data analytics problem solving

A woman with dreads works at a laptop using her data analyst skills.

What Are The Key Skills Every Data Analyst Needs?

In the job, a wide range of data analytics skills are required on a daily basis; everything from in-depth analyses to data visualisation and storytelling. One minute you’ll be composing an SQL query to explore a data set, the next you’ll stand in front of a board of directors outlining how the business needs to adapt according to your findings.

Let’s take look at the key skills associated with being a data analyst. You probably already possess some of the skills, since they cover a broad range of skillsets touching on communication, analytics, and problem solving.

Want to pick up some data analytics skills from scratch, for free? Try out CareerFoundry’s 5-day data short course to see if it’s for you!

Here are the key data analyst skills you need:

Excellent problem-solving skills
Solid numerical skills
Excel proficiency and knowledge of querying languages
Expertise in data visualization
Great communication skills
Key takeways

1. Excellent problem-solving skills

Problem solving is one of the most important data analyst skills you should possess. Around 90% of analytics is about critical thinking, and knowing the right questions to ask.

If the questions you ask are grounded in knowledge of the business, the product and the industry, you’ll get the answers you need. Data analysis is about being presented with a problem (i.e., “why aren’t we selling more red bikes?”), and carrying out the necessary investigative tasks to find the answer.

Data analytics is a lot about thinking logically through the problems you encounter. You’ll come to the right conclusions quicker if you’re familiar with the challenges and nuances of the data. If red bikes aren’t selling well, why could this be? Is it because other colors have larger ranges? Are red bikes typically priced higher than other bikes? Are red bikes only available in mountain bike form, therefore discouraging city dwellers to purchase them? Data analysts draw conclusions quicker by using their logic to understand the data.

2. Solid numerical skills

Many data analysts don’t come from the world of numbers—often, they come from a business or marketing background. It’s perfectly possible to grow your knowledge of this area as you go. While not necessarily a ‘skill’, an aptitude for numbers is certainly a good thing for any aspiring data analyst to have.

You’re going to need to bring a level of numerical expertise to the role, either from formal education or other experience. You can learn most of the numerical data analyst skills—such as regression analysis, which involves examining two or more variables and their relationships—without having to go back to school.

Having a thorough grounding in statistics is also beneficial—you can start by learning about descriptive and inferential statistics , and work up from there. You’re going to need an appreciation for queries, which are commands used by computers to perform tasks. In analytics, these commands are used to extract information from data sets. Brushing up on your knowledge of applied science and linear algebra is going to make things easier for you, although don’t be put off if this is all a mystery to you.

3. Excel proficiency and knowledge of querying languages

As we mentioned earlier, knowledge of Microsoft Excel is an essential data analyst skill for working effectively.

It’s a spreadsheet program used by millions of people around the world to store and share information, perform mathematical and statistical operations and create reports and visualizations that summarize important findings. For data analysts, it’s a powerful tool for quickly accessing, organizing, and manipulating data to derive and share insights.

Data analysts work with Excel every day, so you’re going to have to really know your VLOOKUP from your pivot tables . Want to find out where the red bikes sell the most? Curious as to whether the average price of red bikes is higher than blue bikes? Excel can help provide answers to these kinds of questions.

As well as Excel, analysts need to be familiar with at least one querying language. These languages are used to instruct computers to do specific tasks, including many related to the analysis of data. The most popular languages for data analysis are SQL and SAS. For a good introduction to SQL, try this cheatsheet . Programming languages such as Python and R also have a wide variety of powerful programs dedicated to analyzing data.

Many of the languages available perform different functions or are geared at one particular industry. SAS is primarily used in the medical industry, whereas SQL is often used for retrieving data from databases. If you have an idea of the industry you’d like to work in, it’s beneficial to do some research and find out what languages they use—tailoring your learning to the sector(s) you’re most interested in is a clever move.

4. Expertise in data visualization

It’s difficult to take a complicated topic and present findings in a simple way, but that’s precisely the job of the data analyst!

It’s all about turning your findings into easily digestible chunks of information. Telling a compelling story with your data is crucial, and so much of this involves the use of visual aids. Graphs and pie charts are a popular and extremely effective means of illustrating data findings.

Both Microsoft Excel and Tableau boast plenty of options for visualizing data, enabling you to present findings in an accurate way. This data analyst skill lies in knowing how best to present the data, so that your findings speak for themselves. There’s something of a tendency among tech professionals to speak in complex and esoteric terms, but to be a good data analyst is to communicate findings easily and effectively through simple visualizations.

5. Great communication skills

As well as being able to visualize your findings, accurately, data analysts must be able to communicate findings verbally. Data analysts work constantly with stakeholders, fellow colleagues and data suppliers, so good communication is an essential data analyst. How good are you at talking to people? Can you effectively break down technical information into simple words? This is a crucial skill that goes hand in hand with data visualization—it’s all in the delivery!

You’ll often need to present your findings in front of an audience, who might not be familiar with your analytical methods and processes. The job of the data analyst is to clearly translate their findings into non-technical terms. Your audience wants to hear your findings in ways which relate to their own roles. The bike designer is interested in hearing what designs of the red bike aren’t selling that well, and if customers are choosing not to buy a certain design in red.

The marketing manager wants to know if red bikes aren’t selling well in a certain country and whether sales have been affected by lack of marketing spend. The product manager wants to know if there is a general shift in popularity towards fixed gear bikes, and whether the drop in red bike sales is likely to last for a longer period of time. It’s crucial data analysts take their audience into consideration.

Key takeaways

Data analysts aren’t one trick ponies! They have a broad skillset incorporating a wide range of data analytics skills.
A head for math and statistics is core to the work of a data analyst.
As well as robust Excel knowledge, a good command of at least one programming language is required to carry out effective data analysis.
Having the ability to effectively ask “what does this mean?’” and “what impact could this have on something else?” is an essential part of analyzing data.
Similarly, possessing the ability to communicate your findings both visually and verbally is crucial to the role of the data analyst.
Data analytics is a hands-on field; get a taste of what it’s like in this free introductory short course .

So you’ve now learned about the main data analyst skills. If that’s made you curious to learn more, our data analytics blog contains more related articles about working in the field. And, if you’re keen to find out how to become a data analyst, check out this guide .

8 of 10 chapters available

Solve Any Data Analysis Problem you own this product $(document).ready(function() { $.ajax({ url: "/ajax/getWishListDetails" }).done(function (data) { if (!jQuery.isEmptyObject(data) && data['wishlistProductIds']) { $(".wishlist-container").each(function() { if (data.wishlistProductIds.indexOf($(this).find('.wishlist-toggle').data('product-id')) > -1) { $(this).addClass("on-wishlist"); } }); } }); $.ajax({ url: "/ajax/getProductOwnershipDetails?productId=3091" }).done(function (data) { if (!jQuery.isEmptyObject(data)) { if (data['ownership']) { $(".wishlist-container").hide(); $(".ownership-indicator").addClass('owned'); $(document.body).addClass("user-owns-product"); } } }); }); document.addEventListener("subscription-status-loaded", function(e){ var status = e && e.detail && e.detail['status']; if(status != "ACTIVE" && status != "PAUSED"){ return; } if(window.readingListsServerVars != null){ $(document).ready(function() { var $readingListToggle = $(".reading-list-toggle"); $(document.body).append(' '); $(document.body).append(' loading reading lists ... '); function adjustReadingListIcon(isInReadingList){ $readingListToggle.toggleClass("fa-plus", !isInReadingList); $readingListToggle.toggleClass("fa-check", isInReadingList); var tooltipMessage = isInReadingList ? "edit in reading lists" : "add to reading list"; $readingListToggle.attr("title", tooltipMessage); $readingListToggle.attr("data-original-title", tooltipMessage); } $.ajax({ url: "/readingList/isInReadingList", data: { productId: 3091 } }).done(function (data) { adjustReadingListIcon(data && data.hasProductInReadingList); }).catch(function(e){ console.log(e); adjustReadingListIcon(false); }); $readingListToggle.on("click", function(){ if(codePromise == null){ showToast() } loadCode().then(function(store){ store.requestReadingListSpecificationForProduct({ id: window.readingListsServerVars.externalId, manningId: window.readingListsServerVars.productId, title: window.readingListsServerVars.title }); ReadingLists.ReactDOM.render( ReadingLists.React.createElement(ReadingLists.ManningOnlineReadingListModal, { store: store, }), document.getElementById("reading-lists-modal") ); }).catch(function(e){ console.log("Error loading code reading list code"); }); }); var codePromise var readingListStore function loadCode(){ if(codePromise) { return codePromise } return codePromise = new Promise(function (resolve, reject){ $.getScript(window.readingListsServerVars.libraryLocation).done(function(){ hideToast() readingListStore = new ReadingLists.ReadingListStore( new ReadingLists.ReadingListProvider( new ReadingLists.ReadingListWebProvider( ReadingLists.SourceApp.marketplace, getDeploymentType() ) ) ); readingListStore.onReadingListChange(handleChange); readingListStore.onReadingListModalChange(handleChange); resolve(readingListStore); }).catch(function(){ hideToast(); console.log("Error downloading reading lists source"); $readingListToggle.css("display", "none"); reject(); }); }); } function handleChange(){ if(readingListStore != null) { adjustReadingListIcon(readingListStore.isInAtLeastOneReadingList({ id: window.readingListsServerVars.externalId, manningId: window.readingListsServerVars.productId })); } } var $readingListToast = $("#reading-list-toast"); function showToast(){ $readingListToast.css("display", "flex"); setTimeout(function(){ $readingListToast.addClass("shown"); }, 16); } function hideToast(){ $readingListToast.removeClass("shown"); setTimeout(function(){ $readingListToast.css("display", "none"); }, 150); } function getDeploymentType(){ switch(window.readingListsServerVars.deploymentType){ case "development": case "test": return ReadingLists.DeploymentType.dev; case "qa": return ReadingLists.DeploymentType.qa; case "production": return ReadingLists.DeploymentType.prod; case "docker": return ReadingLists.DeploymentType.docker; default: console.error("Unknown deployment environment, defaulting to production"); return ReadingLists.DeploymentType.prod; } } }); } });

MEAP began November 2023
Publication in Fall 2024 ( estimated )
ISBN 9781633437531
325 pages (estimated)
printed in black & white
eBook pdf, ePub, online
print includes eBook
subscription from $19.99 includes this product

pro $24.99 per month

access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
choose one free eBook per month to keep
exclusive 50% discount on all purchases

lite $19.99 per month

access to all Manning books, including MEAPs!

5, 10 or 20 seats+ for your team - learn more

High-value skills to tackle specific analytical problems
Deconstructing problems for faster, practical solutions
Data modeling, PDF data extraction, and categorical data manipulation
Handling vague metrics, deciphering inherited projects, and defining customer records

about the book

About the reader, about the author, choose your plan.

choose another free product every time you renew
choose twelve free products per year

five seats for your team
Subscribe to our Newsletter
Manning on LinkedIn
Manning on Instagram
Manning on Facebook
Manning on Twitter
Manning on YouTube
Manning on Twitch
Manning on Mastodon

how to play

guess the geekle in 5-, 6-, 7- tries.
each guess must be a valid 4-6 letter tech word. hit enter to submit.
after each guess, the color of the tiles will change to show how close your guess was to the word.

geekle is based on a wordle clone .

How to analyze a problem

May 7, 2023 Companies that harness the power of data have the upper hand when it comes to problem solving. Rather than defaulting to solving problems by developing lengthy—sometimes multiyear—road maps, they’re empowered to ask how innovative data techniques could resolve challenges in hours, days or weeks, write senior partner Kayvaun Rowshankish and coauthors. But when organizations have more data than ever at their disposal, which data should they leverage to analyze a problem? Before jumping in, it’s crucial to plan the analysis, decide which analytical tools to use, and ensure rigor. Check out these insights to uncover ways data can take your problem-solving techniques to the next level, and stay tuned for an upcoming post on the potential power of generative AI in problem-solving.

The data-driven enterprise of 2025

How data can help tech companies thrive amid economic uncertainty

How to unlock the full value of data? Manage it like a product

Data ethics: What it means and what it takes

Author Talks: Think digital

Five insights about harnessing data and AI from leaders at the frontier

Real-world data quality: What are the opportunities and challenges?

How a tech company went from moving data to using data: An interview with Ericsson’s Sonia Boije

Harnessing the power of external data

5 Reasons Why Data Analytics is Important in Problem Solving

Data analytics is important in problem solving and it is a key sub-branch of data science. Even though there are endless data analytics applications in a business, one of the most crucial roles it plays is problem-solving.

Using data analytics not only boosts your problem-solving skills, but it also makes them a whole lot faster and efficient, automating a majority of the long and repetitive processes.

Whether you’re fresh out of university graduate or a professional who works for an organization, having top-notch problem-solving skills is a necessity and always comes in handy.

Everybody keeps facing new kinds of complex problems every day, and a lot of time is invested in overcoming these obstacles. Moreover, much valuable time is lost while trying to find solutions to unexpected problems, and your plans also get disrupted often.

This is where data analytics comes in. It lets you find and analyze the relevant data without too much of human-support. It’s a real time-saver and has become a necessity in problem-solving nowadays. So if you don’t already use data analytics in solving these problems, you’re probably missing out on a lot!

As the saying goes from the chief analytics officer of TIBCO,

“Think analytically, rigorously, and systematically about a business problem and come up with a solution that leverages the available data .”

– Michael O’Connell.

In this article, I will explain the importance of data analytics in problem-solving and go through the top 5 reasons why it cannot be ignored. So, let’s dive into it right away.

Highly Recommended Articles:

13 Reasons Why Data Analytics is Important in Decision Making

This is Why Business Analytics is Vital in Every Business

Is Data Analysis Qualitative or Quantitative? (We find Out!)

Will Algorithms Erode our Decision-Making Skills?

What is Data Analytics?

Data analytics is the art of automating processes using algorithms to collect raw data from multiple sources and transform it. This results in achieving the data that’s ready to be studied and used for analytical purposes, such as finding the trends, patterns, and so forth.

Why is Data Analytics Important in Problem Solving?

Problem-solving and data analytics often proceed hand in hand. When a particular problem is faced, everybody’s first instinct is to look for supporting data. Data analytics plays a pivotal role in finding this data and analyzing it to be used for tackling that specific problem.

Although the analytical part sometimes adds further complexities, since it’s a whole different process that might get challenging sometimes, it eventually helps you get a better hold of the situation.

Also, you come up with a more informed solution, not leaving anything out of the equation.

Having strong analytical skills help you dig deeper into the problem and get all the insights you need. Once you have extracted enough relevant knowledge, you can proceed with solving the problem.

However, you need to make sure you’re using the right, and complete data, or using data analytics may even backfire for you. Misleading data can make you believe things that don’t exist, and that’s bound to take you off the track, making the problem appear more complex or simpler than it is.

Let’s see a very straightforward daily life example to examine the importance of data analytics in problem-solving; what would you do if a question appears on your exam, but it doesn’t have enough data provided for you to solve the question?

Obviously, you won’t be able to solve that problem. You need a certain level of facts and figures about the situation first, or you’ll be wandering in the dark.

However, once you get the information you need, you can analyze the situation and quickly develop a solution. Moreover, getting more and more knowledge of the situation will further ease your ability to solve the given problem. This is precisely how data analytics assists you. It eases the process of collecting information and processing it to solve real-life problems.

5 Reasons Why Data Analytics Is Important in Problem Solving

Now that we’ve established a general idea of how strongly connected analytical skills and problem-solving are, let’s dig deeper into the top 5 reasons why data analytics is important in problem-solving .

1. Uncover Hidden Details

Data analytics is great at putting the minor details out in the spotlight. Sometimes, even the most qualified data scientists might not be able to spot tiny details existing in the data used to solve a certain problem. However, computers don’t miss. This enhances your ability to solve problems, and you might be able to come up with solutions a lot quicker.

Data analytics tools have a wide variety of features that let you study the given data very thoroughly and catch any hidden or recurring trends using built-in features without needing any effort. These tools are entirely automated and require very little programming support to work. They’re great at excavating the depths of data, going back way into the past.

2. Automated Models

Automation is the future. Businesses don’t have enough time nor the budget to let manual workforces go through tons of data to solve business problems.

Instead, what they do is hire a data analyst who automates problem-solving processes, and once that’s done, problem-solving becomes completely independent of any human intervention.

The tools can collect, combine, clean, and transform the relevant data all by themselves and finally using it to predict the solutions. Pretty impressive, right?

However, there might be some complex problems appearing now and then, which cannot be handled by algorithms since they’re completely new and nothing similar has come up before. But a lot of the work is still done using the algorithms, and it’s only once in a blue moon that they face something that rare.

However, there’s one thing to note here; the process of automation by designing complex analytical and ML algorithms might initially be a bit challenging. Many factors need to be kept in mind, and a lot of different scenarios may occur. But once it goes up and running, you’ll be saving a significant amount of manpower as well as resources.

3. Explore Similar Problems

If you’re using a data analytics approach for solving your problems, you will have a lot of data available at your disposal. Most of the data would indirectly help you in the form of similar problems, and you only have to figure out how these problems are related.

Once you’re there, the process gets a lot smoother because you get references to how such problems were tackled in the past.

Such data is available all over the internet and is automatically extracted by the data analytics tools according to the current problems. People run into difficulties all over the world, and there’s no harm if you follow the guidelines of someone who has gone through a similar situation before.

Even though exploring similar problems is also possible without the help of data analytics, we’re generating a lot of data nowadays , and searching through tons of this data isn’t as easy as you might think. So, using analytical tools is the smart choice since they’re quite fast and will save a lot of your time.

4. Predict Future Problems

While we have already gone through the fact that data analytics tools let you analyze the data available from the past and use it to predict the solutions to the problems you’re facing in the present, it also goes the other way around.

Whenever you use data analytics to solve any present problem, the tools you’re using store the data related to the problem and saves it in the form of variables forever. This way, similar problems faced in the future don’t need to be analyzed again. Instead, you can reuse the previous solutions you have, or the algorithms can predict the solutions for you even if the problems have evolved a bit.

This way, you’re not wasting any time on the problems that are recurring in nature. You jump directly onto the solution whenever you face a situation, and this makes the job quite simple.

5. Faster Data Extraction

However, with the latest tools, the data extraction is greatly reduced, and everything is done automatically with no human intervention whatsoever.

Moreover, once the appropriate data is mined and cleaned, there are not many hurdles that remain, and the rest of the processes are done without a lot of delays.

When businesses come across a problem, around 70%-80% is their time is consumed while gathering the relevant data and transforming it into usable forms. So, you can estimate how quick the process could get if the data analytics tools automate all this process.

Even though many of the tools are open-source, if you’re a bigger organization that can spend a bit on paid tools, problem-solving could get even better. The paid tools are literal workhorses, and in addition to generating the data, they could also develop the models to your solutions, unless it’s a very complex one, without needing any support of data analysts.

What problems can data analytics solve? 3 Real-World Examples

Employee performance problems .

Imagine a Call Center with over 100 agents

By Analyzing data sets of employee attendance, productivity, and issues that tend to delay in resolution. Through that, preparing refresher training plans, and mentorship plans according to key weak areas identified.

Sales Efficiency Problems

Imagine a Business that is spread out across multiple cities or regions

By analyzing the number of sales per area, the size of the sales reps’ team, the overall income and disposable income of potential customers, you can come up with interesting insights as to why some areas sell more or less than the others. Through that, prepping a recruitment and training plan or area expansion in order to boost sales could be a good move.

Business Investment Decisions Problems

Imagine an Investor with a portfolio of apps/software)

By analyzing the number of subscribers, sales, the trends in usage, the demographics, you can decide which peace of software has a better Return on Investment over the long term.

Throughout the article, we’ve seen various reasons why data analytics is very important for problem-solving.

Many different problems that may seem very complex in the start are made seamless using data analytics, and there are hundreds of analytical tools that can help us solve problems in our everyday lives.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

The “problem-solver” approach to data preparation for analytics

By david loshin, president, knowledge integrity, inc..

In many environments, the maturity of your reporting and business analytics functions depends on how effective you are at managing data before it’s time to analyze it. Traditional environments relied on a provisioning effort to conduct data preparation for analytics. After extracting data from source systems, the data landed at a staging area for cleansing, standardization and reorganization before loading it in a data warehouse.

Recently, there has been signification innovation in the evolution of end-user discovery and analysis tools. Often, these systems allow the analyst to bypass the traditional data warehouse by accessing the source data sets directly.

This is putting more data – and analysis of that data – in the hands of more people. This encourages “undirected analysis,” which doesn’t pose any significant problems; the analysts are free to point their tools at any (or all!) data sets, with the hope of identifying some nugget of actionable knowledge that can be exploited.

It’s important to ask the IT department to facilitate a problem-solver approach to data preparation by adjusting the methods by which data sets are made available.

However, it would be naïve to presume that many organizations are willing to allow a significant amount of “data-crunching” time to be spent on purely undirected discovery. Rather, data scientists have specific directions to solve particular types of business problems, such as analyzing:

Global spend to identify opportunities for cost reduction.
Logistics and facets of the supply chain to optimize the delivery channels.
Customer interactions to increase customer lifetime value.

Different challenges have different data needs, but if the analysts need to use data from the original sources, it’s worth considering an alternate approach to the conventional means of data preparation. The data warehouse approach balances two key goals: organized data inclusion (a large amount of data is integrated into a single data platform), and objective presentation (data is managed in an abstract data model specifically suited for querying and reporting).

A new approach to data preparation for analytics

Does the data warehouse approach work in more modern, “built-to-suit” analytics? Maybe not, especially if data scientists go directly to the data – bypassing the data warehouse altogether. For data scientists, armed with analytics at their fingertips, let’s consider a rational, five-step approach to problem-solving.

Clarify the question you want to answer.
Identify the information necessary to answer the question.
Determine what information is available and what is not available.
Acquire the information that is not available.
Solve the problem.

In this process, steps 2, 3, and 4 all deal with data assessment and acquisition – but in a way that is parametrically opposed to the data warehouse approach. First, the warehouse’s data inclusion is predefined, which means that the data that is not available at step 3 may not be immediately accessible from the warehouse in step 4. Second, the objectiveness of the warehouse’s data poses a barrier to creativity on the analyst’s behalf. In fact, this is why data discovery tools that don’t rely on the data warehouse are becoming more popular. By acquiring or accessing alternate data sources, the analyst can be more innovative in problem-solving!

Preparing data with the problem in mind

A problem-solver approach to data preparation for analytics lets the analyst decide what information needs to be integrated into the analysis platform, what transformations are to be done, and how the data is to be used. This approach differs from the conventional extract/transform/load cycle in three key ways:

First, the determination of the data sources is done by the analyst based on data accessibility, not what the IT department has interpreted as a set of requirements.
Second, the analyst is not constrained by the predefined transformations embedded in the data warehouse ETL processes.
Third, the analyst decides the transformations and standardizations that are relevant for the analysis, not the IT department.

While it’s a departure from “standard operating procedure,” it’s important to ask the IT department to facilitate a problem-solver approach to data preparation by adjusting the methods by which data sets are made available. In particular, instead of loading all data into a data warehouse, IT can create an inventory or catalog of data assets that are available for consumption. And instead of applying a predefined set of data transformations, a data management center of excellence can provide a library of available transformations – and a services and rendering layer that an analyst can use for customized data preparation.

Both of these capabilities require some fundamental best practices and enterprise information management tools aside from the end-user discovery technology, such as:

Metadata management as a framework for creating the data asset catalog and ensuring consistency in each data artifact’s use.
Data integration and standardization tools that have an “easy-to-use” interface that can be employed by practitioner and analyst alike.
Business rules-based data transformations that can be performed as part of a set of enterprise data services.
Data federation and virtualization to enable access to virtual data sets whose storage footprint may span multiple sources.
Event stream processing to enable acquisition of data streams as viable and usable data sources.

An evolving environment that encourages greater freedom for the data analyst community should not confine those analysts based on technology decisions for data preparation. Empowering the analysts with flexible tools for data preparation will help speed the time from the initial question to a practical, informed and data-driven decision.

David Loshin, president of Knowledge Integrity, Inc., is a recognized thought leader and expert consultant in the areas of data quality, master data management and business intelligence. David is a prolific author regarding data management best practices, via the expert channel at b-eye-network.com and numerous books, white papers, and web seminars on a variety of data management best practices.

What is a data governance framework... and do I already have one?

5 data management best practices to help you do data right

Get More Insights

Want more Insights from SAS? Subscribe to our Insights newsletter. Or check back often to get more insights on the topics you care about, including analytics , big data , data management , marketing , and risk & fraud .

Data Analytics with R

1 problem solving with data, 1.1 introduction.

This chapter will introduce you to a general approach to solving problems and answering questions using data. Throughout the rest of the module, we will reference back to this chapter as you work your way through your own data analysis exercises.

The approach is applicable to actuaries, data scientists, general data analysts, or anyone who intends to critically analyze data and develop insights from data.

This framework, which some may refer to as The Data Science Process includes the following five main components:

Data Collection
Data Cleaning
Exploratory Data Analysis
Model Building
Inference and Communication

Note that all five steps may not be applicable in every situation, but these steps should guide you as you think about how to approach each analysis you perform.

In the subsections below, we’ll dive into each of these in more detail.

1.2 Data Collection

In order to solve a problem or answer a question using data, it seems obvious that you must need some sort of data to start with. Obtaining data may come in the form of pre-existing or generating new data (think surveys). As an actuary, your data will often come from pre-existing sources within your company. This could include querying data from databases or APIs, being sent excel files, text files, etc. You may also find supplemental data online to assist you with your project.

For example, let’s say you work for a health insurance company and you are interested in determining the average drive time for your insured population to the nearest in-network primary care providers to see if it would be prudent to contract with additional doctors in the area. You would need to collect at least three pieces of data:

Addresses of your insured population (internal company source/database)
Addresses of primary care provider offices (internal company source/database)
Google Maps travel time API to calculate drive times between addresses (external data source)

In summary, data collection provides the fundamental pieces needed to solve your problem or answer your question.

1.3 Data Cleaning

We’ll discuss data cleaning in a little more detail in later chapters, but this phase generally refers to the process of taking the data you collected in step 1, and turning it into a usable format for your analysis. This phase can often be the most time consuming as it may involve handling missing data as well as pre-processing the data to be as error free as possible.

Depending on where you source your data will have major implications for how long this phase takes. For example, many of us actuaries benefit from devoted data engineers and resources within our companies who exert much effort to make our data as clean as possible for us to use. However, if you are sourcing your data from raw files on the internet, you may find this phase to be exceptionally difficult and time intensive.

1.4 Exploratory Data Analysis

Exploratory Data Analysis , or EDA, is an entire subject itself. In short, EDA is an iterative process whereby you:

Generate questions about your data
Search for answers, patterns, and characteristics of your data by transforming, visualizing, and summarizing your data
Use learnings from step 2 to generate new questions and insights about your data

We’ll cover some basics of EDA in Chapter 4 on Data Manipulation and Chapter 5 on Data Visualization, but we’ll only be able to scratch the surface of this topic.

A successful EDA approach will allow you to better understand your data and the relationships between variables within your data. Sometimes, you may be able to answer your question or solve your problem after the EDA step alone. Other times, you may apply what you learned in the EDA step to help build a model for your data.

1.5 Model Building

In this step, we build a model, often using machine learning algorithms, in an effort to make sense of our data and gain insights that can be used for decision making or communicating to an audience. Examples of models could include regression approaches, classification algorithms, tree-based models, time-series applications, neural networks, and many, many more. Later in this module, we will practice building our own models using introductory machine learning algorithms.

It’s important to note that while model building gets a lot of attention (because it’s fun to learn and apply new types of models), it typically encompasses a relatively small portion of your overall analysis from a time perspective.

It’s also important to note that building a model doesn’t have to mean applying machine learning algorithms. In fact, in actuarial science, you may find more often than not that the actuarial models you create are Microsoft Excel-based models that blend together historical data, assumptions about the business, and other factors that allow you make projections or understand the business better.

1.6 Inference and Communication

The final phase of the framework is to use everything you’ve learned about your data up to this point to draw inferences and conclusions about the data, and to communicate those out to an audience. Your audience may be your boss, a client, or perhaps a group of actuaries at an SOA conference.

In any instance, it is critical for you to be able to condense what you’ve learned into clear and concise insights and convince your audience why your insights are important. In some cases, these insights will lend themselves to actionable next steps, or perhaps recommendations for a client. In other cases, the results will simply help you to better understand the world, or your business, and to make more informed decisions going forward.

1.7 Wrap-Up

As we conclude this chapter, take a few minutes to look at a couple alternative visualizations that others have used to describe the processes and components of performing analyses. What do they have in common?

Karl Rohe - Professor of Statistics at the University of Wisconsin-Madison
Chanin Nantasenamat - Associate Professor of Bioinformatics and Youtuber at the “Data Professor” channel

7 Common Data Analytics Problems – & How to Solve Them

By Rotem Yifat, Product Marketing Manager

May 30, 2023

In a nutshell:

Data analysts often face issues with limited value of historical insights and unused insights.
Data goes unused due to limited capacity to process and analyze it.
Bias is unavoidable in traditional predictive modeling.
Long time to value and data-security concerns are common problems.
Predictive analytics platforms can overcome these issues by providing accurate predictions, easy integration, and automated processes.

As a data analyst, your job is to make sense of data by breaking it down into manageable parts, processing it, and performing statistical analyses that reveal trends, patterns, and relationships. And you typically need to present those insights in a way that’s easy for stakeholders to understand.

This process is crucial for organizations that are looking for a data-driven way to make informed decisions, improve business outcomes, and gain a competitive advantage. And thanks to the emergence of new tools, technologies, and techniques, the realm of what’s possible is constantly expanding. Indeed, with the advent of AI, data analytics has become more powerful and efficient than ever before.

However, like many analysts, you may be grappling with some all-too-familiar issues that prevent analysts from doing their best work and making a significant business impact.

In this article, we’ll explore seven common issues faced by data analysts, and how using predictive analytics can be a great way to overcome them.

Problem 1: Limited value of historical insights

The most common application of data analysis is descriptive analytics , where historical data is analyzed in order to understand past trends and events.

The problem? Relying solely on historical insights has limited value, especially in fast-changing businesses where consumer behavior and preferences, as well as market conditions, are constantly evolving. By definition, such insights are based on past trends and events, which may not apply to current or future scenarios and can lead to inaccurate or incomplete analyses.

Relying only on past data can also create bias towards the status quo, which limits your ability to identify new opportunities and potential risks. Take the example of a retailer that relies solely on historical data to determine which products to stock: they’re likely to miss out on new trends or shifts in customer preferences, which leads to missed sales opportunities.

To address this problem, you should strive to complement historical insights with predictive analytics. With this approach, you can identify emerging trends and quickly adapt to changing market conditions.

And by leveraging machine learning within a predictive analytics platform , you can identify meaningful patterns that would otherwise be undetectable. This leads to highly accurate predictions that your business can take advantage of in order to make proactive, well-informed decisions.

Problem 2: Insights aren’t utilized

No one loves the idea of toiling away for months or years, only to discover that their work has been overlooked, undervalued, or not put into practice.

Unfortunately, statistical insights are often viewed as unusable, or even meaningless, if stakeholders can’t easily identify and take relevant action. As mentioned above, this is often the case with descriptive analytics, where there is a focus on the past rather than the future. And as a result, analysts often invest lots of energy into preparing dashboards and reports that are rarely ever used or incorporated into the business workflow.

One way to overcome this challenge is by using a predictive analytics platform that allows you to choose from a variety of pre-built models that can be customized to fit specific use cases. This way, you can easily generate actionable predictions that serve a particular goal, and also enjoy the benefit of automatically generated, easy-to-use dashboards.

In addition, predictions can be integrated directly into your existing work tools. For example: if your goal is to reduce customer churn, you can integrate churn predictions alongside your existing CRM data. This would allow your colleagues to strategize something like an email campaign built on segments reflecting how likely each customer is to churn.

The best way to ensure all your hard work is put to use is by generating insights that can truly guide business decisions and help your company achieve mission-critical KPIs.

Problem 3: Data goes unused

Businesses collect and generate massive amounts of data. But even when they have sufficient resources, they’re not able to use much of this data due to humans’ limited capacity to think about and process data. In many cases, analysts aren’t even sure of whether particular data is worth using due to data quality issues or questions about its meaning.

For these reasons and more, data professionals are generally only able to build a small number of rule-based models, which can only account for two or three variables at a time.

This common challenge can be overcome with a predictive analytics platform like Pecan. By leveraging automated machine learning to analyze vast amounts of data, you can slash the time and effort it takes to decide which data is relevant. Here’s what we mean…

You can instantly feed raw data into a predictive model, and this data can come from any source (such as sales data, user engagement data , customer demographics, and social media). And automating this process means you can use updated data to generate fresh predictions regularly, helping you stay on top of changing customer behavior and market conditions.

The platform will then automatically determine which data is relevant (through behind-the-scenes processes like feature selection and feature engineering), and then will find the best predictive model that can be built using that data.These automated processes enable you to analyze massive datasets and generate accurate predictions within a matter of hours, instead of months. This means you can focus on communicating and creating real value out of your insights, without doing all the heavy lifting.

issues with hand-built machine learning models also included in text

Hand-built models might seem like the ideal solution to data problems, but they can introduce their own issues.

Problem 4: Bias is unavoidable

Traditional predictive modeling involves the use of statistical and mathematical techniques to uncover relationships and identify trends. But as scientific as it may be, there is always human bias in the process of selecting variables.

This means that hand-built models will inevitably, at some point:

Include variables that are not actually important for the model (but possibly correlate with the outcome)
Leave out important variables because they don’t fit with the model builder’s preconceived ideas
Perpetuate or even amplify existing bias (e.g., by restricting your model to a certain gender or zip code)
Fail to generalize beyond your sample set (e.g., if the model is based on data from a limited time period)

This bias doesn’t happen when you use a predictive analytics platform . One key reason is because automated feature engineering will evaluate and construct thousands of potential variables that could be used in your model, and then determine which are most relevant. (Naturally, this multi-variable approach also leads to more accurate and reliable predictions.)

In the case of Pecan, a simple dashboard will reveal how your model arrived at its predictions, by showing the degree to which each variable (a.k.a. feature) influenced its outcomes. This knowledge also enables you to identify and mitigate any potential biases that may be introduced through your raw data itself.

Problem 5: Long time to value

To be implemented and adopted, many analytics tools require significant change management and engineering assistance. And, of course, analytics projects themselves demand a significant amount of time and resources. Sometimes they will bear fruit, and other times they won’t.

Contrast that with a predictive analytics platform, which can do in hours what it might take a data analyst or scientist months to achieve. Not only can you connect multiple data sources to the platform for automatic importing, but with Pecan, you can use Predictive GenAI to define a predictive question that can be answered with a model based on your raw data. Auto-generated code kickstarts your model, which the Pecan engine then automatically builds. The model uses hidden patterns in your data to generate predictions. With those predictions, your business colleagues will be able to act quickly on the provided insights to achieve their business goals.

Another thing to keep in mind: data changes over time, and models require ongoing maintenance in order to remain accurate and effective. In the case of traditional rule-based models, this often means restarting the modeling process from scratch.

But with an automated approach, a predictive model only needs to be built once. With automated model-retraining and monitoring capabilities, all you need to do is feed it new data and/or adjust the variables you wish to use in your predictions.

automated predictive analytics vs traditional data science

The time required to resolve data problems with an automated platform can be far less than preparing data by hand for modeling.

Problem 6: Data-security concerns

Technical difficulties aside, concerns and regulations around data security can make it extremely challenging to integrate different data tools and achieve smooth adoption.

According to a 2020 report by IBM , the average cost of a data breach is $3.86 million. Navigating security best practices and avoiding potential security issues is a lot to ask of a data analyst who is not a security professional yet is tasked with managing and integrating sensitive data across multiple tools, whether locally or in the cloud.

This is where an all-in-one predictive analytics platform again proves advantageous. For example, Pecan prioritizes data security and takes various measures to ensure sensitive information is protected at all times. Let an enterprise-grade solution take care of the security business—so you can focus on yours.

Problem 7: Tedious, time-consuming processes

If we haven’t made it clear by this point, turning raw data into actionable insights is no small feat. And we’d be remiss not to spend some time talking about the person at the center of all this: the data analyst.

In data analytics projects, manual processes add multiple layers of complexity, difficulty, and stress. Analysts often need to carry out tedious and/or time-consuming tasks like data collection, data cleaning, data transformation, data visualization, and, of course, statistical analysis itself. Add a variety of tools and techniques—from programming languages like Python or R to data-visualization tools like Tableau or Power BI to statistical software like SPSS or SAS—and an analyst’s work is cut out for them.

Furthermore, incomplete, inconsistent, inaccurate, or outdated data can significantly impact the effectiveness of a statistical model. So when you add issues of data quality to an already heavy load, fulfilling a data analytics project can seem like an insurmountable feat.

Fortunately, predictive analytics platforms can handle all of the most tedious and complex processes. For example, Pecan automatically performs tasks like data prep , feature engineering, model tuning , and model deployment and monitoring. It can also identify and remove incomplete or inaccurate data and automatically transform your data into the right format for training accurate machine learning models .

What this means is that data analysts, instead of being weighed down by outdated data practices, can focus on building business use cases and imagining how their predictive insights can help solve real business needs.

Wrapping up

An honest assessment of the “old way” of doing things will lead to one obvious conclusion: It’s time to update the way your team performs data analytics. You and your organization should aim for a more holistic approach that maximizes the value of your data. You can gain a complete understanding of your customers and business processes, and make more informed (and profitable) decisions.

Predictive analytics platforms are a great solution for overcoming many of the pain points that plague data analysts. By leveraging machine learning algorithms and automated ML processes, you can quickly analyze huge volumes of data, generate accurate predictions that target a specific business need, and help your business make better decisions that keep you ahead of the competition.

Ready to see how easy it can be to use predictive analytics to supercharge your data analytics role and solve your data problems? Sign up for a free trial now and try it yourself!

How to Build a Predictive Analytics Model, Your Way

Learn how to build a predictive analytics model your way with our guide. Explore methods, data quality tips, and industry examples.

What is an AI Data Analyst?

Ready to become an AI data analyst? Get answers to your biggest questions about a career in AI data analytics, including skills and salary.

A Simple Guide to Rebuilding Your Data Team in 2024

Discover how to rebuild your data team in 2024. Leverage new technologies, foster a data-driven culture, and improve efficiency.

If you're seeing this message, it means we're having trouble loading external resources on our website.

If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.

To log in and use all the features of Khan Academy, please enable JavaScript in your browser.

Unit 11: Advanced: Problem solving and data analysis

About this unit.

Ready for a challenge? This unit covers the hardest problem solving and data analysis questions on the SAT Math test. Work through each skill, taking quizzes and the unit test to level up your mastery progress.

Ratios, rates, and proportions: advanced

Ratios, rates, and proportions | SAT lesson (Opens a modal)
Ratios, rates, and proportions — Basic example (Opens a modal)
Ratios, rates, and proportions — Harder example (Opens a modal)
Ratios, rates, and proportions: advanced Get 3 of 4 questions to level up!

Unit conversion: advanced

Unit conversion | Lesson (Opens a modal)
Units — Basic example (Opens a modal)
Units — Harder example (Opens a modal)
Unit conversion: advanced Get 3 of 4 questions to level up!

Percentages: advanced

Percentages | Lesson (Opens a modal)
Percents — Basic example (Opens a modal)
Percents — Harder example (Opens a modal)
Percentages: advanced Get 3 of 4 questions to level up!

Center, spread, and shape of distributions: advanced

Center, spread, and shape of distributions | Lesson (Opens a modal)
Center, spread, and shape of distributions — Basic example (Opens a modal)
Center, spread, and shape of distributions — Harder example (Opens a modal)
Center, spread, and shape of distributions: advanced Get 3 of 4 questions to level up!

Data representations: advanced

Data representations | Lesson (Opens a modal)
Key features of graphs — Basic example (Opens a modal)
Key features of graphs — Harder example (Opens a modal)
Data representations: advanced Get 3 of 4 questions to level up!

Scatterplots: advanced

Scatterplots | Lesson (Opens a modal)
Scatterplots — Basic example (Opens a modal)
Scatterplots — Harder example (Opens a modal)
Scatterplots: advanced Get 3 of 4 questions to level up!

Linear and exponential growth: advanced

Linear and exponential growth | Lesson (Opens a modal)
Linear and exponential growth — Basic example (Opens a modal)
Linear and exponential growth — Harder example (Opens a modal)
Linear and exponential growth: advanced Get 3 of 4 questions to level up!

Probability and relative frequency: advanced

Probability and relative frequency | Lesson (Opens a modal)
Table data — Basic example (Opens a modal)
Table data — Harder example (Opens a modal)
Probability and relative frequency: advanced Get 3 of 4 questions to level up!

Data inferences: advanced

Data inferences | Lesson (Opens a modal)
Data inferences — Basic example (Opens a modal)
Data inferences — Harder example (Opens a modal)
Data inferences: advanced Get 3 of 4 questions to level up!

Evaluating statistical claims: advanced

Evaluating statistical claims | Lesson (Opens a modal)
Data collection and conclusions — Basic example (Opens a modal)
Data collection and conclusions — Harder example (Opens a modal)
Evaluating statistical claims: advanced Get 3 of 4 questions to level up!

Your Data Won’t Speak Unless You Ask It The Right Data Analysis Questions

Business man searching for the right data analysis questions

In our increasingly competitive digital age, setting the right data analysis and critical thinking questions is essential to the ongoing growth and evolution of your business. It is not only important to gather your business’s existing information but you should also consider how to prepare your data to extract the most valuable insights possible.

That said, with endless rafts of data to sift through, arranging your insights for success isn’t always a simple process. Organizations may spend millions of dollars on collecting and analyzing information with various data analysis tools , but many fall flat when it comes to actually using that data in actionable, profitable ways.

Here we’re going to explore how asking the right data analysis and interpretation questions will give your analytical efforts a clear-cut direction. We’re also going to explore the everyday data questions you should ask yourself to connect with the insights that will drive your business forward with full force.

Let’s get started.

Data Is Only As Good As The Questions You Ask

The truth is that no matter how advanced your IT infrastructure is, your data will not provide you with a ready-made solution unless you ask it specific questions regarding data analysis.

To help transform data into business decisions, you should start preparing the pain points you want to gain insights into before you even start data gathering. Based on your company’s strategy, goals, budget, and target customers you should prepare a set of questions that will smoothly walk you through the online data analysis and enable you to arrive at relevant insights.

For example, you need to develop a sales strategy and increase revenue. By asking the right questions, and utilizing sales analytics software that will enable you to mine, manipulate and manage voluminous sets of data, generating insights will become much easier. An average business user and cross-departmental communication will increase its effectiveness, decreasing the time to make actionable decisions and, consequently, providing a cost-effective solution.

Before starting any business venture, you need to take the most crucial step: prepare your data for any type of serious analysis. By doing so, people in your organization will become empowered with clear systems that can ultimately be converted into actionable insights. This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy.

“Today, big data is about business disruption. Organizations are embarking on a battle not just for success but for survival. If you want to survive, you need to act.” – Capgemini and EMC² in their study Big & Fast Data: The Rise of Insight-Driven Business .

This quote might sound a little dramatic. However, consider the following statistics pulled from research developed by Forrester Consulting and Collibra:

84% of correspondents report that data at the center stage of developing business strategies is critical
81% of correspondents realized an advantage in growing revenue
8% admit an advantage in improving customers' trust
58% of "data intelligent" organizations are more likely to exceed revenue goals

Based on this survey, it seems that business professionals believe that data is the ultimate cure for all their business ills. And that's not a surprise considering the results of the survey and the potential that data itself brings to companies that decide to utilize it properly. Here we will take a look at data analysis questions examples and explain each in detail.

19 Data Analysis Questions To Improve Your Business Performance In The Long Run

What are data analysis questions, exactly? Let’s find out. While considering the industry you’re in, and competitors your business is trying to outperform, data questions should be clearly defined. Poor identification can result in faulty interpretation, which can directly affect business efficiency, and general results, and cause problems.

Here at datapine, we have helped solve hundreds of analytical problems for our clients by asking big data questions. All of our experience has taught us that data analysis is only as good as the questions you ask. Additionally, you want to clarify these questions regarding analytics now or as soon as possible – which will make your future business intelligence much clearer. Additionally, incorporating a decision support system software can save a lot of the company’s time – combining information from raw data, documents, personal knowledge, and business models will provide a solid foundation for solving business problems.

That’s why we’ve prepared this list of data analysis questions examples – to be sure you won’t fall into the trap of futile, “after the fact” data processing, and to help you start with the right mindset for proper data-driven decision-making while gaining actionable business insights.

1) What exactly do you want to find out?

It’s good to evaluate the well-being of your business first. Agree company-wide on what KPIs are most relevant for your business and how they already develop. Research different KPI examples and compare them to your own. Think about what way you want them to develop further. Can you influence this development? Identify where changes can be made. If nothing can be changed, there is no point in analyzing data. But if you find a development opportunity, and see that your business performance can be significantly improved, then a KPI dashboard software could be a smart investment to monitor your key performance indicators and provide a transparent overview of your company’s data.

The next step is to consider what your goal is and what decision-making it will facilitate. What outcome from the analysis you would deem a success? These introductory examples of analytical questions are necessary to guide you through the process and focus on key insights. You can start broad, by brainstorming and drafting a guideline for specific questions about the data you want to uncover. This framework can enable you to delve deeper into the more specific insights you want to achieve.

Let’s see this through an example and have fun with a little imaginative exercise.

Let’s say that you have access to an all-knowing business genie who can see into the future. This genie (who we’ll call Data Dan) embodies the idea of a perfect data analytics platform through his magic powers.

Now, with Data Dan, you only get to ask him three questions. Don’t ask us why – we didn’t invent the rules! Given that you’ll get exactly the right answer to each of them, what are you going to ask it? Let’s see….

Talking With A Data Genie

You: Data Dan! Nice to meet you, my friend. Didn’t know you were real.

Data Dan: Well, I’m not actually. Anyways – what’s your first data analysis question?

You: Well, I was hoping you could tell me how we can raise more revenue in our business.

Data Dan: (Rolls eyes). That’s a pretty lame question, but I guess I’ll answer it. How can you raise revenue? You can do partnerships with some key influencers, you can create some sales incentives, and you can try to do add-on services to your most existing clients. You can do a lot of things. Ok, that’s it. You have two questions left.

You: (Panicking) Uhhh, I mean – you didn’t answer well! You just gave me a bunch of hypotheticals!

Data Dan: I exactly answered your question. Maybe you should ask for better ones.

You: (Sweating) My boss is going to be so mad at me if I waste my questions with a magic business genie. Only two left, only two left… OK, I know! Genie – what should I ask you to make my business the most successful?

Data Dan: OK, you’re still not good at this, but I’ll be nice since you only have one data question left. Listen up buddy – I’m only going to say this once.

The Key To Asking Good Analytical Questions

Data Dan: First of all, you want your questions to be extremely specific. The more specific it is, the more valuable (and actionable) the answer is going to be. So, instead of asking, “How can I raise revenue?”, you should ask: “What are the channels we should focus more on in order to raise revenue while not raising costs very much, leading to bigger profit margins?”. Or even better: “Which marketing campaign that I did this quarter got the best ROI, and how can I replicate its success?”

These key questions to ask when analyzing data can define your next strategy in developing your organization. We have used a marketing example, but every department and industry can benefit from proper data preparation. By using a multivariate analysis, different aspects can be covered and specific inquiries defined.

2) What standard KPIs will you use that can help?

OK, let’s move on from the whole genie thing. Sorry, Data Dan! It’s crucial to know what data analysis questions you want to ask from the get-go. They form the bedrock for the rest of this process.

Think about it like this: your goal with business intelligence is to see reality clearly so that you can make profitable decisions to help your company thrive. The questions to ask when analyzing data will be the framework, the lens, that allows you to focus on specific aspects of your business reality.

Once you have your data analytics questions, you need to have some standard KPIs that you can use to measure them. For example, let’s say you want to see which of your PPC campaigns last quarter did the best. As Data Dan reminded us, “did the best” is too vague to be useful. Did the best according to what? Driving revenue? Driving profit? Giving the most ROI? Giving the cheapest email subscribers?

All of these KPI examples can be valid choices. You just need to pick the right ones first and have them in agreement company-wide (or at least within your department).

Let’s see this through a straightforward example.

The total volume of sales, a retail KPI showing the amount of sales over a period of time

You are a retail company and want to know what you sell, where, and when – remember the specific questions for analyzing data? In the example above, it is clear that the amount of sales performed over a set period tells you when the demand is higher or lower – you got your specific KPI answer. Then you can dig deeper into the insights and establish additional sales opportunities, and identify underperforming areas that affect the overall sales of products.

It is important to note that the number of KPIs you choose should be limited as monitoring too many can make your analysis confusing and less efficient. As the old analytics saying goes, just because you can measure something, it doesn't mean you should. We recommended sticking to a careful selection of 3-6 KPIs per business goal, this way, you'll avoid getting distracted by meaningless data.

The criteria to pick your KPIs is they should be attainable, realistic, measurable in time, and directly linked to your business goals. It is also a good practice to set KPI targets to measure the progress of your efforts.

Now let’s proceed to one of the most important data questions to ask – the data source.

3) Where will your data come from?

Our next step is to identify data sources you need to dig into all your data, pick the fields that you’ll need, leave some space for data you might potentially need in the future, and gather all the information in one place. Be open-minded about your data sources in this step – all departments in your company, sales, finance, IT, etc., have the potential to provide insights.

Don’t worry if you feel like the abundance of data sources makes things seem complicated. Our next step is to “edit” these sources and make sure their data quality is up to par, which will get rid of some of them as useful choices.

Right now, though, we’re just creating the rough draft. You can use CRM data, data from things like Facebook and Google Analytics, or financial data from your company – let your imagination go wild (as long as the data source is relevant to the questions you’ve identified in steps 1 and It could also make sense to utilize business intelligence software , especially since datasets in recent years have expanded in so much volume that spreadsheets can no longer provide quick and intelligent solutions needed to acquire a higher quality of data.

Another key aspect of controlling where your data comes from and how to interpret it effectively boils down to connectivity. To develop a fluent data analytics environment, using data connectors is the way forward.

Digital data connectors will empower you to work with significant amounts of data from several sources with a few simple clicks. By doing so, you will grant everyone in the business access to valuable insights that will improve collaboration and enhance productivity.

3.5) Which scales apply to your different datasets?

WARNING: This is a bit of a “data nerd out” section. You can skip this part if you like or if it doesn’t make much sense to you.

You’ll want to be mindful of the level of measurement for your different variables, as this will affect the statistical techniques you will be able to apply in your analysis.

There are basically 4 types of scales:

Table of the levels of measurements according to the type of descriptive statistic

*Statistics Level Measurement Table*

Nominal – you organize your data in non-numeric categories that cannot be ranked or compared quantitatively.

Examples: – Different colors of shirts – Different types of fruits – Different genres of music

Ordinal – GraphPad gives this useful explanation of ordinal data:

“You might ask patients to express the amount of pain they are feeling on a scale of 1 to 10. A score of 7 means more pain than a score of 5, and that is more than a score of 3. But the difference between the 7 and the 5 may not be the same as that between 5 and 3. The values simply express an order. Another example would be movie ratings, from 0 to 5 stars.”

Interval – in this type of scale, data is grouped into categories with order and equal distance between these categories.

Direct comparison is possible. Adding and subtracting is possible, but you cannot multiply or divide the variables. Example: Temperature ratings. An interval scale is used for both Fahrenheit and Celsius.

Again, GraphPad has a ready explanation: “The difference between a temperature of 100 degrees and 90 degrees is the same difference as between 90 degrees and 80 degrees.”

Ratio – has the features of all three earlier scales.

Like a nominal scale, it provides a category for each item, items are ordered like on an ordinal scale and the distances between items (intervals) are equal and carry the same meaning.

With ratio scales, you can add, subtract, divide, multiply… all the fun stuff you need to create averages and get some cool, useful data. Examples: height, weight, revenue numbers, leads, and client meetings.

4) Will you use market and industry benchmarks?

In the previous point, we discussed the process of defining the data sources you’ll need for your analysis as well as different methods and techniques to collect them. While all of those internal sources of information are invaluable, it can also be a useful practice to gather some industry data to use as benchmarks for your future findings and strategies.

To do so, it is necessary to collect data from external sources such as industry reports, research papers, government studies, or even focus groups and surveys performed on your targeted customer as a market research study to extract valuable information regarding the state of the industry in general but also the position each competitor occupies in the market.

In doing so, you’ll not only be able to set accurate benchmarks for what your company should be achieving but also identify areas in which competitors are not strong enough and exploit them as a competitive advantage. For example, you can perform a market research survey to analyze the perception customers have about your brand and your competitors and generate a report to analyze the findings, as seen in the image below.

**click to enlarge**

This market research dashboard is displaying the results of a survey on brand perception for 8 outdoor brands. Respondents were asked different questions to analyze how each brand is recognized within the industry. With these answers, decision-makers are able to complement their strategies and exploit areas where there is potential.

5) Is the data in need of cleaning?

Insights and analytics based on a shaky “data foundation” will give you… well, poor insights and analytics. As mentioned earlier, information comes from various sources, and they can be good or bad. All sources within a business have a motivation for providing data, so the identification of which information to use and from which source it is coming should be one of the top questions to ask about data analytics.

Remember – your data analysis questions are designed to get a clear view of reality as it relates to your business being more profitable. If your data is incorrect, you’re going to be seeing a distorted view of reality.

That’s why your next step is to “clean” your data sets in order to discard wrong, duplicated, or outdated information. This is also an appropriate time to add more fields to your data to make it more complete and useful. That can be done by a data scientist or individually, depending on the size of the company.

An interesting survey comes from CrowdFlower , a provider or a data enrichment platform among data scientists. They have found out that most data scientists spend:

60% of their time organizing and cleaning data (!).
19% is spent on collecting datasets.
9% is spent mining the data to draw patterns.
3% is spent on training the datasets.
4% is spent refining the algorithms.
5% of the time is spent on other tasks.

57% of them consider the data cleaning process the most boring and least enjoyable task. If you are a small business owner, you probably don’t need a data scientist, but you will need to clean your data and ensure a proper standard of information.

Yes, this is annoying, but so are many things in life that are very important.

When you’ve done the legwork to ensure your data quality, you’ll have built yourself the useful asset of accurate data sets that can be transformed, joined, and measured with statistical methods. But, cleaning is not the only thing you need to do to ensure data quality, there are more things to consider which we’ll discuss in the next question.

6) How can you ensure data quality?

Did you know that poor data quality costs the US economy up to $3.1 trillion yearly? Taking those numbers into account it is impossible to ignore the importance of this matter. Now, you might be wondering, what do I do to ensure data quality?

We already mentioned making sure data is cleaned and prepared to be analyzed is a critical part of it, but there is more. If you want to be successful on this matter, it is necessary to implement a carefully planned data quality management system that involves every relevant data user in the organization as well as data-related processes from acquisition to distribution and analysis.

Some best practices and key elements of a successful data quality management process include:

Carefully clean data with the right tools.
Tracking data quality metrics such as the rate of errors, data validity, and consistency, among others.
Implement data governance initiatives to clearly define the roles and responsibilities for data access and manipulation
Ensure security standards for data storage and privacy are being implemented
Rely on automation tools to clean and update data to avoid the risk of manual human error

These are only a couple of the many actions you can take to ensure you are working with the correct data and processes. Ensuring data quality across the board will save your business a lot of money by avoiding costly mistakes and bad-informed strategies and decisions.

7) Which statistical analysis techniques do you want to apply?

There are dozens of statistical analysis techniques that you can use. However, in our experience, these 3 statistical techniques are most widely used for business:

Regression Analysis – a statistical process for estimating the relationships and correlations among variables.

More specifically, regression helps understand how the typical value of the dependent variable changes when any of the independent variables is varied, while the other independent variables are held fixed.

In this way, regression analysis shows which among the independent variables are related to the dependent variable, and explores the forms of these relationships. Usually, regression analysis is based on past data, allowing you to learn from the past for better decisions about the future.

Cohort Analysis – it enables you to easily compare how different groups, or cohorts, of customers, behave over time.

For example, you can create a cohort of customers based on the date when they made their first purchase. Subsequently, you can study the spending trends of cohorts from different periods in time to determine whether the quality of the average acquired customer is increasing or decreasing over time.

Cohort analysis tools give you quick and clear insight into customer retention trends and the perspectives of your business.

Predictive & Prescriptive Analysis – in short, it is based on analyzing current and historical datasets to predict future possibilities, including alternative scenarios and risk assessment.

Methods like artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA), time series, seasonal naïve approach, and data mining find wide application in data analytics nowadays.

Conjoint analysis: Conjoint analytics is a form of statistical analysis that firms use in market research to understand how customers value different components or features of their products or services.

This type of analytics is incredibly valuable, as it will give you the insight required to see how your business’s products are really perceived by your audience, giving you the tools to make targeted improvements that will offer a competitive advantage.

Cluster analysis: Cluster or 'clustering' refers to the process of grouping a set of objects or datasets. With this type of analysis, objects are placed into groups (known as a cluster) based on their values, attributes, or similarities.

This branch of analytics is often seen when working with autonomous applications or trying to identify particular trends or patterns.

We’ve already explained them and recognized them among the biggest business intelligence trends for 2022. Your choice of method should depend on the type of data you’ve collected, your team’s skills, and your resources.

8) What ETL procedures need to be developed (if any)?

One of the crucial questions to ask when analyzing data is if and how to set up the ETL process. ETL stands for Extract-Transform-Load, a technology used to read data from a database, transform it into another form and load it into another database. Although it sounds complicated for an average business user, it is quite simple for a data scientist. You don’t have to do all the database work, but an ETL service does it for you; it provides a useful tool to pull your data from external sources, conform it to demanded standards, and convert it into a destination data warehouse. These tools provide an effective solution since IT departments or data scientists don’t have to manually extract information from various sources, or you don’t have to become an IT specialist to perform complex tasks.

*ETL data warehouse*

If you have large data sets, and today most businesses do, it would be wise to set up an ETL service that brings all the information your organization is using and can optimize the handling of data.

9) What limitations will your analysis process have (if any)?

This next question is fundamental to ensure success in your analytical efforts. It requires you to put yourself in all the potential worst-case scenarios so you can prepare in advance and tackle them immediately with a solution. Some common limitations can be related to the data itself such as not enough sample size in a survey or research, lack of access to necessary technologies, and insufficient statistical power, among many others, or they can be related to the audience and users of the analysis such as lack of technical knowledge to understand the data.

No matter which of these limitations you might face, identifying them in advance will help you be ready for anything. Plus, it will prevent you from losing time trying to find a solution for an issue, something that is especially valuable in a business context in which decisions need to be made as fast as possible.

10) Who are the final users of your analysis results?

Another of the significant data analytics questions refers to the end-users of our analysis. Who are they? How will they apply your reports? You must get to know your final users, including:

What they expect to learn from the data
What their needs are
Their technical skills
How much time they can spend analyzing data?

Knowing the answers will allow you to decide how detailed your data report will be and what data you should focus on.

Remember that internal and external users have diverse needs. If the reports are designed for your own company, you more or less know what insights will be useful for your staff and what level of data complexity they can struggle through.

However, if your reports will also be used by external parties, remember to stick to your corporate identity. The visual reports you provide them with should be easy-to-use and actionable. Your final users should be able to read and understand them independently, with no IT support needed.

Also: think about the status of the final users. Are they junior members of the staff or part of the governing body? Every type of user has diverse needs and expectations.

11) How will the analysis be used?

Following on the latest point, after asking yourself who will use your analysis, you also need to ask yourself how you’re actually going to put everything into practice. This will enable you to arrange your reports in a way that transforms insight into action.

Knowing which questions to ask when analyzing data is crucial, but without a plan of informational action, your wonderfully curated mix of insights may as well be collecting dust on the virtual shelf. Here, we essentially refer to the end-use of your analysis. For example, when building reports, will you use it once as a standalone tool, or will you embed it for continual analytical use?

Embedded analytics is essentially a branch of BI technology that integrates professional dashboards or platforms into your business's existing applications to enhance its analytical scope and abilities. By leveraging the power of embedded dashboards , you can squeeze the juice out of every informational touchpoint available to your organization, for instance, by delivering external reports and dashboard portals to your external stakeholders to share essential information with them in a way that is interactive and easy to understand.

Another key aspect of considering how you’re going to use your reports is to understand which mediums will work best for different kinds of users. In addition to embedded reports, you should also consider whether you want to review your data on a mobile device, as a file export, or even printed to mull through your newfound insights on paper. Considering and having these options at your disposal will ensure your analytical efforts are dynamic, flexible, and ultimately more valuable.

The bottom line? Decide how you’re going to use your insights in a practical sense, and you will set yourself on the path to data enlightenment.

12) What data visualizations should you choose?

Your data is clean and your calculations are done, but you are not finished yet. You can have the most valuable insights in the world, but if they’re presented poorly, your target audience won’t receive the impact from them that you’re hoping for.

And we don’t live in a world where simply having the right data is the end-all, be-all. You have to convince other decision-makers within your company that this data is:

Urgent to act upon

Effective presentation aids in all of these areas. There are dozens of data charts to choose from and you can either thwart all your data-crunching efforts by picking the wrong data visualization (like displaying a time evolution on a pie chart) or give it an additional boost by choosing the right types of graphs .

There are a number of online data visualization tools that can get the hard work done for you. These tools can effectively prepare the data and interpret the outcome. Their ease of use and self-service application in testing theories, analyzing changes in consumer buying behavior, leverage data for analytical purposes without the assistance of analysts or IT professionals have become an invaluable resource in today’s data management practice.

By being flexible enough to personalize its features to the end-user and adjust to your prepared questions for analyzing data, the tools enable a voluminous analysis that can help you not to overlook any significant issue of the day or the overall business strategy.

Dynamic modern dashboards are far more powerful than their static counterparts. You can reach out and interact with the information before you while gaining access to accurate real-time data at a glance. With interactive dashboards, you can also access your insights via mobile devices with the swipe of a screen or the click of a button 24/7. This will give you access to every single piece of analytical data you will ever need.

13) What kind of software will help?

Continuing on our previous point, there are some basic and advanced tools that you can utilize. Spreadsheets can help you if you prefer a more traditional, static approach, but if you need to tinker with the data on your own, perform basic and advanced analysis on a regular basis, and have real-time insights plus automated reports, then modern and professional tools are the way to go.

With the expansion of business intelligence solutions , data analytics questions to ask have never been easier. Powerful features such as basic and advanced analysis, countless chart types, quick and easy data source connection, and endless possibilities to interact with the data as questions arise, enable users to simplify oftentimes complex processes. No matter the analysis type you need to perform, the designated software will play an essential part in making your data alive and "able to speak."

Moreover, modern software will not require continuous manual updates of the data but it will automatically provide real-time insights that will help you answer critical questions and provide a stable foundation and prerequisites for good analysis.

14) What advanced technologies do you have at your disposal?

When you're deciding on which analysis question to focus on, considering which advanced or emerging technologies you have at your disposal is always essential.

By working with the likes of artificial intelligence (AI), machine learning (ML), and predictive analytics, you will streamline your data questions analysis strategies while gaining an additional layer of depth from your information.

The above three emerging technologies are interlinked in the sense that they are autonomous and aid business intelligence (BI) across the board. Using AI technology, it’s possible to automate certain data curation and analytics processes to boost productivity and hone in on better-quality insights.

By applying ML innovations, you can make your data analysis dashboards smarter with every single action or interaction, creating a self-improving ecosystem where you consistently boost the efficiency as well as the informational value of your analytical efforts with minimal human intervention.

From this ecosystem will emerge the ability to utilize predictive analytics to make accurate projections and develop organizational strategies that push you ahead of the competition. Armed with the ability to spot visual trends and patterns, you can nip any emerging issues or inefficiencies in the bud while playing on your current strengths for future gain.

With datapine, you can leverage the power of autonomous technologies by setting up data alerts that will notify you of a variety of functions - the kind that will help you exceed your business goals, as well as identify emerging patterns and particular numeric or data-driven thresholds. These BI features armed with cutting-edge technology will optimize your analytical activities in a way that will foster innovation and efficiency across the business.

15) How regularly should you check your data?

Once you’ve answered all of the previous questions you should be 80% on the right track to be successful with your analytical efforts. That being said, data analytics is a never-ending process that requires constant monitoring and optimization. This leads us to our next question: how regularly should you check your data?

There is no correct answer to this question as the frequency will depend on the goals of your analysis and the type of data you are tracking. In a business setting, there will be reports that contain data that you’ll need to track on a daily basis and in real-time since they influence the immediate performance of your organization for example, the marketing department might want to track the performance of their paid campaigns on a daily basis to optimize them and make the most out of their marketing budget.

Likewise, there are other areas that can benefit from monthly tracking to extract more in-depth conclusions. For example, the customer service team might want to track the number of issues by channel on a monthly basis to identify patterns that can help them optimize their service.

Modern data analysis tools provide users with the ability to automatically update their data as soon as it is generated. This alleviates the pain of having to manually check the data for new insights while significantly reducing the risk of human error. That said, no matter what frequency of monitoring you choose, it is also important to constantly check your data and analytical strategies to see if they still make sense for the current situation of the business. More on this in the next question.

16) What else do you need to know?

Before finishing up, one of the crucial questions to ask about data analytics is how to verify the results. Remember that statistical information is always uncertain even if it is not reported in that way. Thinking about which information is missing and how you would use more information if you had it could be one point to consider. That way you can identify potential information that could help you make better decisions. Keep also in mind that by using simple bullet points or spreadsheets, you can overlook valuable information that is already established in your business strategy.

Always go back to the original objectives and make sure you look at your results in a holistic way. You will want to make sure your end result is accurate and that you haven’t made any mistakes along the way. In this step, important questions for analyzing data should be focused on:

Does is it make sense on a general level?
Are the measures I’m seeing in line with what I already know about the business?

Your end result is equally important as your process beforehand. You need to be certain that the results are accurate, verify the data, and ensure that there is no space for big mistakes. In this case, there are some data analysis types of questions to ask such as the ones we mentioned above. These types of questions will enable you to look at the bigger picture of your analytical efforts and identify any points that need more adjustments or additional details to work on.

You can also test your analytical environment against manual calculations and compare the results. If there are extreme discrepancies, there is something clearly wrong, but if the results turn accurate, then you have established a healthy data environment. Doing such a full-sweep check is definitely not easy, but in the long term, it will bring only positive results. Additionally, if you never stop questioning the integrity of your data, your analytical audits will be much healthier in the long run.

17) How can you create a data-driven culture?

Dirty data is costing you.

Whether you are a small business or a large enterprise, the data tell its story, and you should be able to listen. Preparing questions to ask about data analytics will provide a valuable resource and a roadmap to improved business strategies. It will also enable employees to make better departmental decisions and, consequently, create a cost-effective business environment that can help your company grow. Dashboards are a great way to establish such a culture, like in our financial dashboard example below:

Data report example from the financial department

In order to truly incorporate this data-driven approach to running the business, all individuals in the organization, regardless of the department they work in, need to know how to start asking the right data analytics questions.

They need to understand why it is important to conduct data analysis in the first place.

However, simply wishing and hoping that others will conduct data analysis is a strategy doomed to fail. Frankly, asking them to use data analysis (without showing them the benefits first) is also unlikely to succeed.

Instead, lead by example. Show your internal users that the habit of regular data analysis is a priceless aid for optimizing your business performance. Try to create a beneficial dashboard culture in your company.

Data analysis isn’t a means to discipline your employees and find who is responsible for failures, but to empower them to improve their performance and self-improve.

18) Are you missing anything, and is the data meaningful enough?

Once you’ve got your data analytics efforts off the ground and started to gain momentum, you should take the time to explore all of your reports and visualizations to see if there are any informational gaps you can fill.

Hold collaborative meetings with department heads and senior stakeholders to vet the value of your KPIs, visualizations, and data reports. You might find that there is a particular function you’ve brushed over or that a certain piece of data might be better displayed in a different format for greater insight or clarity.

Making an effort to keep track of your return on investment (ROI) and rates of improvements in different areas will help you paint a panoramic picture that will ultimately let you spot any potential analytical holes or data that is less meaningful than you originally thought.

For example, if you’re tracking sales targets and individual rep performance, you will have enough information to make improvements to the department. But with a collaborative conversation and a check on your departmental growth or performance, you might find that also throwing customer lifetime value and acquisition costs into the mix will offer greater context while providing additional insight.

While this is one of the most vital ongoing data analysis questions to ask, you would be amazed at how many decision-makers overlook it: look at the bigger picture, and you will gain an edge on the competition.

19) How can you keep improving the analysis strategy?

When it comes to business questions for analytics, it’s essential to consider how you can keep improving your reports, processes, or visualizations to adapt to the landscape around you.

Regardless of your niche or sector, in the digital age, everything is in constant motion. What works today may become obsolete tomorrow. So, when prioritizing which questions to ask for analysis, it’s vital to decide how you’re going to continually evolve your reporting efforts.

If you’ve paid attention to business questions for data analysis number 18 (“Am I missing anything?” and “Is my data meaningful enough?”), you already have a framework for identifying potential gaps or weaknesses in your data analysis efforts. To take this one step further, you should explore every one of your KPIs or visualizations across departments and decide where you might need to update particular targets, modify your alerts, or customize your visualizations to return insights that are more relevant to your current situation.

You might, for instance, decide that your warehouse KPI dashboard needs to be customized to drill down further into total on-time shipment rates due to recent surges in customer order rates or operational growth.

There is a multitude of reasons you will need to tweak or update your analytical processes or reports. By working with the right BI technology while asking yourself the right questions for analyzing data, you will come out on top time after time.

Start Your Analysis Today!

We just outlined a 19-step process you can use to set up your company for success through the use of the right data analysis questions.

With this information, you can outline questions that will help you to make important business decisions and then set up your infrastructure (and culture) to address them on a consistent basis through accurate data insights. These are good data analysis questions and answers to ask when looking at a data set but not only, as you can develop a good and complete data strategy if you utilize them as a whole. Moreover, if you rely on your data, you can only reap benefits in the long run and become a data-driven individual, and company.

To sum it up, here are the most important data questions to ask:

What exactly do you want to find out?
What standard KPIs will you use that can help?
Where will your data come from?
Will you use market benchmarks?
Is your data in need of cleaning?
How can you ensure data quality?
Which statistical analysis techniques do you want to apply?
What ETL procedures need to be developed (if any?)
What limitations will your analysis process have (if any)?
Who are the final users of your analysis results?
How will your analysis be used?
What data visualization should you choose?
What kind of software will help?
What advanced technologies do you have at your disposal?
What else do you need to know?
How regularly should you check your data?
How can you create a data-driven culture?
Are you missing anything, and is the data meaningful enough?
How can you keep improving the analysis strategy?

Weave these essential data analysis question examples into your strategy, and you will propel your business to exciting new heights.

To start your own analysis, you can try our software for a 14-day trial - completely free!

Smart. Open. Grounded. Inventive. Read our Ideas Made to Matter.

Which program is right for you?

Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.

A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.

A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.

Earn your MBA and SM in engineering with this transformative two-year program.

Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

A joint program for mid-career professionals that integrates engineering and systems thinking. Earn your master’s degree in engineering and management.

An interdisciplinary program that combines engineering, management, and design, leading to a master’s degree in engineering and management.

Executive Programs

A full-time MBA program for mid-career leaders eager to dedicate one year of discovery for a lifetime of impact.

This 20-month MBA program equips experienced executives to enhance their impact on their organizations and the world.

Non-degree programs for senior executives and high-potential managers.

A non-degree, customizable program for mid-career professionals.

Categorical thinking can lead to investing errors

How storytelling helps data-driven teams succeed

Financial services’ deliberate approach to AI

Credit: Mimi Phan / Shutterstock

Ideas Made to Matter

3 business problems data analytics can help solve

Sep 18, 2023

Generative artificial intelligence is booming, the post-COVID economy wobbles on, and the climate crisis is growing. Amid this disruption, what practical problems are global businesses trying to solve in 2023?

Each year, the MIT Sloan Master of Business Analytics Capstone Project partners students with companies that are looking to solve a business problem with data analytics. The program offers unique and up-close insight into what companies were grappling with at the beginning of 2023. This year, students worked on 41 different projects with 33 different companies. The winning projects looked at measuring innovation through patents for Accenture and using artificial intelligence to improve drug safety for Takeda.

“This annual tradition is an insightful pulse check on the ‘data wish list’ of the industry’s top analytics leaders,” said MIT Sloan lecturer Jordan Levine, who leads the Capstone program.

Here are three questions that companies are seeking to answer with analytics.

1. How can data help us identify growth in specific geographic regions?

Businesses looking to open new locations or invest in real estate are using data to find areas that are poised for growth.

Understanding urbanization is important for firms like JPMorgan Chase , which aims to reach new clients and serve existing customers by opening new bank branches in U.S. cities. To get a handle on what areas are likely to grow in the future, the company is using satellite images — including land-cover segmentation from Google — to predict urbanization rates and identify hot spots .

Small and medium-sized businesses account for about 99% of U.S. companies but only 40% of the U.S. economy. Using historic transaction data and U.S. census data, Visa is looking at what parts of the U.S. have the most potential for SMB growth and what levers it can use to help develop those areas, such as helping businesses accept digital transactions.

Asset management firm Columbia Threadneedle wants to identify promising areas for real estate investment in Europe by building a predictive tool for location growth, using factors such as economic drivers, livability, connectivity, and demographics. MBAn students created a tool that predicts long-term growth potential for more than 600 cities and identifies key factors used to make those predictions.

2. How can data help us empower front-line workers?

Employees working directly with customers or in the field often have to make educated guesses and snap decisions. Companies are turning to data analytics to create support tools that will improve efficiency, accuracy, and sales.

Coca-Cola Southwest Beverages is looking to improve how front-line workers assess store inventory and create orders — a process that is now time-consuming and prone to errors. Using demographics, consumption trends, historical sales data, and out-of-stock information, a sales forecast algorithm will improve forecasting, increase sales, and simplify operations.

Handle Global , a health care supply chain technology company, is looking to help hospitals estimate budget allocation and capital expenditures for medical devices, given the churn of assets, variations in types and models, and mergers and acquisitions between manufacturers and hospital systems. The company is looking to develop a decision support tool that uses historic data to make better purchasing decisions.

3. What’s the best way to get the most from large or unwieldy datasets?

While data analytics can produce powerful results, some data is still hard to process, such as unstructured data — data that does not conform to a specific format — or large datasets. Companies are looking for ways to efficiently process and gain insight from this kind of data, which can be time-consuming and inefficient to process.

Health insurance pricing data is now available to competing companies, thanks to a new U.S. government regulation . But this information isn’t easy to access because of the sheer volume of data, insurer noncompliance with disclosure requirements, and data that’s broken into several different categories. Wellmark Blue Cross and Blue Shield is looking to create a coverage rate transparency tool that recommends pricing and areas for negotiation to help it maintain competitive advantage and see optimal profits.

Information services company Wolters Kluwer ’s compliance business unit helps firms meet regulatory requirements while managing risk and increasing efficiency. But verifying government documents, such as vehicle registrations, can be an error-prone and time-consuming process, and the documents have a high rejection rate. The company is looking to create a document classification system using natural language processing and computer vision that makes paperwork that is usually handled manually more accurate and easier to process.

CogniSure AI was created in 2019 to use technology to solve the problem of unstructured data, which makes it difficult to digitize the insurance underwriting industry. The company is looking to build a generic machine learning tool to process documents that are not yet automated , such as loss runs — claims histories of past losses — which have complex and varied formats and structures.

View all of the capstone projects

A stack of jeans with network/AI imagery overlayed on top

Problem Solving and Data Analysis

We have lots of free resources and videos to help you prepare for the SAT. These materials are for the redesigned SAT which is for you if you are taking the SAT in March 2016 and beyond.

Related Pages More Lessons for SAT Math More Resources for SAT

Problem Solving and Data Analysis includes questions that test your ability to

create a representation of the problem.
consider the units involved.
pay attention to the meaning of quantities.
know and use different properties of mathematical properties and representations.
apply key principles of statistics.
estimate the probability of a simple or compound event.

There are many ways that you can be tested and practicing different types of questions will help you to be prepared for the SAT.

The following video lessons will show you how to solve a variety of problem solving and data analysis questions in different situations.

Ratio, Proportion, Units and Percentages

There will be questions on ratios. A ratio represents the proportional relationship between quantities. Fractions can be used to represent ratios.

There will also be questions involving percentages. Percent is a type proportion that means “per 100”.

You will need to convert units when required by the question. One way to perform unit conversion is to write it out as a series of multiplication steps.

Question 1 Question 2 Question 3 Question 4 Question 5 Question 6 Question 7 Question 8

Charts, Graphs and Tables

The questions in Problem Solving and Data Analysis focus on linear, quadratic and exponential relationships which may be represented by charts, graphs or tables. A model is linear if the difference in quantity is constant. A model is exponential if the ratio in the quantity is constant.

A line of best fit is a straight line that best represents the data on a scatterplot. It is written in y = mx + c.

You need to know the formulas for simple and compound interest. Simple Interest: A = P(1 + rt) Compound Interest: A = P(1 + r/n) nt where A is the total amount, P is the principal, r is the interest rate, t is the time period and n is the number of times the interest is compounded per year.

Probability measures how likely an event is. The formula to calculate the probability of an event is: Probability = (number of favorable outcomes)/(total number of possible outcomes)

Question 1 Question 2 Question 3 Question 4 Question 5 Question 6 Question 7 Question 8 Question 9 Question 10 Question 11 Question 12 Question 13 Question 14 Question 15

Data and Statistics

You need to know that mean, median, and mode are measures of center for a data set, while range and standard deviation are measures of spread. You will not be asked to calculate the standard deviation of a set of data, but you do need to understand that a larger standard deviation means that the values are more spread out from the mean. You may be asked to compare the standard deviation of two data sets by approximating the spread from the mean.

You do not need to calculate the margins of error or confidence level, but you do need to know what these concepts are and how to interpret them in context. Take note that the questions in the SAT will always use 95% confidence levels. Some questions may give you the confidence level and ask you to find the value for which the interval applies. When the confidence level is kept the same, the size of the margin of error is affected by the standard deviation and the sample size. The larger the standard deviation, the larger the margin of error. The larger the sample size, the smaller the margin of error. The margin of error and confidence interval are estimates for the entire population and do not apply to values of individual objects in the populations.

The results of a sample can be generalized to the entire population only if the subjects in the sample are selected randomly. Conclusions about cause and effect can appropriately be drawn only if the subjects are randomly assigned to treatment.

Question 1 Question 2 Question 3 Question 4 Question 5 Question 6 Question 7 Question 8 Question 9

We welcome your feedback, comments and questions about this site or page. Please submit your feedback or enquiries via our Feedback page.

Integrated Approach
Testimonials
Business Process Reengineering
Career Development
Competency Development

Data Analytics

Innovation Strategy
Innovation Readiness Survey (IRS)
Innovative Leader Survey (ILS)
Exploring Innovation
Lean Six Sigma Methodology
Lean Six Sigma and Data Analytics
Strategic Workforce Planning
iiP – Investing in People
Innovation and Change
Innovation and Productivity
Lean Six Sigma
Lean Innovation Training (LIT)
Articles and Case Studies
Data Analytics for OD
Lean Six Sigma Nuggets
Leadership Competency Cards
Lean Innovation Tool Kit
Bridging Cards
Data Science für Einsteiger
Praxisbuch Lean Six Sigma
Data Analytics with R
Sign up for Newsletter

Introducing Data Analytics and Data Science into Your Organisation with Carefully Crafted Solutions.

“I only believe in statistics that I doctored myself.” Winston Churchill

Data analytics, or data analysis , is the process of screening, cleaning, transforming, and modeling data with the objective of discovering useful information, suggesting conclusions, and supporting problem solving as well as decision making. There are multiple approaches, including a variety of techniques and tools used for data analytics. Data analytics finds applications in many different environments. As such, it usually covers two steps, graphical analysis and statistical analysis. The selection of tools for a given data analytics task depends on the overall objective, the source and types of data given.

Above all, Data Analytics, as part of Data Science, marks the foundation of all disciplines that are part of Artificial Intelligence (AI).

Objectives of Data Analytics

The objective of the data analytics task can be to screen or inspect the data in order to find out whether the data fulfils certain requirements. These requirements can be a certain distribution, a certain homogeneity of the dataset (no outliers) or just the behaviour of the data under certain stratification conditions (using demographics).

More often than not, another objective would be the analysis of data, in particular survey data , to determine the reliability of the survey instrument used to the collect data. Cronbach’s Alpha is often applied to perform this task. Cronbach’s Alpha determines whether survey items (questions/statements) that belong to the same factor are really behaving in a similar way, i.e. showing the same characteristic as other items in that factor. Testing reliability of a survey instrument is a prerequisite for further analysis using the dataset in question.

Data Preparation Before Data Analysis

Often enough, data is not ready for analysis. This can be due to a data collection format that is not in sync with subsequent analysis tools. This can also be due to a distribution that makes it harder to analyse the data. Hence, reorganising , standardising or transforming (to normal distribution) the dataset might be necessary.

Data Analytics with Descriptive Statistics

Descriptive Statistics includes a set of tools that is used to quantitatively describe a set of data. It usually indicates central tendency, variability, minimum, maximum as well as distribution and deviation from this distribution ( kurtosis , skewness ). Descriptive statistics might also highlight potential outliers for further inspection and action.

Data Analytics with Predictive Statistics

In contrast to descriptive statistics characterising a certain given set of data, inferential statistics uses a subset of the population, a sample, to draw conclusions regarding the population. The inherent risk depends on the required confidence level, confidence interval and the sample size at hand as well as the variation in the data set. Hence, the test result indicates this risk.

Data Analytics with Factor Analysis

Factor Analysis helps determine clusters in datasets, i.e. it finds empirical variables that show a similar variability. These variables may therefore construct the same factor. A factor is a dependent, unobserved variable that includes multiple observed variables in the same cluster. Under certain circumstances, this can lead to a reduction of observed variables and hence the increase of sample size in the remaining unobserved variables (factors). So, both outcomes improve the power of subsequent statistical analysis of the data.

Factor analysis can use different approaches to pursue a multitude of objectives. Exploratory factor analysis (EFA) is used to identify complex interrelationships among items and determine clusters/factors whilst there is no predetermination of factors. Confirmatory factor analysis (CFA) is used to test the hypothesis that the items are associated with specific factors. In this case, factors are predetermined before the analysis.

Data Analytics For Problem Solving

Data analytics can be helpful in problem solving by establishing the significance of the relationship between problems (Y) and potential root causes (X). As a result, a large variety of tools is available. The selection of tools for a given data analytics task depends on the overall objective, the source and types of data. Discrete data, such as counts or attributes require different tools than continuous data, such as measurements. Whilst continuous data are transformable into discrete data for decision making, this process is irreversible.

Depending on the data in X and Y, regression analysis or hypothesis testing will be used to answer the question whether there is a relationship between problem and alleged root cause. These tools do not take away the decision, they rather tell the risk for a certain conclusion. The decision is still to be made by the process owner ( example ).

Analytics was never intended to replace intuition, but to supplement it instead. Stuart Farrand, Data Scientist at Pivotal Insight

Applications for data analytics are evident in all private and public organisations without limits. For example, already some decades ago, companies like Motorola and General Electric discovered the power of data analytics and made this the core of their Six Sigma movement. As a result, these organisations made sure, that problem solving is based on data and applied data analytics wherever appropriate. Nowadays, data analytics or data science is vital part of problem solving and most Lean Six Sigma projects. So, Six Sigma Black Belts are usually well-versed in this kind of data analysis and make good candidates for a Data Scientist career track.

To sum it up, we offer multiple training solutions as public and in-house courses. Please, check out our upcoming events .

Internet of Things for Starters

Managers need data analytics, automating a mess yields an automated mess, data analysis – plot the data, plot the data, plot the data, can we predict when our staff is leaving, leading digital-ready workforce, analytical storytelling, great, we have improved … or not, do you understand your survey results, making sense of the wilcoxon test, making sense of chi-squared test – finding differences in proportions, making sense of test for equal variances, make use of your survey data – kano them, making sense of the two-proportions test, making sense of linear regression.

Architect of High-Performing Organisations

+65 61000 263

[email protected]

Our Locations

Español (Latam)
Bahasa Indonesia
Português (Brasil)

Gemini 1.5: Our next-generation model, now available for Private Preview in Google AI Studio

Last week, we released Gemini 1.0 Ultra in Gemini Advanced. You can try it out now by signing up for a Gemini Advanced subscription . The 1.0 Ultra model, accessible via the Gemini API, has seen a lot of interest and continues to roll out to select developers and partners in Google AI Studio .

Today, we’re also excited to introduce our next-generation Gemini 1.5 model , which uses a new Mixture-of-Experts (MoE) approach to improve efficiency. It routes your request to a group of smaller "expert” neural networks so responses are faster and higher quality.

Developers can sign up for our Private Preview of Gemini 1.5 Pro , our mid-sized multimodal model optimized for scaling across a wide-range of tasks. The model features a new, experimental 1 million token context window, and will be available to try out in Google AI Studio . Google AI Studio is the fastest way to build with Gemini models and enables developers to easily integrate the Gemini API in their applications. It’s available in 38 languages across 180+ countries and territories .

1,000,000 tokens: Unlocking new use cases for developers

Before today, the largest context window in the world for a publicly available large language model was 200,000 tokens. We’ve been able to significantly increase this — running up to 1 million tokens consistently, achieving the longest context window of any large-scale foundation model. Gemini 1.5 Pro will come with a 128,000 token context window by default, but today’s Private Preview will have access to the experimental 1 million token context window.

We’re excited about the new possibilities that larger context windows enable. You can directly upload large PDFs, code repositories, or even lengthy videos as prompts in Google AI Studio. Gemini 1.5 Pro will then reason across modalities and output text.

1) Upload multiple files and ask questions We’ve added the ability for developers to upload multiple files, like PDFs, and ask questions in Google AI Studio. The larger context window allows the model to take in more information — making the output more consistent, relevant and useful. With this 1 million token context window, we’ve been able to load in over 700,000 words of text in one go.

Sorry, your browser doesn't support playback for this video

2) Query an entire code repository

The large context window also enables a deep analysis of an entire codebase, helping Gemini models grasp complex relationships, patterns, and understanding of code. A developer could upload a new codebase directly from their computer or via Google Drive, and use the model to onboard quickly and gain an understanding of the code.

3) Add a full length video

Gemini 1.5 Pro can also reason across up to 1 hour of video. When you attach a video, Google AI Studio breaks it down into thousands of frames (without audio), and then you can perform highly sophisticated reasoning and problem-solving tasks since the Gemini models are multimodal.

More ways for developers to build with Gemini models

In addition to bringing you the latest model innovations, we’re also making it easier for you to build with Gemini:

Easy tuning. Provide a set of examples, and you can customize Gemini for your specific needs in minutes from inside Google AI Studio. This feature rolls out in the next few days.
New developer surfaces . Integrate the Gemini API to build new AI-powered features today with new Firebase Extensions , across your development workspace in Project IDX , or with our newly released Google AI Dart SDK .
Lower pricing for Gemini 1.0 Pro . We’re also updating the 1.0 Pro model, which offers a good balance of cost and performance for many AI tasks. Today’s stable version is priced 50% less for text inputs and 25% less for outputs than previously announced. The upcoming pay-as-you-go plans for AI Studio are coming soon.

Since December, developers of all sizes have been building with Gemini models, and we’re excited to turn cutting edge research into early developer products in Google AI Studio . Expect some latency in this preview version due to the experimental nature of the large context window feature, but we’re excited to start a phased rollout as we continue to fine-tune the model and get your feedback. We hope you enjoy experimenting with it early on, like we have.

Get ready for Google I/O: Program lineup revealed

Gemini 1.5 Pro Now Available in 180+ Countries; with Native Audio Understanding, System Instructions, JSON Mode and more

Publish your Keras models on Kaggle and Hugging Face

Achieving privacy compliance with your CI/CD: A guide for compliance teams

Gemma Family Expands with Models Tailored for Developers and Researchers

TechRepublic

Male system administrator of big data center typing on laptop computer while working in server room. Programming digital operation. Man engineer working online in database center. Telecommunication.

8 Best Data Science Tools and Software

Apache Spark and Hadoop, Microsoft Power BI, Jupyter Notebook and Alteryx are among the top data science tools for finding business insights. Compare their features, pros and cons.

EU’s AI Act: Europe’s New Rules for Artificial Intelligence

Europe's AI legislation, adopted March 13, attempts to strike a tricky balance between promoting innovation and protecting citizens' rights.

Concept image of a woman analyzing data.

10 Best Predictive Analytics Tools and Software for 2024

Tableau, TIBCO Data Science, IBM and Sisense are among the best software for predictive analytics. Explore their features, pricing, pros and cons to find the best option for your organization.

Tableau Review: Features, Pricing, Pros and Cons

Tableau has three pricing tiers that cater to all kinds of data teams, with capabilities like accelerators and real-time analytics. And if Tableau doesn’t meet your needs, it has a few alternatives worth noting.

Futuristic concept art for big data solution for enterprises.

Latest Articles

Cubes, dice or blocks with deep fake letters.

Combatting Deepfakes in Australia: Content Credentials is the Start

The production of deepfakes is accelerating at more than 1,500% in Australia, forcing organisations to create and adopt standards like Content Credentials.

The Top 5 Pipedrive Alternatives for 2024

Discover the top alternatives to Pipedrive. Explore a curated list of CRM platforms with similar features, pricing and pros and cons to find the best fit for your business.

Technology background with national flag of Australia.

The Australian Government’s Manufacturing Objectives Rely on IT Capabilities

The intent of the Future Made in Australia Act is to build manufacturing capabilities across all sectors, which will likely lead to more demand for IT skills and services.

Businessman add new skill or gear into human head to upgrade working skill.

Udemy Report: Which IT Skills Are Most in Demand in Q1 2024?

Informatica PowerCenter, Microsoft Playwright and Oracle Database SQL top Udemy’s list of most popular tech courses.

Gartner: 4 Bleeding-Edge Technologies in Australia

Gartner recently identified emerging tech that will impact enterprise leaders in APAC. Here’s what IT leaders in Australia need to know about these innovative technologies.

Llama 3 Cheat Sheet: A Complete Guide for 2024

Learn how to access Meta’s new AI model Llama 3, which sets itself apart by being open to use under a license agreement.

Zoho vs Salesforce (2024): Which CRM Is Better?

Look at Zoho CRM and Salesforce side-by-side to compare the cost per functionality and top pros and of each provider to determine which is better for your business needs.

Businessman hand holding glowing digital brain.

9 Innovative Use Cases of AI in Australian Businesses in 2024

Australian businesses are beginning to effectively grapple with AI and build solutions specific to their needs. Here are notable use cases of businesses using AI.

An illustration of a monthly salary of a happy employee on year 2024.

How Are APAC Tech Salaries Faring in 2024?

The year 2024 is bringing a return to stable tech salary growth in APAC, with AI and data jobs leading the way. This follows downward salary pressure in 2023, after steep increases in previous years.

Anthropic Releases Claude Team Enterprise AI Plan and iOS App

The enterprise plan seeks to fill a need for generative AI tools for small and medium businesses. Plus, a Claude app is now on iOS.

Top Tech Conferences & Events to Add to Your Calendar in 2024

A great way to stay current with the latest technology trends and innovations is by attending conferences. Read and bookmark our 2024 tech events guide.

TechRepublic Premium Editorial Calendar: Policies, Checklists, Hiring Kits and Glossaries for Download

TechRepublic Premium content helps you solve your toughest IT issues and jump-start your career or next project.

IBM Acquires HashiCorp for $6.4 Billion, Expanding Hybrid Cloud Offerings

The deal is intended to strengthen IBM’s hybrid and multicloud offerings and generative AI deployment.

Customer Relationship Management concept art on a laptop screen.

6 Best Enterprise CRM Software for 2024

Freshsales, Zoho CRM and Pipedrive are among the top enterprise CRM software that organize and automate data workflows to help achieve businesses’ client management goals in 2024.

8 Best Free Alternatives to Microsoft Excel

Discover the best free alternatives to Microsoft Excel: powerful, feature-packed solutions that help you work smarter and faster by allowing you to create comprehensive spreadsheets and analyze data.

Create a TechRepublic Account

Get the web's best business technology news, tutorials, reviews, trends, and analysis—in your inbox. Let's start with the basics.

* - indicates required fields

Sign in to TechRepublic

Lost your password? Request a new password

Reset Password

Please enter your email adress. You will receive an email message with instructions on how to reset your password.

Check your email for a password reset link. If you didn't receive an email don't forgot to check your spam folder, otherwise contact support .

Welcome. Tell us a little bit about you.

This will help us provide you with customized content.

Want to receive more TechRepublic news?

You're all set.

Thanks for signing up! Keep an eye out for a confirmation email from our team. To ensure any newsletters you subscribed to hit your inbox, make sure to add [email protected] to your contacts list.

Frontiers in Remote Sensing
Data Fusion and Assimilation
Research Topics

Advanced Geospatial Data Analytics for Environmental Sustainability: Current Practices and Future Prospects

Total Downloads

Total Views and Downloads

About this Research Topic

Environmental sustainability refers to the conservation and management of natural resources to match the needs of present without negotiating the ability of coming generations to meet theirs. It aims to balance ecological, social, and economic goals with assurance of equitable access to the resources. Over the last few decades, sustainability is concerned with the man environmental interface, the complex boundary where bio-physical and socio-cultural systems interact. It accumulated tremendous knowledge about the surrounding earth surface features and natural phenomena. It is crucial to pursuit the advancements and innovations in the dynamic field of earth and environmental monitoring. It is therefore important to analyze and harness the advanced technologies in achieving environmental sustainability at different observational scales. Geospatial big data and artificial intelligence (AI) provide new and great opportunities to solve the problems associated with environmental sustainability with advanced analytics. It can support real-time spatial analysis and visualization of data in the form of maps. Recent progresses in remote sensing technologies have led us to the time of spatially explicit big Earth data and Artificial Intelligence. The launch of various satellites on board sensors opened a new era in earth observation techniques for environmental studies. Various spaceborne and airborne multispectral sensors, UAV based sensors, and even IoT and social media data also contribute considerably to geospatial big data collection. The last few decades have witnessed an explosion in the amount, collection, and complexity of spatial environmental data. Complex data from multiple sources can be integrated to provide a more comprehensive scenario and to address real-world environmental and sustainability problems. Moreover, the cloud-based computing platforms equipped with AI are now available for solving any big and complex computational problems. Apart from this widespread employment of machines and deep are gaining a lot of interest to handle massive availability of geospatial big data. Advanced modelling tools coupled with Geospatial information meet the requirement for accurate and timely study in environmental monitoring, risk assessment, and planned decision formulation for sustainable development. It supports research across different fields, providing new views on the interconnection of air, water, soil, food, and energy for a resilient society and a sustainable future. Big Earth data coupled with remote sensing and geospatial technologies have become increasingly valuable for environmental studies. State-of-the-art Geospatial data analytics have the potential to develop and use analytical and rigorous computer-based data-science methods to evaluate and manage the Earth’s natural resources to accomplish a sustainable society. In this topical collection, we welcome contributions focusing on geospatial data analytics in Earth observation, environmental monitoring, and management. It aims to foster the comprehensive understanding of timely environmental phenomena and guide us toward sustainable development practices. The submission covers, but is not limited to, the themes given below: • Artificial Intelligence and machine learning; • Big Geospatial data applications; • Cloud computing platforms for Geoenvironmental visualization; • Earth observation and environmental monitoring; • Environmental sustainability; • Geospatial data modelling and fusion; • Multiscale data integration; • Natural hazards and disasters; • Remote sensing and GIS for natural resources.

Keywords : machine learning, artificial intelligence, big geospatial data, geospatial data modelling, multiscale data integration, remote sensing, gis

Important Note : All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Topic Editors

Topic coordinators, submission deadlines, participating journals.

Manuscripts can be submitted to this Research Topic via the following journals:

total views

Demographics

No records found

total views article views downloads topic views

Top countries

Top referring sites, about frontiers research topics.

With their unique mixes of varied contributions from Original Research to Review Articles, Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author.

IMAGES

Problem solving and data analysis concept Vector Image
Problem solving infographic 10 steps concept Vector Image
Problem Solving with Data Analytics Course
Problem Solving with Data Analytics
Here Are The Skills You Need To Work With Big Data
The 5 Stages of Your Data Analytics Journey

VIDEO

How To Develop Analytical & Problem Solving Skills ?
Data Analytics Challenges
Game Play Analysis IV / LeetCode SQL / PANDAS Python tutorial / Data analysis in python
15 December 2023
Applied Prescriptive Analytics At Mu Sigma
Math -Level 3

COMMENTS

Problem-Solving Skills for Data Analysts: A Guide
problem solving is a important role is data analysis because during analysis the project there some of the issues occur like a null values,outlier,etc.some of the step involved like data ...
The Art of Solving Any Data Science Problem
A framework like First-Principle Thinking and Feynman's Technique helps in better understanding the problem we are trying to solve. Data Exploration: Exploratory data analysis is all about asking relevant questions and making sure all the possibilities are considered while solving a problem. Some of the mental models that could be adopted ...
The Essential Data Analyst Skills You'll Need [2024 Guide]
Expertise in data visualization. Great communication skills. Key takeways. 1. Excellent problem-solving skills. Problem solving is one of the most important data analyst skills you should possess. Around 90% of analytics is about critical thinking, and knowing the right questions to ask.
Solve Any Data Analysis Problem
In Solve Any Data Analysis Problem you'll learn how to shift the way you think about data from the structured clean problems you get in a classroom, book, or bootcamp to the messy open-ended challenges of the workplace. As you work through eight problems you'll see over and over on the job, you'll discover a solutions-driven methodology ...
How to analyze a problem
Before jumping in, it's crucial to plan the analysis, decide which analytical tools to use, and ensure rigor. Check out these insights to uncover ways data can take your problem-solving techniques to the next level, and stay tuned for an upcoming post on the potential power of generative AI in problem-solving. The data-driven enterprise of 2025.
Solving data problems: A beginner's guide
Break down problems into small steps. One of the essential strategies for problem-solving is to break down the problem into the smallest steps possible — atomic steps. Try to describe every single step. Don't write any code or start your search for the magic formula. Make notes in plain language.
Problem Solving with Data Analytics
How can you ask questions that lead to helpful insights in order to solve business problems? In this video, Learn about the six basic problem types to craft ...
The Importance of Data Analysis in Problem Solving
These problems will be because of various reasons — businesses, the environment, the stakeholders, and sometimes purely due to people's psychology. To solve those problems, data analysis is very important. Data crunching, business analysis and finding unique insights is a very essential part of management analysis and decision making.
Problem Solving Skills for Data Analysts: How to Identify ...
Powered by AI and the LinkedIn community. 1. Define the problem. 2. Explore the data. 3. Apply analytical techniques. 4. Interpret and communicate results.
5 Reasons Why Data Analytics Is Important In Problem Solving
Now that we've established a general idea of how strongly connected analytical skills and problem-solving are, let's dig deeper into the top 5 reasons why data analytics is important in problem-solving. 1. Uncover Hidden Details. Data analytics is great at putting the minor details out in the spotlight.
The "problem-solver" approach to data preparation
A problem-solver approach to data preparation for analytics lets the analyst decide what information needs to be integrated into the analysis platform, what transformations are to be done, and how the data is to be used. This approach differs from the conventional extract/transform/load cycle in three key ways:
Medium: Problem solving and data analysis
Unit test. Level up on all the skills in this unit and collect up to 1,000 Mastery points! This unit tackles the medium-difficulty problem solving and data analysis questions on the SAT Math test. Work through each skill, taking quizzes and the unit test to level up your mastery progress.
Chapter 1 Problem Solving with Data
1.1 Introduction. This chapter will introduce you to a general approach to solving problems and answering questions using data. Throughout the rest of the module, we will reference back to this chapter as you work your way through your own data analysis exercises. The approach is applicable to actuaries, data scientists, general data analysts ...
Problem-Solving for Data Analysts
PACE is a framework developed with input and feedback from our team of data professionals. The intent of PACE is to provide an initial structure that will he...
7 Common Data Analytics Problems
In a nutshell: Data analysts often face issues with limited value of historical insights and unused insights. Data goes unused due to limited capacity to process and analyze it. Bias is unavoidable in traditional predictive modeling. Long time to value and data-security concerns are common problems. Predictive analytics platforms can overcome ...
20 Data Analytics Projects for All Levels
For more advanced data analytics projects, you need command over mathematics, probability, and statistics. ... Investigating Netflix Movies and Guest Stars in The Office project, you will use data manipulation and visualization to solve a real-world data science problem. You will perform deep exploratory data analysis and draw conclusions from ...
Advanced: Problem solving and data analysis
Unit test. Level up on all the skills in this unit and collect up to 1,000 Mastery points! Ready for a challenge? This unit covers the hardest problem solving and data analysis questions on the SAT Math test. Work through each skill, taking quizzes and the unit test to level up your mastery progress.
19 Data Analysis Questions Examples For Efficient Analytics
Asking the right data analysis questions is crucial to get accurate, actionable insights from your business analytics efforts. See examples here! ... documents, personal knowledge, and business models will provide a solid foundation for solving business problems. That's why we've prepared this list of data analysis questions examples ...
Learn how to SOLVE a data analytics case study problem
Today I'm tackling a data analytics problem asked in data engineering and data science interviews. For most of these questions, there are multiple solutions ...
3 business problems data analytics can help solve
Each year, the MIT Sloan Master of Business Analytics Capstone Project partners students with companies that are looking to solve a business problem with data analytics. The program offers unique and up-close insight into what companies were grappling with at the beginning of 2023. This year, students worked on 41 different projects with 33 ...
Problem Solving and Data Analysis (Examples, solutions)
The questions in Problem Solving and Data Analysis focus on linear, quadratic and exponential relationships which may be represented by charts, graphs or tables. A model is linear if the difference in quantity is constant. A model is exponential if the ratio in the quantity is constant. A line of best fit is a straight line that best represents ...
Real-World Problems, and How Data Helps Us Solve Them
Exploring Time-to-Event with Survival Analysis In an accessible introduction to survival analysis, Olivia Tanuwidjaja covers some of the essential concepts and techniques it relies on, and shows how this approach can be applied across a diverse spectrum of problems—from the medical field to maintenance prediction and customer analytics.
Data Analytics for Problem Solving and Decision Making
Data analytics, or data analysis, is the process of screening, cleaning, transforming, and modeling data with the objective of discovering useful information, suggesting conclusions, and supporting problem solving as well as decision making.There are multiple approaches, including a variety of techniques and tools used for data analytics. Data analytics finds applications in many different ...
Harness Data Analysis for Agribusiness Problem Solving
By data analysis, it helps in solving problems, make smarter decisions, and ultimately grow a more successful and sustainable business! …see more. Like. Like. Celebrate. Support. Love.
How to Become a Data Scientist in 2022?
Data scientists are driving organisations to be become data-driven and empower decision-makers with a scientific approach to solving business problems. The communication also helps data scientists to explain the internal working of their algorithm to help the non-technical audience understand the impact of their solution.
Pair Programming Benefits for Data Science Teams
The benefits outlined, including enhanced problem-solving, knowledge sharing, continuous code review, reduced errors, increased productivity, improved team communication, adaptability to agile methodologies, and a streamlined onboarding process, collectively contribute to a more efficient and cohesive data science team.
Gemini 1.5: Our next-generation model, now available for Private
Last week, we released Gemini 1.0 Ultra in Gemini Advanced. You can try it out now by signing up for a Gemini Advanced subscription.The 1.0 Ultra model, accessible via the Gemini API, has seen a lot of interest and continues to roll out to select developers and partners in Google AI Studio.. Today, we're also excited to introduce our next-generation Gemini 1.5 model, which uses a new Mixture ...
Big Data: Latest Articles, News & Trends
Big Data Big Data Tableau Review: Features, Pricing, Pros and Cons . Tableau has three pricing tiers that cater to all kinds of data teams, with capabilities like accelerators and real-time analytics.
Advanced Geospatial Data Analytics for Environmental ...
Geospatial big data and artificial intelligence (AI) provide new and great opportunities to solve the problems associated with environmental sustainability with advanced analytics. It can support real-time spatial analysis and visualization of data in the form of maps.Recent progresses in remote sensing technologies have led us to the time of ...