporting and predictive analytic capabilities via self-service dashboards. Users can white label the software and embed it into existing SaaS products or hook it up to cloud data warehousing and ETL tools for a more seamless experience. pi also centralizes various sources of data and uses AI analytics and automation to make insights readily available while dashboards can be completely customized for each client without the need to write code.

  • Tableau Software

Platform: Tableau Desktop

Related products:  Tableau Prep, Tableau Server, Tableau Online, Tableau Data Management

Description: Tableau offers an expansive visual BI and analytics platform, and is widely regarded as the major player in the marketplace. The company’s analytic software portfolio is available through three main channels: Tableau Desktop, Tableau Server, and Tableau Online. Tableau connects to hundreds of data sources and is available on-prem or in the cloud. The vendor also offers embedded analytics capabilities, and users can visualize and share data with Tableau Public.

presentation analysis tool

Platform:  Toucan Toco

Description: Toucan offers a data storytelling platform designed for the non-technical decision-maker. The product lets users communicate actionable insights via a built-in no-code framework. Toucan Toco adapts to usage habits by deploying apps to all devices quickly without additional work, installation, or post-deployment maintenance. Included collaboration capabilities can be utilized via native integrations with Teams and Slack. The tool also touts an adaptive design and the ability to embed charts to websites or other platforms.

presentation analysis tool

Platform: Visme

Description:  Visme is an all-in-one visual communication tool that lets users create presentations, infographics, reports, social graphics, and more. The tool features more than 40 different chart and diagram types bar graph, pie chart, flowcharts, diagrams, line graphs, histograms), as well as live data integration and an easy-to-use graph maker. Graphs can be easily shared, downloaded, or embedded as well. Visme touts a free library of chart templates, millions of stock photos and icons, the ability to customize brand fonts and colors, and a drag-and-drop interface.


Platform: Zoho Analytics

Description: Zoho Analytics is a self-service BI and data analytics tool that lets you incorporate data from a wide range of sources, blend it together, and create cross-functional reports and dashboards. The product features a drag-and-drop designer, as well as different visualization tools to drill down to specifics. A “smart” assistant called Zia can answer questions in the form of reports and KPI widgets via AI, machine learning and natural language processing. Users can share and publish reports and smart data alerts ping you when outliers or anomalies happen.

This article was written by Tim King on October 9, 2023

  • Best Data Visualization Software
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Executive Editor

Tim is Solutions Review's Executive Editor and leads coverage on data management and analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in Data Management, Tim is a recognized industry thought leader and changemaker. Story? Reach him via email at tking@solutionsreview dot com.

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The 11 Best Data Analytics Tools for Data Analysts in 2024

As the field of data analytics evolves, the range of available data analysis tools grows with it. If you’re considering a career in the field, you’ll want to know: Which data analysis tools do I need to learn?

In this post, we’ll highlight some of the key data analytics tools you need to know and why. From open-source tools to commercial software, you’ll get a quick overview of each, including its applications, pros, and cons. What’s even better, a good few of those on this list contain AI data analytics tools , so you’re at the forefront of the field as 2024 comes around.

We’ll start our list with the must-haves, then we’ll move onto some of the more popular tools and platforms used by organizations large and small. Whether you’re preparing for an interview, or are deciding which tool to learn next, by the end of this post you’ll have an idea how to progress.

If you’re only starting out, then CareerFoundry’s free data analytics short course will help you take your first steps.

Here are the data analysis tools we’ll cover:

  • Microsoft Excel
  • Jupyter Notebook
  • Apache Spark
  • Google Cloud AutoML
  • Microsoft Power BI

How to choose a data analysis tool

Data analysis tools faq.

So, let’s get into the list then!

1.  Microsoft Excel

Excel at a glance:

  • Type of tool: Spreadsheet software.
  • Availability : Commercial.
  • Mostly used for: Data wrangling and reporting.
  • Pros: Widely-used, with lots of useful functions and plug-ins.
  • Cons: Cost, calculation errors, poor at handling big data.

Excel: the world’s best-known spreadsheet software. What’s more, it features calculations and graphing functions that are ideal for data analysis.

Whatever your specialism, and no matter what other software you might need, Excel is a staple in the field. Its invaluable built-in features include pivot tables (for sorting or totaling data) and form creation tools.

It also has a variety of other functions that streamline data manipulation. For instance, the CONCATENATE function allows you to combine text, numbers, and dates into a single cell. SUMIF lets you create value totals based on variable criteria, and Excel’s search function makes it easy to isolate specific data.

It has limitations though. For instance, it runs very slowly with big datasets and tends to approximate large numbers, leading to inaccuracies. Nevertheless, it’s an important and powerful data analysis tool, and with many plug-ins available, you can easily bypass Excel’s shortcomings. Get started with these ten Excel formulas that all data analysts should know .

Python at a glance:

  • Type of tool: Programming language.
  • Availability: Open-source, with thousands of free libraries.
  • Used for: Everything from data scraping to analysis and reporting.
  • Pros: Easy to learn, highly versatile, widely-used.
  • Cons: Memory intensive—doesn’t execute as fast as some other languages.

  A programming language with a wide range of uses, Python is a must-have for any data analyst. Unlike more complex languages, it focuses on readability, and its general popularity in the tech field means many programmers are already familiar with it.

Python is also extremely versatile; it has a huge range of resource libraries suited to a variety of different data analytics tasks. For example, the NumPy and pandas libraries are great for streamlining highly computational tasks, as well as supporting general data manipulation.

Libraries like Beautiful Soup and Scrapy are used to scrape data from the web, while Matplotlib is excellent for data visualization and reporting. Python’s main drawback is its speed—it is memory intensive and slower than many languages. In general though, if you’re building software from scratch, Python’s benefits far outweigh its drawbacks. You can learn more about Python in our full guide .

R at a glance:

  • Availability: Open-source.
  • Mostly used for: Statistical analysis and data mining.
  • Pros: Platform independent, highly compatible, lots of packages.
  • Cons: Slower, less secure, and more complex to learn than Python.

R, like Python, is a popular open-source programming language. It is commonly used to create statistical/data analysis software.

R’s syntax is more complex than Python and the learning curve is steeper. However, it was built specifically to deal with heavy statistical computing tasks and is very popular for data visualization. A bit like Python, R also has a network of freely available code, called CRAN (the Comprehensive R Archive Network), which offers 10,000+ packages.

It integrates well with other languages and systems (including big data software) and can call on code from languages like C, C++, and FORTRAN. On the downside, it has poor memory management, and while there is a good community of users to call on for help, R has no dedicated support team. But there is an excellent R-specific integrated development environment (IDE) called RStudio , which is always a bonus!

4.  Jupyter Notebook

Jupyter Notebook at a glance:

  • Type of tool: Interactive authoring software.
  • Mostly used for: Sharing code, creating tutorials, presenting work.
  • Pros: Great for showcasing, language-independent.
  • Cons: Not self-contained, nor great for collaboration.

Jupyter Notebook is an open-source web application that allows you to create interactive documents. These combine live code, equations, visualizations, and narrative text.

Imagine something a bit like a Microsoft word document, only far more interactive, and designed specifically for data analytics! As a data analytics tool, it’s great for showcasing work: Jupyter Notebook runs in the browser and supports over 40 languages, including Python and R. It also integrates with big data analysis tools, like Apache Spark (see below) and offers various outputs from HTML to images, videos, and more.

But as with every tool, it has its limitations. Jupyter Notebook documents have poor version control, and tracking changes is not intuitive. This means it’s not the best place for development and analytics work (you should use a dedicated IDE for these) and it isn’t well suited to collaboration.

Since it isn’t self-contained, this also means you have to provide any extra assets (e.g. libraries or runtime systems) to anybody you’re sharing the document with. But for presentation and tutorial purposes, it remains an invaluable data science and data analytics tool.

5.  Apache Spark

Apache Spark at a glance:

  • Type of tool: Data processing framework
  • Availability: Open-source
  • Mostly used for: Big data processing, machine learning
  • Pros: Fast, dynamic, easy to use
  • Cons: No file management system, rigid user interface

Apache Spark is a software framework that allows data analysts and data scientists to quickly process vast data sets. It was first developed in 2012, it’s designed to analyze unstructured big data, Spark distributes computationally heavy analytics tasks across many computers.

While other similar frameworks exist (for example, Apache Hadoop ) Spark is exceptionally fast. By using RAM rather than local memory, it is around 100x faster than Hadoop. That’s why it’s often used for the development of data-heavy machine learning models .

It even has a library of machine learning algorithms, MLlib , including classification, regression, and clustering algorithms, to name a few. On the downside, consuming so much memory means Spark is computationally expensive. It also lacks a file management system, so it usually needs integration with other software, i.e. Hadoop.

6. Google Cloud AutoML

Google Cloud AutoML at a glance:

  • Type of tool: Machine learning platform
  • Availability:  Cloud-based, commercial
  • Mostly used for:  Automating machine learning tasks
  • Pros: Allows analysts with limited coding experience to build and deploy ML models , skipping lots of steps
  • Cons:  Can be pricey for large-scale projects, lacks some flexibility

A serious proposition for data analysts and scientists in 2024 is Google Cloud’s AutoML tool. With the hype around generative AI in 2023 set to roll over into the next year, tools like AutoML but the capability to create machine learning models into your own hands.

Google Cloud AutoML contains a suite of tools across categories from structured data to language translation, image and video classification. As more and more organizations adopt machine learning, there will be a growing demand for data analysts who can use AutoML tools to automate their work easily.

SAS at a glance:

  • Type of tool: Statistical software suite
  • Availability: Commercial
  • Mostly used for: Business intelligence, multivariate, and predictive analysis
  • Pros: Easily accessible, business-focused, good user support
  • Cons: High cost, poor graphical representation

SAS (which stands for Statistical Analysis System) is a popular commercial suite of business intelligence and data analysis tools. It was developed by the SAS Institute in the 1960s and has evolved ever since. Its main use today is for profiling customers, reporting, data mining, and predictive modeling. Created for an enterprise market, the software is generally more robust, versatile, and easier for large organizations to use. This is because they tend to have varying levels of in-house programming expertise.

But as a commercial product, SAS comes with a hefty price tag. Nevertheless, with cost comes benefits; it regularly has new modules added, based on customer demand. Although it has fewer of these than say, Python libraries, they are highly focused. For instance, it offers modules for specific uses such as anti-money laundering and analytics for the Internet of Things.

8. Microsoft Power BI

Power BI at a glance:

  • Type of tool: Business analytics suite.
  • Availability: Commercial software (with a free version available).
  • Mostly used for: Everything from data visualization to predictive analytics.  
  • Pros: Great data connectivity, regular updates, good visualizations.
  • Cons: Clunky user interface, rigid formulas, data limits (in the free version).

At less than a decade old, Power BI is a relative newcomer to the market of data analytics tools. It began life as an Excel plug-in but was redeveloped in the early 2010s as a standalone suite of business data analysis tools. Power BI allows users to create interactive visual reports and dashboards , with a minimal learning curve. Its main selling point is its great data connectivity—it operates seamlessly with Excel (as you’d expect, being a Microsoft product) but also text files, SQL server, and cloud sources, like Google and Facebook analytics.

It also offers strong data visualization but has room for improvement in other areas. For example, it has quite a bulky user interface, rigid formulas, and the proprietary language (Data Analytics Expressions, or ‘DAX’) is not that user-friendly. It does offer several subscriptions though, including a free one. This is great if you want to get to grips with the tool, although the free version does have drawbacks—the main limitation being the low data limit (around 2GB).

Tableau at a glance:

  • Type of tool: Data visualization tool.
  • Availability: Commercial.
  • Mostly used for: Creating data dashboards and worksheets.
  • Pros: Great visualizations, speed, interactivity, mobile support.
  • Cons: Poor version control, no data pre-processing.

If you’re looking to create interactive visualizations and dashboards without extensive coding expertise, Tableau is one of the best commercial data analysis tools available. The suite handles large amounts of data better than many other BI tools, and it is very simple to use. It has a visual drag and drop interface (another definite advantage over many other data analysis tools). However, because it has no scripting layer, there’s a limit to what Tableau can do. For instance, it’s not great for pre-processing data or building more complex calculations.

While it does contain functions for manipulating data, these aren’t great. As a rule, you’ll need to carry out scripting functions using Python or R before importing your data into Tableau. But its visualization is pretty top-notch, making it very popular despite its drawbacks. Furthermore, it’s mobile-ready. As a data analyst , mobility might not be your priority, but it’s nice to have if you want to dabble on the move! You can learn more about Tableau in this post .

KNIME at a glance:

  • Type of tool: Data integration platform.
  • Mostly used for: Data mining and machine learning.
  • Pros: Open-source platform that is great for visually-driven programming.
  • Cons: Lacks scalability, and technical expertise is needed for some functions.

Last on our list is KNIME (Konstanz Information Miner), an open-source, cloud-based, data integration platform. It was developed in 2004 by software engineers at Konstanz University in Germany. Although first created for the pharmaceutical industry, KNIME’s strength in accruing data from numerous sources into a single system has driven its application in other areas. These include customer analysis, business intelligence, and machine learning.

Its main draw (besides being free) is its usability. A drag-and-drop graphical user interface (GUI) makes it ideal for visual programming. This means users don’t need a lot of technical expertise to create data workflows. While it claims to support the full range of data analytics tasks, in reality, its strength lies in data mining. Though it offers in-depth statistical analysis too, users will benefit from some knowledge of Python and R. Being open-source, KNIME is very flexible and customizable to an organization’s needs—without heavy costs. This makes it popular with smaller businesses, who have limited budgets.

Now that we’ve checked out all of the data analysis tools, let’s see how to choose the right one for your business needs.

11. Streamlit

  • Type of tool:  Python library for building web applications
  • Availability:  Open-source
  • Mostly used for:  Creating interactive data visualizations and dashboards
  • Pros: Easy to use, can create a wide range of graphs, charts, and maps, can be deployed as web apps
  • Cons: Not as powerful as Power BI or Tableau, requires a Python installation

Sure we mentioned Python itself as a tool earlier and introduced a few of its libraries, but Streamlit is definitely one data analytics tool to watch in 2024, and to consider for your own toolkit.

Essentially, Streamlit is an open-source Python library for building interactive and shareable web apps for data science and machine learning projects. It’s a pretty new tool on the block, but is already one which is getting attention from data professionals looking to create visualizations easily!

Alright, so you’ve got your data ready to go, and you’re looking for the perfect tool to analyze it with. How do you find the one that’s right for your organization?

First, consider that there’s no one singular data analytics tool that will address all the data analytics issues you may have. When looking at this list, you may look at one tool for most of your needs, but require the use of a secondary tool for smaller processes.

Second, consider the business needs of your organization and figure out exactly who will need to make use of the data analysis tools. Will they be used primarily by fellow data analysts or scientists, non-technical users who require an interactive and intuitive interface—or both? Many tools on this list will cater to both types of user.

Third, consider the tool’s data modeling capabilities. Does the tool have these capabilities, or will you need to use SQL or another tool to perform data modeling prior to analysis?

Fourth—and finally!—consider the practical aspect of price and licensing. Some of the options are totally free or have some free-to-use features (but will require licensing for the full product). Some data analysis tools will be offered on a subscription or licencing basis. In this case, you may need to consider the number of users required or—if you’re looking on solely a project-to-project basis—the potential length of the subscription.

In this post, we’ve explored some of the most popular data analysis tools currently in use. The key thing to takeaway is that there’s no one tool that does it all. A good data analyst has wide-ranging knowledge of different languages and software.

CareerFoundry’s own data expert, Tom Gadsby, explains which data analytics tools are best for specific processes in the following short video:

If you found a tool on this list that you didn’t know about, why not research more? Play around with the open-source data analysis tools (they’re free, after all!) and read up on the rest.

At the very least, it helps to know which data analytics tools organizations are using. To learn more about the field, start our free 5-day data analytics short course .

For more industry insights, check out the following:

  • The 7 most useful data analysis methods and techniques
  • How to build a data analytics portfolio
  • Get started with SQL: A cheatsheet

What are data analytics tools?

Data analytics tools are software and apps that help data analysts collect, clean, analyze, and visualize data. These tools are used to extract insights from data that can be used to make informed business decisions.

What is the most used tool by data analysts?

Microsoft Excel continues to be the most widely used tool by data analysts for data wrangling and reporting. Big reasons are that it provides a user-friendly interface for data manipulation, calculations, and data viz.

Is SQL a data analysis tool?

Yes. SQL is a specialized programming language for managing and querying data in relational databases. Data analysts use SQL to extract and analyze data from databases, which can then be used to generate insights and reports.

Which tool is best to analyse data?

It depends on what you want to do with the data and the context. Some of the most popular and versatile tools are included in this article, namely Python, SQL, MS Excel, and Tableau.

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How to Create an Impressive Analytics Presentation Without Tearing Your Hair Out

“I want to know how my website is performing as my boss is asking me for our numbers last month urgently.” Can you prepare an analytics presentation for Thursday next week?


Stressed-out Client

Ever received a request like this on Friday afternoon from your clients when you’re wrapping up for the week? 

Before jumping into pulling numbers and creating charts, stop. Take a breath. 

It’s worthwhile to systematically break down the request and ask follow-up questions to ensure you’ve created a report fulfilling the request to a T. 

You’ll learn how to create an impressive analytics presentation for your clients without tearing your hair out. Read on! 

What is an Analytics Presentation? 

An analytics presentation compiles data metrics and insights from web analytics tools like Google Analytics, and Adobe Analytics collected in PowerPoint presentation slides or data visualization tools.

Business executives use these presentations’ data analysis and recommendations to review business performance and make strategic decisions. 

Your job, therefore, as an analyst, is to find and present relevant data and recommendations that will address the business concerns of the people reviewing the report and help them make decisions. 


Save time preparing for your next data analytics presentation with DashThis’s Google Analytics report template , gathering all essential web analytics metrics in a beautiful report to ace your next presentation.

5 Steps to Creating the Perfect Analytics Presentation for Your Clients

Step 1: ask questions to gather the correct information.

Don’t be afraid to probe further if you get a vague-sounding request from a client. Take the time to review their request and understand what they are asking. 

First, Sam Priddy from Tableau recommends writing down the request on paper or in your notes to understand the request and identify areas that need clarification. Ensure you and your client are on the same page when understanding requirements and scope. 

Once you’re clear, it’s time to dive deeper. Some questions to get you started:

  • What is the objective of the meeting? Understand if the session influences any decisions, like pricing decisions, budget allocations or more. For example, a discussion on content marketing strategy for lead generation might need metrics on content marketing’s effectiveness at improving conversion rate and business revenue. 
  • What’s the reporting period of the data? Are we looking at monthly, quarterly, or yearly data? 
  • Who will be attending this meeting or reviewing the presentation?  Each audience group cares about different things. A VP of sales might care about net new revenue and overall sales pipeline, while a head of content might care about the bounce rate of a sales landing page, the number of qualified leads and partnerships acquired. Put yourself in different people’s shoes and ensure your report contains relevant information.
  • What’s the desired outcome of the report? Are they looking for a summary of past performance, a pure presentation of numbers, or an interactive way to explore data in real-time to make optimization decisions? 

Tip: Present data effectively by asking yourself what metrics or outcomes each audience group cares about, what their stakeholders want from them and what they expect to see from the report. Research their roles and responsibilities, or ask your client for more information. 

With the answers to these questions, you can identify what information you need and what to discard from your report. Now you know how to approach your presentation and structure your report for maximum efficiency. 

Step 2: Establish Your Kpis

Now that you know the report’s objectives and who will be present at the meeting, ask yourself what KPIs and metrics provide the information your client needs. Good KPIs are always relevant to a stakeholder concern, business goal or marketing goal. A report from Mention and Livestorm details important marketing goals for companies in 2022 and examples of KPIs used to measure them.


How do you go about choosing KPIs from marketing and business goals? 

Let’s say your client has a business goal to build positive social proof and establish trust in their target market for their e-commerce store. 

Your marketing goal will then be to encourage more online reviews for them in the next 6 months, and you’ll use the number of online reviews from their website, visitors, on Google and on marketplace listings as a KPI to track your progress. 

Step 3: Create Your Analytics Dashboard

You could always present your data in a PowerPoint (PPT) slide deck or Excel spreadsheet. But if you’re looking to add that extra spice to your analytics presentation besides a well-formatted PowerPoint template and wow your clients, think about having your data in an interactive dashboard.

An interactive dashboard turns meeting attendees into active participants. They can analyze data for themselves, filter, sort and re-arrange data from nearly any angle. It also helps them follow along in your presentation, making the session more useful. 

Here’s how to create an interactive data visualization in minutes with DashThis.

Step 1: Sign up for an account with DashThis. Our free 15-day trial allows you to see if we’re the right fit for you.

Step 2: Choose from one of our pre-set report templates for you to build your report. Our reports include easy visualization functionality like pie charts and bar graphs for presentation design.

Step 3: Choose from over 40+ integrations and connect your digital marketing and social media tools in seconds.


Step 4:  Select the metrics to include in your report from DashThis’ preset widgets.

Step 5: Add as many data points as you need to set up your report. Once done, save your completed report as a template so you can reuse the template for future presentations with clients, saving even more time!

Tip:  Send your report to your client or people you know will be present at a meeting as a pre-read a few hours before the meeting. This helps you have more productive meetings and makes you and your client look good in front of everyone! 

Step 4: Include Action Points

Remember, data is only valid when synthesized and interpreted. 

Any data you include should answer the ‘So What’ question – what does this data point mean in the context of business goals, and what to do next? 


DashThis allows you to annotate graphs and metrics in your report with notes and comments; useful for summarizing key points, explaining anomalies and including helpful context.

Once you’ve contextualized the data, proceed to develop recommendations, especially if you’re involved in initiatives that influence these metrics.

  • Organize multiple recommendations by priority 
  • Assign owners for whoever is responsible for carrying out these recommendations
  • Align on the next time you’ll review the metric. 

Step 5: Leave Room for Feedback and Discussion

A presentation is a two-way conversation. You’ll want to leave time for your audience to give feedback and ask questions about the data. 

Some tips on handling questions during a presentation like a pro: 

  • Remember, the people you’re presenting to might be seeing the numbers you’re presenting for the first time. Take breaks while preparing the report to see it with fresh eyes or check in with your team to review for any points you may have missed. 
  • Anticipate questions by reviewing the people’s roles at the meeting and what they’ll be looking for. 
  • Use complete sentences in your headers to highlight what’s important in a chart. A chart about social proof can have the heading “The number of customer reviews across all channels increased by 200% in the last 12 months”. 
  • Use annotations to highlight anomalies in the data, paying attention to spikes or dips in essential metrics. 
  • Summarize the presentation with one key takeaway that directly answers the business concerns outlined in step 1. Your clients and stakeholders are busy, and doing the work to distill your presentation ensures it delivers more impact. 

Ultimately, you want to be so familiar with your data that you know it like the back of your hand.

Deliver Client Analytics Presentations Like a Pro

Following these 5 steps will equip you with all the tools you need to ace your next client presentation,

  • Ask questions and clarify what is needed.
  • Establish KPIs relevant to business goals and address stakeholder concerns
  • Make your data interactive to aid comprehension 
  • Answer the So-What question in your action steps and recommendations
  • Anticipate questions and feedback 

And finally, you learn by doing. Every client looks for different things and has various concerns.

The more you work with them and seek feedback to improve, you’ll become known as the presentation master in no time. 

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  • 13 min read

What is Data Interpretation? Methods, Examples & Tools

What is Data Interpretation Methods Examples Tools

What is Data Interpretation?

  • Importance of Data Interpretation in Today's World

Types of Data Interpretation

Quantitative data interpretation, qualitative data interpretation, mixed methods data interpretation, methods of data interpretation, descriptive statistics, inferential statistics, visualization techniques, benefits of data interpretation, data interpretation process, data interpretation use cases, data interpretation tools, data interpretation challenges and solutions, overcoming bias in data, dealing with missing data, addressing data privacy concerns, data interpretation examples, sales trend analysis, customer segmentation, predictive maintenance, fraud detection, data interpretation best practices, maintaining data quality, choosing the right tools, effective communication of results, ongoing learning and development, data interpretation tips.

Data interpretation is the process of making sense of data and turning it into actionable insights. With the rise of big data and advanced technologies, it has become more important than ever to be able to effectively interpret and understand data.

In today's fast-paced business environment, companies rely on data to make informed decisions and drive growth. However, with the sheer volume of data available, it can be challenging to know where to start and how to make the most of it.

This guide provides a comprehensive overview of data interpretation, covering everything from the basics of what it is to the benefits and best practices.

Data interpretation refers to the process of taking raw data and transforming it into useful information. This involves analyzing the data to identify patterns, trends, and relationships, and then presenting the results in a meaningful way. Data interpretation is an essential part of data analysis, and it is used in a wide range of fields, including business, marketing, healthcare, and many more.

Importance of Data Interpretation in Today's World

Data interpretation is critical to making informed decisions and driving growth in today's data-driven world. With the increasing availability of data, companies can now gain valuable insights into their operations, customer behavior, and market trends. Data interpretation allows businesses to make informed decisions, identify new opportunities, and improve overall efficiency.

There are three main types of data interpretation: quantitative, qualitative, and mixed methods.

Quantitative data interpretation refers to the process of analyzing numerical data. This type of data is often used to measure and quantify specific characteristics, such as sales figures, customer satisfaction ratings, and employee productivity.

Qualitative data interpretation refers to the process of analyzing non-numerical data, such as text, images, and audio. This data type is often used to gain a deeper understanding of customer attitudes and opinions and to identify patterns and trends.

Mixed methods data interpretation combines both quantitative and qualitative data to provide a more comprehensive understanding of a particular subject. This approach is particularly useful when analyzing data that has both numerical and non-numerical components, such as customer feedback data.

There are several data interpretation methods, including descriptive statistics, inferential statistics, and visualization techniques.

Descriptive statistics involve summarizing and presenting data in a way that makes it easy to understand. This can include calculating measures such as mean, median, mode, and standard deviation.

Inferential statistics involves making inferences and predictions about a population based on a sample of data. This type of data interpretation involves the use of statistical models and algorithms to identify patterns and relationships in the data.

Visualization techniques involve creating visual representations of data, such as graphs, charts, and maps. These techniques are particularly useful for communicating complex data in an easy-to-understand manner and identifying data patterns and trends.

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Data interpretation plays a crucial role in decision-making and helps organizations make informed choices. There are numerous benefits of data interpretation, including:

  • Improved decision-making: Data interpretation provides organizations with the information they need to make informed decisions. By analyzing data, organizations can identify trends, patterns, and relationships that they may not have been able to see otherwise.
  • Increased efficiency: By automating the data interpretation process, organizations can save time and improve their overall efficiency. With the right tools and methods, data interpretation can be completed quickly and accurately, providing organizations with the information they need to make decisions more efficiently.
  • Better collaboration: Data interpretation can help organizations work more effectively with others, such as stakeholders, partners, and clients. By providing a common understanding of the data and its implications, organizations can collaborate more effectively and make better decisions.
  • Increased accuracy: Data interpretation helps to ensure that data is accurate and consistent, reducing the risk of errors and miscommunication. By using data interpretation techniques, organizations can identify errors and inconsistencies in their data, making it possible to correct them and ensure the accuracy of their information.
  • Enhanced transparency: Data interpretation can also increase transparency, helping organizations demonstrate their commitment to ethical and responsible data management. By providing clear and concise information, organizations can build trust and credibility with their stakeholders.
  • Better resource allocation: Data interpretation can help organizations make better decisions about resource allocation. By analyzing data, organizations can identify areas where they are spending too much time or money and make adjustments to optimize their resources.
  • Improved planning and forecasting: Data interpretation can also help organizations plan for the future. By analyzing historical data, organizations can identify trends and patterns that inform their forecasting and planning efforts.

Data interpretation is a process that involves several steps, including:

  • Data collection: The first step in data interpretation is to collect data from various sources, such as surveys, databases, and websites. This data should be relevant to the issue or problem the organization is trying to solve.
  • Data preparation: Once data is collected, it needs to be prepared for analysis. This may involve cleaning the data to remove errors, missing values, or outliers. It may also include transforming the data into a more suitable format for analysis.
  • Data analysis: The next step is to analyze the data using various techniques, such as statistical analysis, visualization, and modeling. This analysis should be focused on uncovering trends, patterns, and relationships in the data.
  • Data interpretation: Once the data has been analyzed, it needs to be interpreted to determine what the results mean. This may involve identifying key insights, drawing conclusions, and making recommendations.
  • Data communication: The final step in the data interpretation process is to communicate the results and insights to others. This may involve creating visualizations, reports, or presentations to share the results with stakeholders.

Data interpretation can be applied in a variety of settings and industries. Here are a few examples of how data interpretation can be used:

  • Marketing: Marketers use data interpretation to analyze customer behavior, preferences, and trends to inform marketing strategies and campaigns.
  • Healthcare: Healthcare professionals use data interpretation to analyze patient data, including medical histories and test results, to diagnose and treat illnesses.
  • Financial Services: Financial services companies use data interpretation to analyze financial data, such as investment performance, to inform investment decisions and strategies.
  • Retail: Retail companies use data interpretation to analyze sales data, customer behavior, and market trends to inform merchandising and pricing strategies.
  • Manufacturing: Manufacturers use data interpretation to analyze production data, such as machine performance and inventory levels, to inform production and inventory management decisions.

These are just a few examples of how data interpretation can be applied in various settings. The possibilities are endless, and data interpretation can provide valuable insights in any industry where data is collected and analyzed.

Data interpretation is a crucial step in the data analysis process, and the right tools can make a significant difference in accuracy and efficiency. Here are a few tools that can help you with data interpretation:

  • Share parts of your spreadsheet, including sheets or even cell ranges, with different collaborators or stakeholders.
  • Review and approve edits by collaborators to their respective sheets before merging them back with your master spreadsheet.
  • Integrate popular tools and connect your tech stack to sync data from different sources, giving you a timely, holistic view of your data.
  • Google Sheets: Google Sheets is a free, web-based spreadsheet application that allows users to create, edit, and format spreadsheets. It provides a range of features for data interpretation, including functions, charts, and pivot tables.
  • Microsoft Excel: Microsoft Excel is a spreadsheet software widely used for data interpretation. It provides various functions and features to help you analyze and interpret data, including sorting, filtering, pivot tables, and charts.
  • Tableau: Tableau is a data visualization tool that helps you see and understand your data. It allows you to connect to various data sources and create interactive dashboards and visualizations to communicate insights.
  • Power BI: Power BI is a business analytics service that provides interactive visualizations and business intelligence capabilities with an easy interface for end users to create their own reports and dashboards.
  • R: R is a programming language and software environment for statistical computing and graphics. It is widely used by statisticians, data scientists, and researchers to analyze and interpret data.

Each of these tools has its strengths and weaknesses, and the right tool for you will depend on your specific needs and requirements. Consider the size and complexity of your data, the analysis methods you need to use, and the level of customization you require, before making a decision.

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Data interpretation can be a complex and challenging process, but there are several solutions that can help overcome some of the most common difficulties.

Data interpretation can often be biased based on the data sources and the people who interpret it. It is important to eliminate these biases to get a clear and accurate understanding of the data. This can be achieved by diversifying the data sources, involving multiple stakeholders in the data interpretation process, and regularly reviewing the data interpretation methodology.

Missing data can often result in inaccuracies in the data interpretation process. To overcome this challenge, data scientists can use imputation methods to fill in missing data or use statistical models that can account for missing data.

Data privacy is a crucial concern in today's data-driven world. To address this, organizations should ensure that their data interpretation processes align with data privacy regulations and that the data being analyzed is adequately secured.

Data interpretation is used in a variety of industries and for a range of purposes. Here are a few examples:

Sales trend analysis is a common use of data interpretation in the business world. This type of analysis involves looking at sales data over time to identify trends and patterns, which can then be used to make informed business decisions.

Customer segmentation is a data interpretation technique that categorizes customers into segments based on common characteristics. This can be used to create more targeted marketing campaigns and to improve customer engagement.

Predictive maintenance is a data interpretation technique that uses machine learning algorithms to predict when equipment is likely to fail. This can help organizations proactively address potential issues and reduce downtime.

Fraud detection is a use case for data interpretation involving data and machine learning algorithms to identify patterns and anomalies that may indicate fraudulent activity.

To ensure that data interpretation processes are as effective and accurate as possible, it is recommended to follow some best practices.

Data quality is critical to the accuracy of data interpretation. To maintain data quality, organizations should regularly review and validate their data, eliminate data biases, and address missing data.

Choosing the right data interpretation tools is crucial to the success of the data interpretation process. Organizations should consider factors such as cost, compatibility with existing tools and processes, and the complexity of the data to be analyzed when choosing the right data interpretation tool. Layer, an add-on that equips teams with the tools to increase efficiency and data quality in their processes on top of Google Sheets, is an excellent choice for organizations looking to optimize their data interpretation process.

Data interpretation results need to be communicated effectively to stakeholders in a way they can understand. This can be achieved by using visual aids such as charts and graphs and presenting the results clearly and concisely.

The world of data interpretation is constantly evolving, and organizations must stay up to date with the latest developments and best practices. Ongoing learning and development initiatives, such as attending workshops and conferences, can help organizations stay ahead of the curve.

Regardless of the data interpretation method used, following best practices can help ensure accurate and reliable results. These best practices include:

  • Validate data sources: It is essential to validate the data sources used to ensure they are accurate, up-to-date, and relevant. This helps to minimize the potential for errors in the data interpretation process.
  • Use appropriate statistical techniques: The choice of statistical methods used for data interpretation should be suitable for the type of data being analyzed. For example, regression analysis is often used for analyzing trends in large data sets, while chi-square tests are used for categorical data.
  • Graph and visualize data: Graphical representations of data can help to quickly identify patterns and trends. Visualization tools like histograms, scatter plots, and bar graphs can make the data more understandable and easier to interpret.
  • Document and explain results: Results from data interpretation should be documented and presented in a clear and concise manner. This includes providing context for the results and explaining how they were obtained.
  • Use a robust data interpretation tool: Data interpretation tools can help to automate the process and minimize the risk of errors. However, choosing a reliable, user-friendly tool that provides the features and functionalities needed to support the data interpretation process is vital.

Data interpretation is a crucial aspect of data analysis and enables organizations to turn large amounts of data into actionable insights. The guide covered the definition, importance, types, methods, benefits, process, analysis, tools, use cases, and best practices of data interpretation.

As technology continues to advance, the methods and tools used in data interpretation will also evolve. Predictive analytics and artificial intelligence will play an increasingly important role in data interpretation as organizations strive to automate and streamline their data analysis processes. In addition, big data and the Internet of Things (IoT) will lead to the generation of vast amounts of data that will need to be analyzed and interpreted effectively.

Data interpretation is a critical skill that enables organizations to make informed decisions based on data. It is essential that organizations invest in data interpretation and the development of their in-house data interpretation skills, whether through training programs or the use of specialized tools like Layer. By staying up-to-date with the latest trends and best practices in data interpretation, organizations can maximize the value of their data and drive growth and success.

Hady has a passion for tech, marketing, and spreadsheets. Besides his Computer Science degree, he has vast experience in developing, launching, and scaling content marketing processes at SaaS startups.

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Top 10 Data Analysis Templates with Samples and Examples

Top 10 Data Analysis Templates with Samples and Examples

Mohammed Sameer


If people could eat data instead of food, we could end world hunger with enough spare data left over to tackle 3 famines.

This startling but obvious statement underscores the abundance of data available to the human race today and the humungous rate at which it has grown in our digital age. Just as sustenance nourishes our bodies, data fuels our intellect, satiating the hunger for insights and understanding. 

Data is the foundation upon which the structure of information stands tall. Imagine gazing at a puzzle's scattered pieces – each is important, might be beautiful and vital, but the true picture emerges only when the pieces interlock. Similarly, data is the root of knowledge for today’s businesses. Our new Data Analysis Templates are the masterful hands that bring all that scattered knowledge and wisdom together.

These PPT Presentations emerge as essential companions in a landscape where accurate decision-making means the difference between thriving and surviving. Understanding data is pivotal in the symphony of business strategies, marketing endeavors, and research pursuits. 

The 100% customizable nature of the templates provides you with the desired flexibility to edit your presentations. The content-ready slides give you the much-needed structure.

Let’s explore!

Template 1: Data Analysis Process PPT Set

Use this PPT Set to help stakeholders understand difficulties that mar the data analysis process and gain valuable insights. Explore the crucial stages of data analysis, from establishing data requirements and efficient data collection to thorough data processing and cleaning. This PPT Design highlights the often underestimated yet pivotal phase of data cleaning. With this template, you'll understand how data lays the foundation for seamless analysis, leading to more accurate results and impactful communication. Download now!

Data Analysis Process PPT Set

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Template 2: Data Analysis Business Evaluation Process for Visualization and Presentation

This holistic PPT Bundle guides you through the complex stages of visualization and presentation while offering a profound understanding of each crucial phase. Use this presentation template to understand the essence of successful data analysis, as it breaks down the process into digestible segments. From the initial steps of business issue comprehension and data understanding to data preparation, exploratory analysis, monitoring, validation, and finally, captivating visualization and presentation – every facet is covered. This PPT Preset goes beyond mere process explanation, offering a robust framework for the holistic development of data conceptualization, collection, analysis, and cleaning procedures. Get it today!

Data Analysis Business Evaluation Process for Visualization and Presentation

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Template 3: Data Requirement Analysis PPT Bundle

Navigating challenges of problem-solving, prioritization, and data insight, this PPT Presentation presents a strategic roadmap that transforms raw information into actionable intelligence. It starts with a deep dive into the heart of your business challenges. Focusing on defining the core problems, this presentation template guides you through the process of setting priorities, ensuring every move is a step closer to your objectives. Data collection, a crucial cornerstone, is explained through insightful visual aids and organized segments. Witness the transformation of disparate data points into a coherent narrative, empowering you to decipher trends, anomalies, and opportunities.

This PPT Template equips you with the tools to not only gather data but also comprehend its implications, turning information into true knowledge. Navigating the challenges of data requirement analysis is no longer a daunting task. From security gaps that demand attention to complex data systems that require expertise, our template ensures you're prepared to overcome these hurdles with confidence. The high costs that often come with data analysis are confronted head-on, unraveling budget-friendly strategies that don't compromise on quality. Get this template today!

Data Requirement Analysis PPT Bundle

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Template 4: Big Data Analysis PPT Set

This comprehensive PPT Deck presents a pre-made Big Data Analysis funnel that guides you through the rather complex process of turning data into gold. Gain a competitive edge by understanding effective data analysis techniques of association rule learning, classification tree analysis, genetic algorithm, regression analysis, and sentiment analysis. It's more than a run-of-the-mill PPT Presentation; it's a transformative tool. Invest in a big data analysis PPT like resource that's not just about graphs and numbers; get it now. Download now!

Big Data Analysis PPT Set

Template 5: Data Management Analysis PPT Framework

For achieving business excellence, the quest for efficient and time-saving solutions is a universal endeavor. Recognizing your aspirations, we present the Data Management Analysis PowerPoint Presentation — an invaluable asset for seamless change management and effective data analysis. It incorporates PPT Slides designed to provide an effortless avenue for embracing change management and conducting incisive data analysis. It offers a cohesive platform for centralizing your objectives, ready to be shared with your team. The judicious use of text boxes empowers you to articulate your perspectives with precision on each pertinent subject. Download today!

Data Management Analysis PPT Framework

Template 6: Predictive Data Analysis PPT Layout

Get this PPT Preset to consolidate your stakeholder's grasp on predictive analytics, a discipline that uses statistical methodologies, cutting-edge machine learning algorithms, and a suite of tools to dissect historical data. This PPT Layout guides you through a well-structured journey, unfolding the essentials of predictive analytics, its foundational framework, and a suite of models that constitute its core. The significance of predictive analytics takes center stage, underscored by its multifaceted applications. Additionally, this resource has an Estimation Model PPT Slide, which explains the key tenets of diverse predictive analytics tools and their closely-knit workflows. The demarcation between the four pivotal categories of advanced analytics in this PPT deck receives careful attention. It sheds light on predictive analytics models – from classification to clustering models and beyond. Download now!

Predictive Data Analysis PPT Layout

Template 7: Dashboard For IT Operations Data Analysis

This PPT Template Dashboard is a dynamic representation of your operational landscape. This PPT Set helps track the total number of cases from inception to resolution. Visualize trends with a graph showcasing the weekly ebb and flow of opened and closed cases. Prioritize effectively, allocating resources where they matter most, as the presentation template depicts it across departments. Efficiency meets clarity as you explore the time distribution of tickets on a day-by-day basis. Gain a better understanding of workflow patterns and resource utilization. Analyze open case statuses, fostering an environment of proactive response and swift action. Download now!

Dashboard For IT Operations Data Analysis

Template 8: Quarterly Sales Data Analysis Report

Visualize your progress with ease using this PPT Template's intuitive presentation of monthly sales data. Get a clear view of team-wise statistics that showcase individual contributions, fostering a culture of recognition and growth. Uncover finer details through the nuanced comparison of total versus actual sales values, empowering you to identify trends and opportunities. Engage stakeholders in strategy evaluation as you assess team goals versus actual achievements. Pinpoint areas of excellence and those warranting attention, refining your approach. Download now!

Quarterly Sales Data Analysis Report

Template 9: Real-Time  Marketing Data Analysis

Here's a dynamic marketing analysis tool blending insights and aesthetics. It presents a pie chart comparing planned vs. actual budgets while diving deep into sections showcasing real-time marketing benefits: Elevated customer experiences, surging conversions, enhanced retention, and refined brand perception. Navigate budget allocation through intuitive bar graphs. Improve your strategy with data symphony, moving a step closer to success through informed choices. Download now!

Real-Time Marketing Data Analysis

Template 10: Data Analysis Process for Visualization and Presentation

Embark on a data-driven journey with this PPT Set. Learn the process of Data Analysis, Visualization, and Presentation to address complex business challenges. This PPT Design walks you through these stages, from issue identification and data preparation to exploratory analysis modeling. Witness raw data transform into insights through rigorous validation. Culminate in captivating visualizations and masterful presentations, setting new standards for impactful communication. Download now!

Data Analysis Process for Visualization and Presentation

Bridging Numbers and Narratives: Your Journey Through Data Analysis

In a world where data weaves the fabric of progress, our journey through this blog comes to an inspiring end. As you venture into data analysis armed with our templates, remember that each graph, each layout, and each piece of information is a brushstroke on the canvas of understanding. With every mouse click, you’re not just navigating slides; you're charting the course for informed decisions, breakthrough discoveries, and transformative strategies.

FAQs on Data Analysis

What is data analysis.

Data analysis involves inspecting, cleansing, transforming, and modeling data to derive meaningful insights, draw conclusions, and support decision-making. It encompasses various techniques, including statistical methods, machine learning, and visualization, to uncover patterns, trends, and relationships within datasets.

What are the four types of data analysis?

There are four main types of data analysis:

  • Descriptive Analysis: This type of analysis focuses on summarizing and describing the main features of a dataset. It involves statistical measures such as mean, median, mode, range, and standard deviation. Descriptive analysis aims to clearly understand the data's characteristics but doesn't involve drawing conclusions or making predictions.
  • Diagnostic Analysis: Diagnostic analysis involves digging deeper into data to understand why certain patterns or outcomes occurred. It aims to identify the root causes of specific events or trends. Techniques used in diagnostic analysis often include data visualization, exploratory data analysis, and statistical tests to uncover relationships and correlations.
  • Predictive Analysis: Predictive analysis involves using historical data to predict future events or outcomes. This type of analysis uses statistical models, machine learning algorithms, and data mining techniques to identify patterns and trends that can be used to forecast future trends. It's widely used in finance, marketing, and healthcare for making informed decisions.
  • Prescriptive Analysis: Prescriptive analysis goes beyond predicting future outcomes. It provides recommendations or solutions for specific situations based on historical and current data analysis. This type of analysis considers different possible actions and their potential outcomes to guide decision-making. Prescriptive analysis is often used in complex scenarios involving multiple variables and options.

Where is data analysis used?

Data analysis is used in a wide range of fields and industries, including but not limited to:

  • Business: Analyzing customer behavior, market trends, and financial performance.
  • Healthcare: Analyzing patient records, medical research data, and disease trends.
  • Science: Analyzing experimental results, simulations, and observations.
  • Finance: Analyzing investment trends, risk assessment, and portfolio management.
  • Marketing: Analyzing campaign effectiveness, consumer preferences, and market segmentation.
  • Social Sciences: Analyzing survey data, demographic trends, and human behavior.
  • Sports: Analyzing player performance, game statistics, and strategy optimization.

What is the main tool for data analysis?

There isn't a single "main" tool for data analysis, as the choice of tools depends on the specific tasks and the preferences of the analyst. However, some widely used tools for data analysis include:

  • Spreadsheet Software: Like Microsoft Excel or Google Sheets, used for basic data manipulation and visualization.
  • Statistical Software: Such as R and Python's libraries (e.g., pandas, numpy, scipy), used for in-depth statistical analysis and modeling.
  • Data Visualization Tools: Like Tableau, Power BI, or matplotlib/seaborn in Python, used to create visual representations of data.
  • Database Management Systems (DBMS): Such as SQL-based systems for querying and managing large datasets.
  • Machine Learning Libraries: Such as scikit-learn, TensorFlow, and PyTorch for building predictive models.

Why is data analysis important?

Data analysis is crucial for several reasons:

  • Informed Decision-Making: It provides insights that help individuals and organizations make informed decisions based on evidence rather than intuition.
  • Identifying Patterns and Trends: It helps to uncover hidden patterns, trends, and correlations in large datasets that might not be apparent on the surface.
  • Problem Solving: Data analysis aids in solving complex problems by providing a structured approach to understanding and addressing issues.
  • Improving Efficiency and Performance: It allows businesses to optimize processes, improve efficiency, and enhance performance based on data-driven insights.
  • Innovation and Research: Data analysis is essential in scientific research and innovation, helping to validate hypotheses and drive discoveries.
  • Competitive Advantage: Organizations that effectively use data analysis gain a competitive edge by better understanding their customers, markets, and internal operations.
  • Risk Management: Data analysis enables better risk assessment and management by identifying potential issues or anomalies early on.
  • Resource Allocation: It helps allocate resources effectively by understanding where investments are most likely to yield positive outcomes.

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Your Modern Business Guide To Data Analysis Methods And Techniques

Data analysis methods and techniques blog post by datapine

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

  • Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
  • Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
  • Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
  • Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

  • Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
  • Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
  • Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

Data analysis process graphic

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

  • Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
  • Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
  • Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
  • Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
  • Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

Top 17 data analysis methods

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

Cohort analysis chart example from google analytics

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

Example on how to use predictive analytics tool from datapine

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

Example on how to use intelligent alerts from datapine

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Example of multidimensional scaling analysis

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

17 top data analysis techniques by datapine

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

data connectors from datapine

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

Transportation costs logistics KPIs

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

An executive dashboard example showcasing high-level marketing KPIs such as cost per lead, MQL, SQL, and cost per customer.

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

  • Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
  • Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
  • Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

  • Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
  • Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
  • SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
  • Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

  • Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
  • External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
  • Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
  • Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

  • Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
  • Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
  • Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
  • Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
  • Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
  • Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
  • Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
  • Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

  • Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
  • Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
  • Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
  • SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
  • Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

  • By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
  • 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
  • Companies that exploit the full potential of their data can increase their operating margins by 60% .
  • We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

  • Cluster analysis
  • Cohort analysis
  • Regression analysis
  • Factor analysis
  • Neural Networks
  • Data Mining
  • Text analysis
  • Time series analysis
  • Decision trees
  • Conjoint analysis 
  • Correspondence Analysis
  • Multidimensional Scaling 
  • Content analysis 
  • Thematic analysis
  • Narrative analysis 
  • Grounded theory analysis
  • Discourse analysis 

Top 17 Data Analysis Techniques:

  • Collaborate your needs
  • Establish your questions
  • Data democratization
  • Think of data governance 
  • Clean your data
  • Set your KPIs
  • Omit useless data
  • Build a data management roadmap
  • Integrate technology
  • Answer your questions
  • Visualize your data
  • Interpretation of data
  • Consider autonomous technology
  • Build a narrative
  • Share the load
  • Data Analysis tools
  • Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .


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Best AI Presentation Software of 2024

presentation analysis tool

The advent of AI presentation makers has revolutionized the way presentations are created and delivered. These tools combine the creativity of humans with the power of artificial intelligence (AI) to produce visually attractive and highly engaging presentations. With AI presentation makers, users can easily create professional presentations that capture and retain the audience's attention. From selecting and customizing templates to adding graphics and multimedia elements, these tools offer a wide range of features that make the presentation creation process both easy and fun.

AI presentation makers leverage machine learning algorithms, neural networks, and other advanced methods to analyze data and generate content that reflects human creativity. The software offers a range of features that can help users create visually appealing and interactive presentations.

AI presentation tools use sophisticated algorithms to analyze the content of the presentation and identify patterns and trends. They can make suggestions for improving the visual design and layout of the slides. These tools give users access to data and content, allowing them to generate slides with images, charts, graphs, and other elements tailored to their specific needs. Additionally, users can add AI voices to their presentations, giving them an added layer of customization and interactivity.

Whether you are a professional looking to develop effective ad campaigns or a social media influencer looking to engage your audience with visually appealing presentations and AI voices, AI-powered presentation tools are your answer. In this blog post, we will discuss the advantages of using AI presentation makers and see some of the leading tools in the market that give you an edge.

Table of Contents

Automatic content creation , consistency , time-saving , customization , interactive, , , conclusion , advantages of ai presentation tools .

AI presentation tools offer numerous advantages over traditional presentation methods, including faster and more efficient content creation, increased audience engagement, and improved accuracy and consistency. In this section, we will explore the benefits of AI presentation tools in more detail, highlighting how they can help users quickly create visually stunning and highly engaging presentations.

One of the most significant advantages of AI presentation makers is their ability to automate the content creation process. This means that users can generate content and suggested images for their presentations simply by inputting a topic. The AI algorithms within the presentation maker analyze the topic and use natural language processing and machine learning techniques to generate relevant content and suggest appropriate images.

For example, imagine that a user wants to create a presentation on "Renewable Energy." By inputting this topic into an AI presentation maker, the user could automatically generate slides with content discussing various forms of renewable energy, such as solar, wind, and hydropower. The AI algorithms would also suggest relevant images to accompany the text, such as pictures of solar panels or wind turbines. This automation saves the user time and effort in researching and creating content for their presentation.

A study by Skyward found that content with relevant images gets 94% more views than content without relevant images, highlighting the importance of visuals in content creation. 

A second advantage of using AI for presentations is that they ensure consistency across multiple pieces of content, which helps the audience to follow and understand content more easily. A presentation can appear united and professional by using consistent design elements such as fonts, colors, and layout. This consistency also helps to strengthen the brand message and enhance the credibility of a business. 

AI presentation tools can save time by automating repetitive tasks such as formatting, editing, and proofreading. They reduce the time it takes to create presentations and also develop speaker notes for the content. These tools assist businesses in producing high-quality content faster because users do not have to spend a lot of time researching and developing content. The tool does it all for them.

AI presentation makers allow users to add text, pictures, and music of their choice to create personalized and engaging presentations. This helps in creating slides that resonate with the target audience, ultimately improving engagement rates.

AI presentation makers can be used to create interactive presentations that engage the target audience in new and exciting ways. Users can add rhetorical, open-ended, or multiple-choice questions to encourage discussion or incorporate activities, such as group discussions, brainstorming sessions, or role-playing exercises to reinforce key concepts, encourage collaboration, and promote learning.

Top AI Presentation Tools in 2023

Choosing the right AI presentation tool can be challenging, given there are so many options in the market. To help you make an informed decision, we have curated a list of some of the best AI presentation tools, with detailed information on their features, benefits, and pricing.

Murf can help you create voiceovers for your presentations in a fraction of the time it takes to do so manually. Users can upload a pre-recorded audio file of their entire presentation and convert it into a professional-sounding voiceover using Murf's AI voices. Murf offers an extensive range of 130+ AI voices across different languages, accents, and tonalities for you to choose from to create AI-generated audio for your videos and presentations.

Furthermore, you can also add voiceover to Google Slides presentations using Murf's add-on for Google Slides. Murf provides the ability to make changes to your script, resulting in little to no re-work since all you have to do is edit the text and re-render the voiceover. Another standout feature of Murf is users can remove accidentally picked-up background noise from their recording and customize the voiceover to suit their requirements.

Pricing Plans

Basic: $19/month, billed yearly*

Pro: $26/month, billed yearly*

Enterprise: $75/month, billed yearly*

*Check pricing page for the updated pricing information.

Try Murf for free software offers a wide range of customizable templates and design elements that users can adapt to match their brand's identity while creating presentations. With its design capabilities, can automatically arrange and format content to ensure the presentation looks professional. The software also has a user-friendly and intuitive interface that allows adding and arranging text, pictures, and graphics. It is a collaborative AI technology that allows team members to work together on a single presentation, improving efficiency and productivity.

Pro: $12 /month billed annually

Team: $40 /user/month billed annually

Enterprise: Customized is a user-friendly and free online presentation maker that leverages AI technology to help users design stunning presentations with ease. It offers an endless supply of presentation templates, making it easy for users to select a template that suits their needs. The platform provides users with never-ending graphic elements that can be easily added to presentations. It's a versatile platform suitable for various purposes, including group assignments, sales pitches, or business projects. The intuitive user interface makes it easy for users to create a presentation in no time, even without design skills.

Basic: $29/month

Pro: $69/month

Pitch is a powerful presentation software designed for modern teams that offers a range of features to create engaging and collaborative presentations. With the ability to host live meetings and edit together in real-time, team members can work together seamlessly to create stunning presentations. Pitch allows users to manage presentations like projects by assigning slides and setting status updates, making tracking progress easier. The software also provides version history management, allowing users to go back in time to see progress or recover previous versions. The software also offers team collaboration capabilities, allowing multiple people to work within the same design and access Pitch’s icon library, to add beautifully designed icons to presentations.

Starter: Free 

Pro: $8/member/month

Slidebean is another tool that is perfect for those who want to create visually appealing pitch decks and business presentations. With Slidebean, users have the option to build their own presentations or take advantage of the company's presentation design services to create professional quality presentations quickly. One of the standout features of Slidebean is the "Arrange with AI" option, which provides users with multiple design alternatives to choose from. 

Basic: $0/year 

All Access: $228/year

Venngage is an online presentation maker that provides users with a versatile and user-friendly platform to create professional and engaging presentations. One of its notable features is its wide range of fully customizable pre-made templates and layouts created by expert in-house designers. Additionally, the platform allows exporting presentations to PowerPoint or PDF or presenting straight from Venngage's presentation software. For business users, Venngage also allows for brand personalization, including the ability to upload company logos, colors, and fonts. Venngage also provides access to its icon library to add beautifully designed icons to presentations and team collaboration capabilities that allow multiple people to work within the same design.

Premium: $10/month/user billed yearly

Business: $24/month/user billed yearly

Enterprise: $499/month for 10+ seats billed yearly

AI presentation tools have revolutionized the way we create and deliver presentations. With their advanced AI algorithms, machine learning capabilities, and deep learning technology, these tools have made creating visually appealing and interactive presentations easier than ever.

As technology evolves and new AI presentation tools emerge, individuals can expect to see even more advanced features and capabilities in the years ahead. AI presentation tools offer an array of benefits that can help various professionals achieve their goals and captivate their audiences. With AI technology, individuals can easily create stunning visuals, add personalized voiceovers, and receive real-time feedback to improve their presentations.

What are the benefits of using AI presentation tools?

AI presentation makers help users save time by automating the content creation and design process. They also provide customization enabling users to create interactive presentations that engage the viewers.

How do you use AI in a presentation?

AI can be used in a presentation in several ways. One way is to use AI-powered presentation software that can automate presentation creation and design, create outlines, get customizable themes, suggest pictures and layouts, get an endless supply of templates, and even help with speech writing and save so much time. Another idea is to use AI-powered virtual assistants or chatbots to provide interactive elements to the presentation, such as answering questions or providing interesting information. 

Is there AI for PowerPoint?

Yes, there are AI-powered tools available for PowerPoint that can help you automate content creation and design. These tools can analyze data and provide insights that can be used to provide suggestions and create more effective presentations. AI-powered PowerPoint examples include, Slidebean, and Murf AI. 

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It’s time to bid goodbye to tedious PowerPoints by leveraging the power of artificial intelligence in your business presentation-making and communication processes.

Instead of wasting hours creating presentations, allow the best AI presentation software to do the heavy lifting. You can then dedicate your precious time to delivering your message more effectively.

The algorithms used in these AI marketing tools simplify the entire process, enabling your business to generate high-quality marketing presentations in minutes. AI presentation tools not only improve the quality of presentations but also ensure that they are visually pleasing and engaging.

Combining these AI-powered presentation tools with the right content marketing tools can enable your company to communicate its ideas more effectively, capturing audience attention and improving the information retention rate.

Below, I dive deep into the best AI presentation software to help you find the right tool for your business communication and marketing.

What is the Best AI Presentation Software?

Here is an in-depth analysis of the best AI presentation tools based on main capabilities and features.

1. Simplified (Best Free AI Presentation Maker)

Best free AI presentation software.

Simplified free AI presentation maker

With Simplified AI presentation maker , you can say goodbye to the tedious task of crafting captivating presentations. This innovative technology combines artificial intelligence with an intuitive design that makes creating strikingly beautiful and impactful presentations a breeze.

The versatile tool caters to professionals, students, and anyone aiming for well-designed slides that speak volumes.

With over 1 million users, Simplified is one of the best AI content generators that can help you generate text, video, animations, social media posts, and of course, presentations.

With just a simple topic input, the AI will whip up a unique presentation tailored just for you. By adjusting it to suit your preferences and enabling collaboration between you and your team, you can fine-tune the presentation until it reaches perfection. From fonts and text to colors, images, and even sounds—you’ve got the creative reins to craft incredible designs.

Simplified Key Features as the best Free AI Presentation Maker

  • AI-Driven Design – The AI presentation maker utilizes advanced algorithms to analyze your content and automatically generate visually appealing designs, layouts, and formatting.
  • Intuitive Interface – With its easy-to-use interface, you can effortlessly create stunning presentations without any prior design experience. The platform offers 100k+ pre-built design templates, themes, and customizable elements that cater to your specific needs.
  • Time-Saving Solution – The AI presentation maker significantly reduces the time you need to create a presentation. No more endless hours spent on designing and formatting.
  • Collaboration Made Easy – Simplified AI presentation maker supports real-time collaboration, allowing you to work with your team members seamlessly. Share your presentation, receive feedback, and make edits simultaneously, ensuring your final product is polished and ready to impress.
  • Accessible Anywhere, Anytime – The AI presentation maker operates on a cloud-based system, allowing you to access your presentations from any device with internet connectivity. There’s no need to stress over lost work or transferring files anymore—everything is securely stored and readily available whenever you need it.

Simplified Pricing

Simplified offers a free-forever plan to all its users with thousands of templates and 1GB of storage.

  • Free-Forever – Drag-and-drop design editor, 100k+ templates, 10+ AI tools for photo editing, and more.
  • Small Team ($20/mo) – Access premium images and other assets, upload your custom fonts and brand kit, create presentations in bulk, and get 100GB of storage.
  • Business ($33/mo) – Perfect for managing multiple brands while assigning roles to team members. It offers 200GB of storage.
  • Growth ($81/mo) – Great for managing multiple clients, projects, and departments, offering 500GB of storage.


Best AI presentation tool for creating banners, logos, videos, and mockups.

Designs AI combines machine learning and artificial intelligence technologies to empower its users to generate, edit, and scale content. It is an integrated Agency-as-a-service platform with all the AI presentation tools you need to simplify workflows, cut costs, and save time. 

One of this software’s great features is its AI video editing and production tool , which converts text scripts, posts, and articles into mind-blowing, fully-edited videos in over 20 languages. You can auto-adjust and optimize your videos for Instagram, Facebook, TikTok, and YouTube. 

Another great feature of is the AI logo generator . This tool analyzes your brand information to create thousands of stunning logos in seconds. It also allows you to select from more than 10,000 icons as well as export the logos in various formats, such as PDF, JPEG, SVG, and PNG.

What I like the most is that the AI-powered tool allows you to instantly access a full brand identity kit for creating brand collaterals . The kit includes branded merchandise mockups,  style guidelines, brand narratives, social media logos, and more. 

Furthermore, helps you generate thousands of amazing design variations to impress clients, increase conversions, and win projects.  With that comes over 20,000 dynamic design templates to choose from, enabling you to create your marketing collateral in seconds. Key Features 

  • 50+ Natural-sounding Voiceovers – uses text-to-speech technology to transform text scripts into realistic voiceovers in more than 15 global languages.  You can use the voiceovers for tutorials, sales videos, explainers, and more.
  • Vast Content Library – The library comprises thousands of templates, elements, and graphics and millions of images and clips. 
  • Easy Project Collaboration – This software streamlines project communication and allows sharing your content designs with teammates.  
  • Brand Kit Integration – You can use your brand style on your brochures, ads, posters, and flyers.
  • Cloud Hosting and Storage – organizes and saves all brand collateral on online storage.
  • Intuitive User Experience – The platform offers step-by-step creation for all its tools. It also has a simple editor for easy customization of the initial drafts. 
  • Online-based Software – You can use any browser or device to access the software without the need for installation. Pricing starts at $19/mo with a yearly billing cycle. However, you can start using the software with its free trial.

3. Decktopus  

Best AI presentation maker for generating slide notes.

Decktopus AI powered presentation generator

Decktopus is a game-changer for businesses seeking to create presentations that are not only visually stunning but also incredibly professional. The software leverages an AI deck builder that helps you generate a wide array of content, including presentations, stylish portfolios, sales funnels, mail signature links, proposals, and more.

The best thing is that you don’t have to start from scratch when preparing your presentations. This platform provides thousands of presentation and slide template options to choose from and edit to your liking. Besides, you can customize your presentations with its extensive range of colors, themes, and fonts.  

And as a source of inspiration, Decktopus features AI-powered deck samples , giving you a glimpse into how easy it is to generate professionally-looking presentations.

Once your presentations are ready, you can generate a link and share the content with the targeted audience using your custom domain or sharing through PPT and PDF format.

What’s more, Decktopus provides embedded forms to help you capture data within presentations, making them interactive. This makes it possible for your audience to get in touch with you, provide feedback, as well as rate your content.

Decktopus has AI tools for presenters, sales professionals, account executives, agencies, and marketing managers.

Decktopus Key Features 

  • Seamless Collaboration and Sharing – The software runs on the cloud providing easy online link sharing. You can easily collaborate with teams and export your presentation in PPT and PDF formats.
  • Analytics – The feature gives you insights into the number of times your documents are viewed and the slides with the highest bounce rate.
  • Presentation Tips – These resources help you learn more about creating and delivering presentations. 
  • Royalty-free Visuals – You can instantly access millions of stunning icons, images, quotes, and GIFs.
  • Visually Appealing Templates – They make it quick to create your presentations as you don’t have to start from scratch. 
  • Voice Recorder – You can hold a voice narration on pages and make it appear like you were there. 
  • Webhook Integration – The integration helps streamline workflows and automate actions. 

Decktopus Pricing

You can start using Decktopus with its free-forever plan . Pricing starts as low as $4.99/mo.

4. SlidesAI

Best for integrations.


SlidesAI is an innovative AI-powered tool designed to streamline the process of creating presentation slides. By leveraging advanced algorithms, SlidesAI can transform any text into a visually appealing and professional presentation in a matter of seconds.

The platform offers a user-friendly interface where users can input their desired text, either by writing it directly or pasting it from any source. Once the text is entered, users can customize the appearance of their presentation by selecting from pre-designed color schemes and font styles or creating their own unique look.

As the AI processes the input, it generates an outline and presentation content based on the provided text. Users can then preview their presentation and make any necessary adjustments before finalizing the project. With SlidesAI, users can save hours of manual work and create engaging presentations with ease.

SlidesAI is currently compatible with Google Slides, and an integration with Microsoft PowerPoint is in development. Users with a regular Gmail account can access SlidesAI without needing a Google Workspace account. The platform also offers a refund policy and responsive customer support via a chat widget for any inquiries or assistance.

SlidesAI Key Features

  • AI-Powered Text to Presentation – SlidesAI uses advanced algorithms to transform any text into a visually appealing presentation in seconds.
  • Customizable Design – You can choose from pre-designed color and font presets or create your own custom look for your presentations.
  • Time-saving – SlidesAI significantly reduces the time and effort required to create professional, engaging slides.
  • Easy to Use – Simply enter the desired text and let the AI generate an outline and presentation content based on the input.
  • Google Slides Integration – SlidesAI is currently compatible with Google Slides, with Microsoft PowerPoint integration in development.
  • No Google Workspace Account Required – Users with a regular Gmail account can access SlidesAI without needing a Google Workspace account.
  • Responsive Customer Support – The platform offers a chat widget for quick assistance and inquiries, ensuring a seamless user experience.

SlidesAI Pricing

  • Free-forever – Create 3 presentations every month for free.
  • Pro (€11.54/mo) – Generate 10 presentations per month.
  • Premium (€23.07/mo) – Generate unlimited presentations.

5. Presentory by Wondershare

Wondershare Presentory

Wondershare’s Presentory is an AI-powered presentation maker designed to facilitate impactful digital communication. Geared towards individuals looking to enhance their presentations – be it educators, business professionals, or content creators – it presents a unique combination of ease and depth.

What makes Presentory unique is its AI capabilities, which not only simplify the creation of dynamic presentations but also ensure they’re engaging. Be it the creation of a sales pitch or an educational lesson, the AI tools proposals corresponding to your needs, saving you valuable time and effort.

With Presentory, you can create visually appealing slides featuring dynamic transitions and well-arranged charts, not in hours but in minutes. A feature that sets Presentory apart is the one-click recording and live streaming capability. Combined with a built-in teleprompter, this feature can be a game-changer for those struggling with public speaking or for virtual conferences.

Key to Presentory’s appeal is its ability to adapt to myriad users’ needs, from social media influencers sharing knowledge to teachers in a virtual classroom. Its prolific customization capabilities also mean you can infuse individuality and a personal touch into your communication.

Presentory Key Features

  • Fast AI Presentation Maker – Presentory uses AI technology to streamline the creation of dynamic, immersive presentations.
  • Canvas Editing and Scene Arranging – Customize your content and layout with intuitive features to make your presentation impactful.
  • Recording and Streaming in One Click – Record and live-stream your presentations with ease using a handy built-in teleprompter.
  • Creating and Sharing Online – Presentory allows you to create, access, and share presentations from anywhere, at any time.

Presentory Pricing

You can try Presentory for free with up to 1,500 AI credits. Its Premium license is $2/mo, but you can also purchase a lifetime plan for $34.


The number #1 AI presentation software for creating modern decks.

Presentations AI

With , blank slides and unresponsive templates have become a thing of the past. The software boasts an AI-native technology that enables you to transform your ideas into decks instantly. 

All you need to do is feed in a prompt or select a template, respond to a few questions, and voila! You can also opt to paste content from a webpage or a doc and turn the draft into a deck in minutes. 

The good thing is that you can deliver your decks anywhere, whether through Zoom or in person, while tracking their effectiveness and controlling access. gives you access to hundreds of pre-built templates and slides tailored to showcase your ideas automatically. Its intuitive AI-powered controls and diverse theme palette enables you to customize your story to fit your unique brand.

In addition, this platform has built-in layout and style variations that allow you to toggle them to visualize content in multiple ways and switch back when need be. 

To add to it, provides support for live collaboration with remote teams . You get a single hub where you can assign tasks, track presentations, and request feedback seamlessly in real time. Besides, you can control your team’s access to information as well as invite guests to work collaboratively on specific documents. 

On the other hand, your remote teams can discuss ideas, host meetings, make decisions, and do editing together in real time. They can also add comments, reactions, and other input to your assigned tasks and slides. Key Features 

  • Slide Analytics – Use the data and insights to track the impact of your content and engagement.
  • Text Editor – You can paste notes, chats, and emails into the software’s text editor, allowing it to generate a presentation for you. 
  • Shareable Links – The feature enables you to manage feedback by sharing links rather than heavy file attachments.
  • Multi-device Compatibility – You can use any device to access
  • Live Collaboration – Your company can collaborate effectively with remote teams by obtaining feedback, assigning tasks, and tracking presentations. 
  • Anti-fragile Templates – The platform allows you to customize the templates automatically to showcase your unique ideas.
  • Follow-up Action Plan – gives you an outline of the steps to follow after the presentation. It also includes clear instructions for accessing the platform. Pricing is still in beta, so its prices may fluctuate in the near future. Currently, you can save 100% for a limited time and create unlimited decks with unlimited AI credits.


Best software with unique AI tools for presentations.

Beautiful AI is your secret weapon for presenting your ideas meaningfully, smartly, and easily to capture attention and increase productivity. This cloud-based online presentation tool puts into practice the rules of great design in real-time to help you organize your thoughts and bring your brand story to life. 

You can quickly get a headstart on training decks, pitches, and reports with’s smart presentation and slide templates , saving time. 

The smart templates come with built-in layout variations, enabling you to toggle and visualize content in multiple ways and generate new layouts in seconds. Besides, you can stay on top of your brand consistency with the platform’s customizable themes, fonts, and colors. 

On the other hand, the software’s simple, intuitive slide controls make it possible to do edits, including defining the position, size, and value of your content in a snap.

And when you need some inspiration to get your creative juices flowing, provides a slide library where you can search existing content and slide layout templates. 

This platform also features robust data visualization tools , including dozens of uniquely designed diagrams, charts, and graphs to keep you in control of every detail. Key Features 

  • 60+ Customizable Smart Slide Templates – The feature saves you time by eliminating the need to create presentations from scratch. This makes it quick for beginners to get started.
  • Advanced Sharing Options – You can share a link or invite teammates through email to check your presentations. Another option is to embed the presentation on a blog or website. 
  • Easy Exports to PowerPoint – allows you to design your presentations within the platform and export them to PowerPoint to streamline workflows.
  • Viewer Analytics – You can use the data to measure presentation metrics. 
  • Easy Slide Updates – empowers you to update old reports with a single click, saving your team hours.
  • Multi-device Support – Everything you create with this software is stored on the cloud, enabling you to access and share presentations on any device and from anywhere. 
  • Automatic Animations – Just provide the text of what you want and let bring your ideas to life. Pricing

Beautiful AI starts at $12/mo with unlimited slides for individuals.


One-of-a-kind AI presentation software for creating more persuasive pitches.

Kroma AI features tons of AI-powered presentation tools to help you create content for nearly anything, from fundraising pitch decks to product development roadmaps and investor update templates. 

Its hundreds of hand-crafted presentation decks are flexible to use and come with the right balance of structure, enabling you to propose, pitch, and present your ideas confidently. 

This online presentation software further brings to the table thousands of data visualization slides, elements, infographics, and templates alongside millions of creative assets . This makes your presentation-making process as easy as 123. also helps you to organize data neatly into graphs and charts in minutes for more straightforward analysis and visualization. 

One feature that makes stand out even more is the Grab and Go content .  The feature provides users with ready-to-use content that requires very minimal changes. You only include branding, then download, and you are good to go.

Moreover, this AI-powered tool allows you to access a high-resolution library fully packed with icons, images, and videos. Key Features 

  • Data Visualization Tools – This includes graphics, charts, maps, and infographics and helps you create easily digestible and shareable visuals.
  • Unlimited Image, Template, and Video Downloads – You can generate and download as many images, videos, and templates as you want for creating professional presentations.
  • Expert Decks – provides hundreds of expert decks for various use cases, including pitching to investors, presenting data and ideas, sales pitches, customer profiles, and creating B2B proposals. 
  • 100,000 Presentation Tools – You can use the tools to create presentation slides in minutes.
  • Email Support – This software offers excellent email support, enabling you to share links to your content via email. 
  • Millions of Creative Assets – gives you access to over a million creative assets with content rights for crafting engaging presentations.
  • Fully Packed Library – Sail through the library to get inspiration. Pricing

Kroma AI offers a free plan with all the presentation tools you need, although you can only download one high-quality template. Then, pricing starts at $39.99/mo, billed annually.

9. Sendsteps

Best AI interactive presentation tool for engaging with the audience.


As one of the best AI presentation makers, Sendsteps is a one-stop AI-powered platform to create interactive presentations with minimal effort. This software empowers you to hold live presentations and serves as a great interactive teaching tool and an online interactive meeting tool. It allows presenters to engage with their audience via numerous features, such as Q&A sessions, live polls, and interactive word clouds.

The platform features an AI presentation maker that generates an entire interactive presentation depending on the information provided or the subject matter. This enables you to create presentations 10x faster, saving time.

Besides, Sendsteps allows you to access a wide range of comprehensive question types that you can implement in your AI presentations, making them more engaging. 

Each question format is tailored toward a specific purpose, minimizing the chances of running short of interactive features and survey tools to attain your goals. You can also display your presentation results in multiple formats , including word clouds, bar charts, and pie charts.

This AI presentation maker leverages data management processes that rely on full transparency and industry-best standards to guarantee enhanced privacy and security . All the users’ data and information are secured with robust end-to-end encryption protocols and are only accessible to well-trained, authorized in-house employees.

Furthermore, Sendsteps provides seamless integrations with the tools you already use, such as PowerPoint, Google Slides, Web App, and Microsoft Teams App, enabling you to access all the interactive and engagement tools.   

Sendsteps Key Features 

  • Easy Moderation – Sendsteps gives you access to advanced filtering options, enabling you to eliminate inappropriate responses during your meetings automatically.
  • Unflinching Support –  The software has readily available customer support to offer technical assistance when facing difficulties. 
  • Insightful Analytics – Sendsteps gives you well-rounded feedback reports to help you collect insightful data from the audience. 
  • Comprehensive Question Format Choices – You get a selection of questions you can include in your presentations to increase engagement. 
  • Multiple Channels of Receiving Responses – You can obtain responses from your audience through a simple web portal or allow them to share their opinions or votes via SMS. 
  • AI Presentation Maker – You only need to enter a topic, choose your preferred language, and highlight your target audience, then leave the rest of the work to the AI presentation maker. 
  • Tons of Presentation Templates – You can opt to generate templates with AI or use the pre-made options to inspire your presentation.

Sendsteps Pricing

With Sendsteps’ free-forever plan, you can reach an audience of 10, including five quizzes per session. For unlimited features, pricing starts at $9.99/mo.

Uniquely powerful AI presentation tool for creating pitch decks.

Pitch presentation software

Whether you want to create professional-looking presentations, conference keynotes, sales decks, or project plans, Pitch has all the AI presentation tools you need to achieve precisely that.   This AI presentation software strikes a perfect balance between flexibility and design guardrails to generate stunning decks in no time.

Its  intelligent formatting  and  intuitive editing options  have the power to bring out the inner designer in you, enabling you to make edits faster and create visually appealing presentations.

To add life to your content, Pitch allows you to embed videos into your presentations. You can upload your own videos or get them from Vimeo, YouTube, and Loom. Additionally, this software integrates with Giphy, Unsplash, Brandfetch, and Icons8 , giving you access to millions of images to include in your slides. 

You can customize your presentation decks with the most up-to-date colors, fonts, and templates to stay on the approved brand style and enhance consistency. The best thing is that Pitch has a presentation gallery where you can browse to get inspired by well-curated presentations from teams, companies, and professionals across the globe. 

Its advanced collaboration tools give this software an edge over other AI presentation platforms.  One is the live video collaboration , whereby you can initiate a live video call and collaboratively edit a presentation with teammates. During the live session, the participants can hear and see each other as they edit a single presentation. 

On top of that, Pitch gives you offline access to your presentations . You can view and edit your content when you don’t have an active internet connection. All your presentations are stored in workspace folders where your team members can easily access, view, and edit.

Pitch Key Features 

  • Presentation Analytics – This feature gives you a glimpse into how the audience consumes your presentations using behavioral data.
  • Unlimited Presentations – The platform allows you to create unlimited presentations with the number of slides you want at no extra charge.  
  • Video and Image Library – Pitch lets you create and organize your images and video files in a library and access them anytime.
  • PDF and PowerPoint Exports – After creating your presentations, you can export them as PDFs and PPTX files.
  • Custom Links – Pitch allows you to generate individual links with or without a passcode to share with specific groups. 
  • Custom Templates and Fonts – Choose unlimited templates and fonts and upload them to your workspace. 

Pitch Pricing

You can create unlimited presentations with unlimited members with Pitch’s free-forever plan. For more advanced features, pricing starts at $8/mo per member.

11. DeckRobot 

An exceptional AI presentation tool for generating on-brand slide decks. 


DeckRobot is your go-to solution for reducing manual work when preparing your MS PowerPoint presentations. The software leverages AI technology to transform raw slides into enterprise-ready presentation slides. Using the DeckRobot plugin in your Microsoft PowerPoint can help you save more than 40% of your working time. 

This AI presentation tool is well-trained on corporate brand guidelines and AI algorithms, enabling you to generate eye-catching presentations that blend with your company brand book. Its project design preset feature contains corporate colors, fonts, icons, headers, and more, making it easy for users to choose a pre-setup of slide styling for their content. 

In addition, DeckRobot has an align objects feature , enabling you to maintain consistent presentations. This feature uses an AI algorithm that highlights a slide pattern, process, shape, and agenda and aligns items on the slide to match your specifications.

The software further provides you with AI tools to help you fix the visual elements of each slide of your presentation. This may include adding corporate stylistics to your slide color palettes, headers, and fonts based on the highlighted guidelines without tampering with the background.

Something unique about this software is that it doesn’t access your data . DeckRobot stores all the input slides and slide library on the client’s server for maximum security and privacy. 

DeckRobot Key Features

  • Millions of Slide Examples – You can draw ideas from those examples to inspire your subsequent professional and visually stunning slides.
  • Unlimited Number of Styles – They help you to format your slide decks to match your brand and enhance consistency. 
  • 100,000+ Firm Templates – These customizable templates serve as a base for building on your preferred slides.
  • Align Objects Feature – The feature helps you to create consistent brand presentations to tell your story better.
  • Alternative Design – DeckRobot’s AI identifies the items on your input slides and provides a design that blends well with the slide pattern. 
  • Anonymizing of Slides Content – The platform anonymizes your slide content for security purposes.
  • API Integration – This software allows you to choose and securely send anonymized slides to the EY server for further design options. 

DeckRobot Pricing

The company doesn’t disclose pricing, so you must schedule a demo to get more information.

Best AI Presentation Tools FAQs

What is ai presentation software.

AI presentation software refers to digital tools that rely on artificial intelligence technologies to help you create, design, and create engaging presentations. These software applications leverage machine learning algorithms to analyze user input, suggest relevant content, and offer visually appealing templates to simplify the presentation creation process.

How do AI presentation makers differ from traditional presentation tools?

While traditional presentation tools rely on users’ manual input and design choices, AI presentation tools streamline the process by providing intelligent suggestions, automated design elements, and content recommendations based on your preferences and context. This not only can save time but also improves the overall quality of your presentations, making it easier to create a professional-looking presentation in minutes.

What types of presentations can these AI-powered software create?

There are different types of presentations, including: – Business presentations. – Video presentations. – Tutorials. – Courses. – And more.

Who can benefit from using AI presentation software?

Professionals across various industries, including business, education, marketing, and more, can benefit from AI presentation tools. Whether you’re a seasoned presenter looking to enhance your slides or a beginner seeking assistance creating visually stunning presentations, AI-powered tools can help you achieve your goals more efficiently.

What are some key features of the best AI presentation software?

Some key features to consider when choosing AI presentation tools include: – User-friendly interface. – Customizable templates and design options. – Intelligent content recommendations. – Integration with popular data sources and file formats. – Real-time collaboration capabilities. – Accessibility and compatibility across devices. – Effective customer support.

Best AI Presentation Software: Conclusion 

The  best AI presentation software  tools are here to revolutionize how you conduct marketing and communication for your business. There’s no more need to rely on traditional presentation software for creating stunning presentations.

Whether you need to create professional presentations or social media graphics, these AI tools for presentations can help you create interactive and engaging content using machine learning algorithms and artificial intelligence to keep your audience hooked. 

No more wasting your precious time designing presentations. Critically examine the tools above and invest in an option that will help you meet your business presentation needs.  

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Vaslou is a passionate digital creator and blogger who loves to explore unique paths to generate online income. He’s also a musician, always looking for exciting paths to articulate his inventive spirit in the musical realm. When he’s not at work on his online endeavors, Vasco loves to delve into spiritual realms to become a better version of himself.

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AI presentation maker

When lack of inspiration or time constraints are something you’re worried about, it’s a good idea to seek help. Slidesgo comes to the rescue with its latest functionality—the AI presentation maker! With a few clicks, you’ll have wonderful slideshows that suit your own needs . And it’s totally free!

presentation analysis tool

Generate presentations in minutes

We humans make the world move, but we need to sleep, rest and so on. What if there were someone available 24/7 for you? It’s time to get out of your comfort zone and ask the AI presentation maker to give you a hand. The possibilities are endless : you choose the topic, the tone and the style, and the AI will do the rest. Now we’re talking!

Customize your AI-generated presentation online

Alright, your robotic pal has generated a presentation for you. But, for the time being, AIs can’t read minds, so it’s likely that you’ll want to modify the slides. Please do! We didn’t forget about those time constraints you’re facing, so thanks to the editing tools provided by one of our sister projects —shoutouts to Wepik — you can make changes on the fly without resorting to other programs or software. Add text, choose your own colors, rearrange elements, it’s up to you! Oh, and since we are a big family, you’ll be able to access many resources from big names, that is, Freepik and Flaticon . That means having a lot of images and icons at your disposal!

presentation analysis tool

How does it work?

Think of your topic.

First things first, you’ll be talking about something in particular, right? A business meeting, a new medical breakthrough, the weather, your favorite songs, a basketball game, a pink elephant you saw last Sunday—you name it. Just type it out and let the AI know what the topic is.

Choose your preferred style and tone

They say that variety is the spice of life. That’s why we let you choose between different design styles, including doodle, simple, abstract, geometric, and elegant . What about the tone? Several of them: fun, creative, casual, professional, and formal. Each one will give you something unique, so which way of impressing your audience will it be this time? Mix and match!

Make any desired changes

You’ve got freshly generated slides. Oh, you wish they were in a different color? That text box would look better if it were placed on the right side? Run the online editor and use the tools to have the slides exactly your way.

Download the final result for free

Yes, just as envisioned those slides deserve to be on your storage device at once! You can export the presentation in .pdf format and download it for free . Can’t wait to show it to your best friend because you think they will love it? Generate a shareable link!

What is an AI-generated presentation?

It’s exactly “what it says on the cover”. AIs, or artificial intelligences, are in constant evolution, and they are now able to generate presentations in a short time, based on inputs from the user. This technology allows you to get a satisfactory presentation much faster by doing a big chunk of the work.

Can I customize the presentation generated by the AI?

Of course! That’s the point! Slidesgo is all for customization since day one, so you’ll be able to make any changes to presentations generated by the AI. We humans are irreplaceable, after all! Thanks to the online editor, you can do whatever modifications you may need, without having to install any software. Colors, text, images, icons, placement, the final decision concerning all of the elements is up to you.

Can I add my own images?

Absolutely. That’s a basic function, and we made sure to have it available. Would it make sense to have a portfolio template generated by an AI without a single picture of your own work? In any case, we also offer the possibility of asking the AI to generate images for you via prompts. Additionally, you can also check out the integrated gallery of images from Freepik and use them. If making an impression is your goal, you’ll have an easy time!

Is this new functionality free? As in “free of charge”? Do you mean it?

Yes, it is, and we mean it. We even asked our buddies at Wepik, who are the ones hosting this AI presentation maker, and they told us “yup, it’s on the house”.

Are there more presentation designs available?

From time to time, we’ll be adding more designs. The cool thing is that you’ll have at your disposal a lot of content from Freepik and Flaticon when using the AI presentation maker. Oh, and just as a reminder, if you feel like you want to do things yourself and don’t want to rely on an AI, you’re on Slidesgo, the leading website when it comes to presentation templates. We have thousands of them, and counting!.

How can I download my presentation?

The easiest way is to click on “Download” to get your presentation in .pdf format. But there are other options! You can click on “Present” to enter the presenter view and start presenting right away! There’s also the “Share” option, which gives you a shareable link. This way, any friend, relative, colleague—anyone, really—will be able to access your presentation in a moment.

Discover more content

This is just the beginning! Slidesgo has thousands of customizable templates for Google Slides and PowerPoint. Our designers have created them with much care and love, and the variety of topics, themes and styles is, how to put it, immense! We also have a blog, in which we post articles for those who want to find inspiration or need to learn a bit more about Google Slides or PowerPoint. Do you have kids? We’ve got a section dedicated to printable coloring pages! Have a look around and make the most of our site!


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