exploratory descriptive analytical and predictive research

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Chapter 3: Developing a Research Question

3.2 Exploration, Description, Explanation

As you can see, there is much to think about and many decisions to be made as you begin to define your research question and your research project. Something else you will need to consider in the early stages is whether your research will be exploratory, descriptive, or explanatory. Each of these types of research has a different aim or purpose, consequently, how you design your research project will be determined in part by this decision. In the following paragraphs we will look at these three types of research.

Exploratory research

Researchers conducting exploratory research are typically at the early stages of examining their topics. These sorts of projects are usually conducted when a researcher wants to test the feasibility of conducting a more extensive study; he or she wants to figure out the lay of the land with respect to the particular topic. Perhaps very little prior research has been conducted on this subject. If this is the case, a researcher may wish to do some exploratory work to learn what method to use in collecting data, how best to approach research participants, or even what sorts of questions are reasonable to ask. A researcher wanting to simply satisfy his or her own curiosity about a topic could also conduct exploratory research. Conducting exploratory research on a topic is often a necessary first step, both to satisfy researcher curiosity about the subject and to better understand the phenomenon and the research participants in order to design a larger, subsequent study. See Table 2.1 for examples.

Descriptive research

Sometimes the goal of research is to describe or define a particular phenomenon. In this case, descriptive research would be an appropriate strategy. A descriptive may, for example, aim to describe a pattern. For example, researchers often collect information to describe something for the benefit of the general public. Market researchers rely on descriptive research to tell them what consumers think of their products. In fact, descriptive research has many useful applications, and you probably rely on findings from descriptive research without even being aware that that is what you are doing. See Table 3.1 for examples.

Explanatory research

The third type of research, explanatory research, seeks to answer “why” questions. In this case, the researcher is trying to identify the causes and effects of whatever phenomenon is being studied. An explanatory study of college students’ addictions to their electronic gadgets, for example, might aim to understand why students become addicted. Does it have anything to do with their family histories? Does it have anything to do with their other extracurricular hobbies and activities? Does it have anything to do with the people with whom they spend their time? An explanatory study could answer these kinds of questions. See Table 3.1 for examples.

Table 3.1 Exploratory, descriptive and explanatory research differences (Adapted from Adjei, n.d.).

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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8 Types of Data Analysis

Data analysis is an aspect of  data science and data analytics that is all about analyzing data for different kinds of purposes. The data analysis process involves inspecting, cleaning, transforming and modeling data to draw useful insights from it.

What Are the Different Types of Data Analysis?

• Descriptive analysis
• Diagnostic analysis
• Exploratory analysis
• Inferential analysis
• Predictive analysis
• Causal analysis
• Mechanistic analysis
• Prescriptive analysis

With its multiple facets, methodologies and techniques, data analysis is used in a variety of fields, including business, science and social science, among others. As businesses thrive under the influence of technological advancements in data analytics, data analysis plays a huge role in  decision-making , providing a better, faster and more efficacious system that minimizes risks and reduces  human biases .

That said, there are different kinds of data analysis catered with different goals. We’ll examine each one below.

Two Camps of Data Analysis

Data analysis can be divided into two camps, according to the book  R for Data Science :

• Hypothesis Generation — This involves looking deeply at the data and combining your domain knowledge to generate hypotheses about why the data behaves the way it does.
• Hypothesis Confirmation — This involves using a precise mathematical model to generate falsifiable predictions with statistical sophistication to confirm your prior hypotheses.

Types of Data Analysis

Data analysis can be separated and organized into types, arranged in an increasing order of complexity.

1. Descriptive Analysis

The goal of descriptive analysis is to describe or summarize a set of data. Here’s what you need to know:

• Descriptive analysis is the very first analysis performed in the data analysis process.
• It generates simple summaries about samples and measurements.
• It involves common, descriptive statistics like measures of central tendency, variability, frequency and position.

Descriptive Analysis Example

Take the  Covid-19 statistics page on Google, for example. The line graph is a pure summary of the cases/deaths, a presentation and description of the population of a particular country infected by the virus.

Descriptive analysis is the first step in analysis where you summarize and describe the data you have using descriptive statistics, and the result is a simple presentation of your data.

More on Data Analysis: Data Analyst vs. Data Scientist: Similarities and Differences Explained

2. Diagnostic Analysis 

Diagnostic analysis seeks to answer the question “Why did this happen?” by taking a more in-depth look at data to uncover subtle patterns. Here’s what you need to know:

• Diagnostic analysis typically comes after descriptive analysis, taking initial findings and investigating why certain patterns in data happen. 
• Diagnostic analysis may involve analyzing other related data sources, including past data, to reveal more insights into current data trends.  
• Diagnostic analysis is ideal for further exploring patterns in data to explain anomalies.  

Diagnostic Analysis Example

A footwear store wants to review its website traffic levels over the previous 12 months. Upon compiling and assessing the data, the company’s marketing team finds that June experienced above-average levels of traffic while July and August witnessed slightly lower levels of traffic. 

To find out why this difference occurred, the marketing team takes a deeper look. Team members break down the data to focus on specific categories of footwear. In the month of June, they discovered that pages featuring sandals and other beach-related footwear received a high number of views while these numbers dropped in July and August. 

Marketers may also review other factors like seasonal changes and company sales events to see if other variables could have contributed to this trend.   

3. Exploratory Analysis (EDA)

Exploratory analysis involves examining or exploring data and finding relationships between variables that were previously unknown. Here’s what you need to know:

• EDA helps you discover relationships between measures in your data, which are not evidence for the existence of the correlation, as denoted by the phrase, “ Correlation doesn’t imply causation .”
• It’s useful for discovering new connections and forming hypotheses. It drives design planning and data collection.

Exploratory Analysis Example

Climate change is an increasingly important topic as the global temperature has gradually risen over the years. One example of an exploratory data analysis on climate change involves taking the rise in temperature over the years from 1950 to 2020 and the increase of human activities and industrialization to find relationships from the data. For example, you may increase the number of factories, cars on the road and airplane flights to see how that correlates with the rise in temperature.

Exploratory analysis explores data to find relationships between measures without identifying the cause. It’s most useful when formulating hypotheses.

4. Inferential Analysis

Inferential analysis involves using a small sample of data to infer information about a larger population of data.

The goal of statistical modeling itself is all about using a small amount of information to extrapolate and generalize information to a larger group. Here’s what you need to know:

• Inferential analysis involves using estimated data that is representative of a population and gives a measure of uncertainty or standard deviation to your estimation.
• The  accuracy of inference depends heavily on your sampling scheme. If the sample isn’t representative of the population, the generalization will be inaccurate. This is known as the  central limit theorem .

Inferential Analysis Example

The idea of drawing an inference about the population at large with a smaller sample size is intuitive. Many statistics you see on the media and the internet are inferential; a prediction of an event based on a small sample. For example, a psychological study on the benefits of sleep might have a total of 500 people involved. When they followed up with the candidates, the candidates reported to have better overall attention spans and well-being with seven-to-nine hours of sleep, while those with less sleep and more sleep than the given range suffered from reduced attention spans and energy. This study drawn from 500 people was just a tiny portion of the 7 billion people in the world, and is thus an inference of the larger population.

Inferential analysis extrapolates and generalizes the information of the larger group with a smaller sample to generate analysis and predictions.

5. Predictive Analysis

Predictive analysis involves using historical or current data to find patterns and make predictions about the future. Here’s what you need to know:

• The accuracy of the predictions depends on the input variables.
• Accuracy also depends on the types of models. A linear model might work well in some cases, and in other cases it might not.
• Using a variable to predict another one doesn’t denote a causal relationship.

Predictive Analysis Example

The 2020 US election is a popular topic and many  prediction models are built to predict the winning candidate. FiveThirtyEight did this to forecast the 2016 and 2020 elections. Prediction analysis for an election would require input variables such as historical polling data, trends and current polling data in order to return a good prediction. Something as large as an election wouldn’t just be using a linear model, but a complex model with certain tunings to best serve its purpose.

Predictive analysis takes data from the past and present to make predictions about the future.

More on Data: Explaining the Empirical for Normal Distribution

6. Causal Analysis

Causal analysis looks at the cause and effect of relationships between variables and is focused on finding the cause of a correlation. Here’s what you need to know:

• To find the cause, you have to question whether the observed correlations driving your conclusion are valid. Just looking at the surface data won’t help you discover the hidden mechanisms underlying the correlations.
• Causal analysis is applied in randomized studies focused on identifying causation.
• Causal analysis is the gold standard in data analysis and scientific studies where the cause of phenomenon is to be extracted and singled out, like separating wheat from chaff.
• Good data is hard to find and requires expensive research and studies. These studies are analyzed in aggregate (multiple groups), and the observed relationships are just average effects (mean) of the whole population. This means the results might not apply to everyone.

Causal Analysis Example  

Say you want to test out whether a new drug improves human strength and focus. To do that, you perform randomized control trials for the drug to test its effect. You compare the sample of candidates for your new drug against the candidates receiving a mock control drug through a few tests focused on strength and overall focus and attention. This will allow you to observe how the drug affects the outcome.

Causal analysis is about finding out the causal relationship between variables, and examining how a change in one variable affects another.

7. Mechanistic Analysis

Mechanistic analysis is used to understand exact changes in variables that lead to other changes in other variables. Here’s what you need to know:

• It’s applied in physical or engineering sciences, situations that require high precision and little room for error, only noise in data is measurement error.
• It’s designed to understand a biological or behavioral process, the pathophysiology of a disease or the mechanism of action of an intervention. 

Mechanistic Analysis Example

Many graduate-level research and complex topics are suitable examples, but to put it in simple terms, let’s say an experiment is done to simulate safe and effective nuclear fusion to power the world. A mechanistic analysis of the study would entail a precise balance of controlling and manipulating variables with highly accurate measures of both variables and the desired outcomes. It’s this intricate and meticulous modus operandi toward these big topics that allows for scientific breakthroughs and advancement of society.

Mechanistic analysis is in some ways a predictive analysis, but modified to tackle studies that require high precision and meticulous methodologies for physical or engineering science .

8. Prescriptive Analysis 

Prescriptive analysis compiles insights from other previous data analyses and determines actions that teams or companies can take to prepare for predicted trends. Here’s what you need to know: 

• Prescriptive analysis may come right after predictive analysis, but it may involve combining many different data analyses. 
• Companies need advanced technology and plenty of resources to conduct prescriptive analysis. AI systems that process data and adjust automated tasks are an example of the technology required to perform prescriptive analysis.  

Prescriptive Analysis Example

Prescriptive analysis is pervasive in everyday life, driving the curated content users consume on social media. On platforms like TikTok and Instagram, algorithms can apply prescriptive analysis to review past content a user has engaged with and the kinds of behaviors they exhibited with specific posts. Based on these factors, an algorithm seeks out similar content that is likely to elicit the same response and recommends it on a user’s personal feed. 

When to Use the Different Types of Data Analysis 

• Descriptive analysis summarizes the data at hand and presents your data in a comprehensible way.
• Diagnostic analysis takes a more detailed look at data to reveal why certain patterns occur, making it a good method for explaining anomalies. 
• Exploratory data analysis helps you discover correlations and relationships between variables in your data.
• Inferential analysis is for generalizing the larger population with a smaller sample size of data.
• Predictive analysis helps you make predictions about the future with data.
• Causal analysis emphasizes finding the cause of a correlation between variables.
• Mechanistic analysis is for measuring the exact changes in variables that lead to other changes in other variables.
• Prescriptive analysis combines insights from different data analyses to develop a course of action teams and companies can take to capitalize on predicted outcomes. 

A few important tips to remember about data analysis include:

• Correlation doesn’t imply causation.
• EDA helps discover new connections and form hypotheses.
• Accuracy of inference depends on the sampling scheme.
• A good prediction depends on the right input variables.
• A simple linear model with enough data usually does the trick.
• Using a variable to predict another doesn’t denote causal relationships.
• Good data is hard to find, and to produce it requires expensive research.
• Results from studies are done in aggregate and are average effects and might not apply to everyone.​

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Exploratory data analysis: frequencies, descriptive statistics, histograms, and boxplots.

Jacob Shreffler ; Martin R. Huecker .

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Last Update: November 3, 2023 .

• Definition/Introduction

Researchers must utilize exploratory data techniques to present findings to a target audience and create appropriate graphs and figures. Researchers can determine if outliers exist, data are missing, and statistical assumptions will be upheld by understanding data. Additionally, it is essential to comprehend these data when describing them in conclusions of a paper, in a meeting with colleagues invested in the findings, or while reading others’ work.

• Issues of Concern

This comprehension begins with exploring these data through the outputs discussed in this article. Individuals who do not conduct research must still comprehend new studies, and knowledge of fundamentals in analyzing data and interpretation of histograms and boxplots facilitates the ability to appraise recent publications accurately. Without this familiarity, decisions could be implemented based on inaccurate delivery or interpretation of medical studies.

Frequencies and Descriptive Statistics

Effective presentation of study results, in presentation or manuscript form, typically starts with frequencies and descriptive statistics (ie, mean, medians, standard deviations). One can get a better sense of the variables by examining these data to determine whether a balanced and sufficient research design exists. Frequencies also inform on missing data and give a sense of outliers (will be discussed below).

Luckily, software programs are available to conduct exploratory data analysis. For this chapter, we will be examining the following research question.

RQ: Are there differences in drug life (length of effect) for Drug 23 based on the administration site?

A more precise hypothesis could be: Is drug 23 longer-lasting when administered via site A compared to site B?

To address this research question, exploratory data analysis is conducted. First, it is essential to start with the frequencies of the variables. To keep things simple, only variables of minutes (drug life effect) and administration site (A vs B) are included. See Image. Figure 1 for outputs for frequencies.

Figure 1 shows that the administration site appears to be a balanced design with 50 individuals in each group. The excerpt for minutes frequencies is the bottom portion of Figure 1 and shows how many cases fell into each time frame with the cumulative percent on the right-hand side. In examining Figure 1, one suspiciously low measurement (135) was observed, considering time variables. If a data point seems inaccurate, a researcher should find this case and confirm if this was an entry error. For the sake of this review, the authors state that this was an entry error and should have been entered 535 and not 135. Had the analysis occurred without checking this, the data analysis, results, and conclusions would have been invalid. When finding any entry errors and determining how groups are balanced, potential missing data is explored. If not responsibly evaluated, missing values can nullify results.  

After replacing the incorrect 135 with 535, descriptive statistics, including the mean, median, mode, minimum/maximum scores, and standard deviation were examined. Output for the research example for the variable of minutes can be seen in Figure 2. Observe each variable to ensure that the mean seems reasonable and that the minimum and maximum are within an appropriate range based on medical competence or an available codebook. One assumption common in statistical analyses is a normal distribution. Image . Figure 2 shows that the mode differs from the mean and the median. We have visualization tools such as histograms to examine these scores for normality and outliers before making decisions.

Histograms are useful in assessing normality, as many statistical tests (eg, ANOVA and regression) assume the data have a normal distribution. When data deviate from a normal distribution, it is quantified using skewness and kurtosis. [1]  Skewness occurs when one tail of the curve is longer. If the tail is lengthier on the left side of the curve (more cases on the higher values), this would be negatively skewed, whereas if the tail is longer on the right side, it would be positively skewed. Kurtosis is another facet of normality. Positive kurtosis occurs when the center has many values falling in the middle, whereas negative kurtosis occurs when there are very heavy tails. [2]

Additionally, histograms reveal outliers: data points either entered incorrectly or truly very different from the rest of the sample. When there are outliers, one must determine accuracy based on random chance or the error in the experiment and provide strong justification if the decision is to exclude them. [3]  Outliers require attention to ensure the data analysis accurately reflects the majority of the data and is not influenced by extreme values; cleaning these outliers can result in better quality decision-making in clinical practice. [4]  A common approach to determining if a variable is approximately normally distributed is converting values to z scores and determining if any scores are less than -3 or greater than 3. For a normal distribution, about 99% of scores should lie within three standard deviations of the mean. [5]  Importantly, one should not automatically throw out any values outside of this range but consider it in corroboration with the other factors aforementioned. Outliers are relatively common, so when these are prevalent, one must assess the risks and benefits of exclusion. [6]

Image . Figure 3 provides examples of histograms. In Figure 3A, 2 possible outliers causing kurtosis are observed. If values within 3 standard deviations are used, the result in Figure 3B are observed. This histogram appears much closer to an approximately normal distribution with the kurtosis being treated. Remember, all evidence should be considered before eliminating outliers. When reporting outliers in scientific paper outputs, account for the number of outliers excluded and justify why they were excluded.

Boxplots can examine for outliers, assess the range of data, and show differences among groups. Boxplots provide a visual representation of ranges and medians, illustrating differences amongst groups, and are useful in various outlets, including evidence-based medicine. [7]  Boxplots provide a picture of data distribution when there are numerous values, and all values cannot be displayed (ie, a scatterplot). [8]  Figure 4 illustrates the differences between drug site administration and the length of drug life from the above example.

Image . Figure 4 shows differences with potential clinical impact. Had any outliers existed (data from the histogram were cleaned), they would appear outside the line endpoint. The red boxes represent the middle 50% of scores. The lines within each red box represent the median number of minutes within each administration site. The horizontal lines at the top and bottom of each line connected to the red box represent the 25th and 75th percentiles. In examining the difference boxplots, an overlap in minutes between 2 administration sites were observed: the approximate top 25 percent from site B had the same time noted as the bottom 25 percent at site A. Site B had a median minute amount under 525, whereas administration site A had a length greater than 550. If there were no differences in adverse reactions at site A, analysis of this figure provides evidence that healthcare providers should administer the drug via site A. Researchers could follow by testing a third administration site, site C. Image . Figure 5 shows what would happen if site C led to a longer drug life compared to site A.

Figure 5 displays the same site A data as Figure 4, but something looks different. The significant variance at site C makes site A’s variance appear smaller. In order words, patients who were administered the drug via site C had a larger range of scores. Thus, some patients experience a longer half-life when the drug is administered via site C than the median of site A; however, the broad range (lack of accuracy) and lower median should be the focus. The precision of minutes is much more compacted in site A. Therefore, the median is higher, and the range is more precise. One may conclude that this makes site A a more desirable site.

• Clinical Significance

Ultimately, by understanding basic exploratory data methods, medical researchers and consumers of research can make quality and data-informed decisions. These data-informed decisions will result in the ability to appraise the clinical significance of research outputs. By overlooking these fundamentals in statistics, critical errors in judgment can occur.

• Nursing, Allied Health, and Interprofessional Team Interventions

All interprofessional healthcare team members need to be at least familiar with, if not well-versed in, these statistical analyses so they can read and interpret study data and apply the data implications in their everyday practice. This approach allows all practitioners to remain abreast of the latest developments and provides valuable data for evidence-based medicine, ultimately leading to improved patient outcomes.

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Exploratory Data Analysis Figure 1 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Exploratory Data Analysis Figure 2 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Exploratory Data Analysis Figure 3 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Exploratory Data Analysis Figure 4 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Exploratory Data Analysis Figure 5 Contributed by Martin Huecker, MD and Jacob Shreffler, PhD

Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Shreffler J, Huecker MR. Exploratory Data Analysis: Frequencies, Descriptive Statistics, Histograms, and Boxplots. [Updated 2023 Nov 3]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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• Published: 19 October 2021

Description, prediction, explanation

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Description, prediction and explanation are all important in science. We welcome descriptive, predictive and explanatory studies, so long as the work is clear about its aims and uses appropriate methods to achieve its goals.

Traditionally, the social and life sciences have emphasized explanation: the identification of causal relationships between phenomena, such that intervening to change the cause would necessarily change the outcome. Furthermore, social and life scientists prioritize mechanistic evidence that can explain causal relationships between events or traits.

Description and prediction have traditionally had a secondary role in the natural and social sciences. Description has largely been considered valuable only insofar as it provides the starting point for causal inference. Prediction can be useful, but in and of itself has little to do with the pursuit of ‘scientific truth’ or the identification of laws that govern natural and social phenomena.

These historical emphases and preferences have also determined what is considered a ‘significant scientific advance’ in these fields — that is, the identification and accurate estimation of causal effects.

The past few years have seen a change in this attitude, partly because of the rise of big data and partly because of the life and social sciences gaining increased interaction with computer science and machine learning, where prediction is the central goal.

Editorially, we do not believe that descriptive studies are inherently of lower value or interest, especially when they uncover previously unknown phenomena or describe phenomena at scale through new measures and tools. Similarly, forecasting studies are invaluable in predicting future outcomes — for instance, predicting risk of disease, identifying young people at risk of poorer educational outcomes or predicting the impacts of anthropogenic climate change — when causal relationships are poorly understood or even when the relationships between effects cannot plausibly be causal.

If your manuscript asks a directional question (does x cause/impact/affect y ?), but reports only correlational evidence, it will most probably be returned to you without review, explaining the reason for our decision. Experiments are the key tool for causal inference. However, for several of the key questions regarding human behaviour, manipulating the independent variables of interest experimentally may be unethical, illegal or unfeasible. In those cases, we will expect that authors make use of identification strategies developed for observational data — for example, difference-in-difference designs, regression discontinuity or instrumental variables 1 .

If your manuscript aims to forecast future outcomes in a domain of broad interest and significance, we will expect that it includes out-of-sample validation of your predictions in an independent dataset if a suitable test dataset exists. In cases where no other suitable dataset exists, work can rely on cross-validation using the same dataset and partitioning the dataset into training and test components.

We value descriptive studies, especially when robust descriptions of specific phenomena are lacking or new phenomena of broad significance are discovered and the dataset is large and sufficiently diverse or representative. Mechanistic evidence is not a requirement for publication in those cases, nor is forecasting: although descriptive studies may form the starting point for causal inference or prediction in the future, this isn’t a requirement for their publication. However, if the phenomenon in question has been well described in the past and the specific field expects mechanistic evidence as the next step, we will expect that the work goes beyond description.

Researchers have argued that the boundaries between prediction and explanation are far less sharp than traditionally conceived: identifying causal effects provides a basis for prediction of future outcomes in the same contexts. However, explanatory models are almost invariably built without consideration of predictive accuracy, especially beyond the specific context. Recent proposals have made a case for ‘integrative empirical modelling’ that combines causal inference and prediction of future outcomes 2 . We find these proposals valuable and strongly encourage the submission of research that makes use of integrative empirical modelling.

Marinescu, I. E., Lawlor, P. N. & Kording, K. P. Nat. Hum. Behav. 2 , 891–898 (2018).

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• Key Differences

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Difference Between Exploratory and Descriptive Research

The research design is defined as a framework for carrying out research activities in different fields of study. The research design is classified into two important categories i.e. exploratory and conclusive research. Conclusive research is further subdivided into descriptive and casual research. The people often juxtapose exploratory research and descriptive research, but the fact is that they are different.

Take a read of this article to understand the differences between exploratory and descriptive research.

Content: Exploratory Research Vs Descriptive Research

Comparison chart, definition of exploratory research.

As the name implies, the primary objective of exploratory research is to explore a problem to provide insights into and comprehension for more precise investigation. It focuses on the discovery of ideas and thoughts. The exploratory research design is suitable for studies which are flexible enough to provide an opportunity for considering all the aspects of the problem.

At this point, the required information is loosely defined, and the research process is flexible and unstructured. It is used in the situation when you must define the problem correctly, identify alternative courses of actions, develop a hypothesis, gain additional insights before the development of an approach, set priorities for further examination. The following methods are used for conducting exploratory research

• Survey of concerning literature
• Experience survey
• Analysis of insights stimulating

Definition of Descriptive Research

By the term descriptive research, we mean a type of conclusive research study which is concerned with describing the characteristics of a particular individual or group. It includes research related to specific predictions, features or functions of person or group, the narration of facts, etc.

The descriptive research aims at obtaining complete and accurate information for the study, the method adopted must be carefully planned. The researcher should precisely define what he wants to measure? How does he want to measure? He should clearly define the population under study. It uses methods like quantitative analysis of secondary data, surveys, panels, observations, interviews, questionnaires, etc.

Descriptive Research concentrates on formulating the research objective, designing methods for the collection of data, selection of the sample, data collection, processing, and analysis, reporting the results.

Key Differences Between Exploratory and Descriptive Research

The difference between exploratory and descriptive research can be drawn clearly on the following grounds:

• Research conducted for formulating a problem for more clear investigation is called exploratory research. Research that explore and explains an individual, group or a situation, is called descriptive research.
• The exploratory research aims at the discovery of ideas and thoughts whereas the primary purpose of descriptive research is to describe the characteristics and functions.
• The overall design of the exploratory research should be flexible enough so that it provides an opportunity to consider various aspects of the problem. On the contrary, in descriptive research, the overall design should be rigid which protects against bias and also maximise reliability.
• The research process is unstructured in exploratory research. However, it is structured in the case of descriptive research.
• Non-probability sampling i.e. judgment or purposive sampling design is used in exploratory research. As opposed to descriptive research where probability (random) sampling design is used.
• When it comes to statistical design, exploratory research has no pre-planned design for analysis. Unlike, descriptive research that has the pre-planned design for analysis.

Therefore exploratory research results in insights or hypothesis, regardless of the method adopted, the most important thing is that it should remain flexible so that all the facets of the problem can be studied, as and when they arise. Conversely, descriptive research is a comparative design which is prepared according to the study and resources available. Such study minimises bias and maximises reliability.

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Explanatory, descriptive, analytical and predictive research

Research design and methodology, 4.2 research types, 4.2.2 explanatory, descriptive, analytical and predictive research.

Neuman (1994:27) states that exploratory research involves the use of a literature search or conducting focus group interviews. The goal of exploratory research is to discover ideas and insights, thereby helping the researcher’s need to better understand, test the feasibility of a more extensive study, and determine the best methods to be used in a subsequent study. Collins and Hussey (2003:10) argue that exploratory research design does not to come up with final answers or decisions but rather, researchers hope to produce hypotheses about what is going on in a situation. It provides very rich, meaningful information and definitive explanations for researchers.

For these reasons, exploratory research is broad in focus and rarely provides definite answers to specific research issues. The objective of exploratory research is to identify key issues and key variables. For instance, one outcome might be a better system of measurement for a specific variable. Some of the more popular methods of

exploratory research according to Cooper and Schindler (2003:11) include literature searches, depth interviews, focus groups, and case analyses.

Figure 4.2: Research designs

Source:Cooper and Schindler (2003)

As noted in Chapter One, a lack of in-depth research in the area of Internet banking adoption determinants in Ghana, means that this research features elements of explanatory research design.

As its name suggests, descriptive research seeks to provide an accurate description of observations of a phenomenon design where the major emphasis is on determining the frequency with which an event occurs or the extent to which two variables co-vary (Cooper & Schindler 2003:10). The objective of most descriptive research is to map the terrain of a specific phenomenon. Studies of this type usually start with questions such as: “What similarities or contrasts exist between variable A

Exploratory Case analyses Focus groups Depth interviews Literature searches

and B”. Descriptive research comparisons produce useful insights and lead to hypothesis formation.

Neuman (1994:28) indicates that descriptive research is used for purposes such as describing the characteristics of certain groups, determining the proportion of people who behave in a certain way, making specific predictions as well as determining relationships between variables. Descriptive data becomes useful for solving problems only when the process is guided by one or more specific research problems, and when much thought and effort, and quite often exploratory research to clarify the problem and develop hypotheses has occurred. Cooper and Schindler (2003:10) refer to two types of descriptive studies, these being cross-sectional and longitudinal.

Cross-sectional research involves drawing a sample of elements from a population of interest through the adoption of a technique called a sample survey. Characteristics of the elements, or sample members, are measured only once. A longitudinal study, on the other hand, involves a panel, which is a fixed sample of elements. The panel or sample remains relatively constant through time, although members may be added or replaced to keep it representative. Furthermore, the sample members in a panel are measured repeatedly over time, in contrast with the one-time measurement in a cross-sectional study. Longitudinal study also involves two types of panels, namely continuous panels (sometimes called true panels) and discontinuous panels (sometimes called omnibus panels). See Figure 4.3 below.

Figure 4.3: Descriptive research design

Source: Cooper and Schindler (2003)

This study therefore describes each of the various relationships between the variables as stated in Chapter One which determine the adoption of Internet banking. Analytical research also known as explanatory or causal research according to Gregor, (2006:621) is used in testing hypotheses that specify how and why certain empirical phenomena occur. It is mainly concerned with quantifying a relationship or comparing groups. The aim often is to identify a cause–effect relationship and it is usually conducted through a controlled experiment (fixed design) and supported by quantitative data. Also, it promotes comparison and statistical analysis. Collins and Hussey (2003:11) mention that analytical researchers use existing theories and hypotheses to identify the existence of relationships between variables. Examples of explanatory research which has been pursued in the IS literature includes: determinants of auction prices (Ariely & Simonson, 2003); diffusion and non-diffusion of e-commerce among SMEs (Grandon & Pearson, 2004); attitudes towards online security and privacy (Malhotra, Kim, & Agarwal, 2004:348); understanding the

Descriptive studies Longitudinal Sample survey Discontinuous panel Continuous panel Cross- sectional

antecedents and consequences of online trust (Gefen, Karahanna & Straub, 2003:54) and the impact of overlapping auctions (Jank & Shmueli, 2007:3).

In contrast to the proliferation of explanatory research, predictive research according to Gregor (2006:622) is an extension of the explanatory research whereby, instead of explaining existing phenomena, it is rather aimed at predicting the future or new observations with high accuracy.

There are two main reasons why predictive research is important, especially in IS (Malhotra, Kim, & Agarwal, 2004:338). First is its value for building theory in fast- changing environments such as the online environment that poses many challenges for the economic, psychological, and other theoretical models traditionally employed in IS. Predictive research plays a major role in theory-building where it shows new patterns and behaviours and helps uncover potential new causal mechanisms, which in turn leads to new theories being developed, provided the model is interpretable. Secondly, it provides a way out of the rigor relevance puzzle. Predictive research also serves as a statistically rigorous “reality check” to test the relevance of theories and the strength of explanatory causal models.

For part of this research, accurately predicting future behaviour of Internet banking adopters is more important than merely explaining past behaviour without any reference to future behaviour, since it is anticipated that future behaviour will guide actions of banks in their diffusion of technology.

• Theory of reasoned action (TRA)
• Theory of planned behaviour (TPB)
• Extension of technology acceptance model (TAM 2)
• CONCEPT OF INTERNET BANKING
• DETERMINANTS OF INTERNET BANKING ADOPTION
• Qualitative and quantitative research
• Explanatory, descriptive, analytical and predictive research (You are here)
• Population and sample selection
• Pilot study
• DATA ANALYSIS METHODS
• HYPOTHESISED RELATIONSHIPS
• LIST OF SOURCES

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Decision Intelligence Analytics and the Implementation of Strategic Business Management pp 15–30 Cite as

A Complete Overview of Analytics Techniques: Descriptive, Predictive, and Prescriptive

• Debashish Roy 7 ,
• Rajeev Srivastava 7 ,
• Mansi Jat 7 &
• Mustafa Said Karaca 8  
• First Online: 01 January 2022

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Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Analytics today is an area whose demand has reached a boom with every other organization using it to ponder upon major decisions. The data is growing exponentially day by day. The future of businesses is very much dependent on big data. This chapter reflects on the three types of analytics techniques used while discovering, interpreting, and communicating the meaningful patterns and trends in data, i.e., descriptive, predictive, and prescriptive analytics.

Descriptive : Analytics technique that uses data mining to get insights on what has happened in the past.

Predictive : Analytics technique that uses statistical methodologies and forecasting to know what is likely to happen in future.

Prescriptive : Analytics technique that uses algorithms to know what should be done to affect what is likely to happen in future. Beginning with the brief idea of analytics, the chapter reflects on data mining along with the role of ML and AI in analytics.

Techniques are compared stating the purposes they are used for. The big firms using them as a combination to grab every possible opportunity is discussed. These techniques being unique in their own implications have both the advantages and disadvantages. The chapter also discusses the various statistical methodologies, tools, and programming languages being used in these techniques. The overall thrust is to reflect on how organizations can adopt the new trend in order to completely change their operations and strategies to match up with the era where data is playing a huge part in taking informed decisions.

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Home » Descriptive Analytics – Methods, Tools and Examples

Descriptive Analytics – Methods, Tools and Examples

Table of Contents

Descriptive Analytics

Definition:

Descriptive analytics focused on describing or summarizing raw data and making it interpretable. This type of analytics provides insight into what has happened in the past. It involves the analysis of historical data to identify patterns, trends, and insights. Descriptive analytics often uses visualization tools to represent the data in a way that is easy to interpret.

Descriptive Analytics in Research

Descriptive analytics plays a crucial role in research, helping investigators understand and describe the data collected in their studies. Here’s how descriptive analytics is typically used in a research setting:

• Descriptive Statistics: In research, descriptive analytics often takes the form of descriptive statistics . This includes calculating measures of central tendency (like mean, median, and mode), measures of dispersion (like range, variance, and standard deviation), and measures of frequency (like count, percent, and frequency). These calculations help researchers summarize and understand their data.
• Visualizing Data: Descriptive analytics also involves creating visual representations of data to better understand and communicate research findings . This might involve creating bar graphs, line graphs, pie charts, scatter plots, box plots, and other visualizations.
• Exploratory Data Analysis: Before conducting any formal statistical tests, researchers often conduct an exploratory data analysis, which is a form of descriptive analytics. This might involve looking at distributions of variables, checking for outliers, and exploring relationships between variables.
• Initial Findings: Descriptive analytics are often reported in the results section of a research study to provide readers with an overview of the data. For example, a researcher might report average scores, demographic breakdowns, or the percentage of participants who endorsed each response on a survey.
• Establishing Patterns and Relationships: Descriptive analytics helps in identifying patterns, trends, or relationships in the data, which can guide subsequent analysis or future research. For instance, researchers might look at the correlation between variables as a part of descriptive analytics.

Descriptive Analytics Techniques

Descriptive analytics involves a variety of techniques to summarize, interpret, and visualize historical data. Some commonly used techniques include:

Statistical Analysis

This includes basic statistical methods like mean, median, mode (central tendency), standard deviation, variance (dispersion), correlation, and regression (relationships between variables).

Data Aggregation

It is the process of compiling and summarizing data to obtain a general perspective. It can involve methods like sum, count, average, min, max, etc., often applied to a group of data.

Data Mining

This involves analyzing large volumes of data to discover patterns, trends, and insights. Techniques used in data mining can include clustering (grouping similar data), classification (assigning data into categories), association rules (finding relationships between variables), and anomaly detection (identifying outliers).

Data Visualization

This involves presenting data in a graphical or pictorial format to provide clear and easy understanding of the data patterns, trends, and insights. Common data visualization methods include bar charts, line graphs, pie charts, scatter plots, histograms, and more complex forms like heat maps and interactive dashboards.

This involves organizing data into informational summaries to monitor how different areas of a business are performing. Reports can be generated manually or automatically and can be presented in tables, graphs, or dashboards.

Cross-tabulation (or Pivot Tables)

It involves displaying the relationship between two or more variables in a tabular form. It can provide a deeper understanding of the data by allowing comparisons and revealing patterns and correlations that may not be readily apparent in raw data.

Descriptive Modeling

Some techniques use complex algorithms to interpret data. Examples include decision tree analysis, which provides a graphical representation of decision-making situations, and neural networks, which are used to identify correlations and patterns in large data sets.

Descriptive Analytics Tools

Some common Descriptive Analytics Tools are as follows:

Excel: Microsoft Excel is a widely used tool that can be used for simple descriptive analytics. It has powerful statistical and data visualization capabilities. Pivot tables are a particularly useful feature for summarizing and analyzing large data sets.

Tableau: Tableau is a data visualization tool that is used to represent data in a graphical or pictorial format. It can handle large data sets and allows for real-time data analysis.

Power BI: Power BI, another product from Microsoft, is a business analytics tool that provides interactive visualizations with self-service business intelligence capabilities.

QlikView: QlikView is a data visualization and discovery tool. It allows users to analyze data and use this data to support decision-making.

SAS: SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it.

SPSS: SPSS (Statistical Package for the Social Sciences) is a software package used for statistical analysis. It’s widely used in social sciences research but also in other industries.

Google Analytics: For web data, Google Analytics is a popular tool. It allows businesses to analyze in-depth detail about the visitors on their website, providing valuable insights that can help shape the success strategy of a business.

R and Python: Both are programming languages that have robust capabilities for statistical analysis and data visualization. With packages like pandas, matplotlib, seaborn in Python and ggplot2, dplyr in R, these languages are powerful tools for descriptive analytics.

Looker: Looker is a modern data platform that can take data from any database and let you start exploring and visualizing.

When to use Descriptive Analytics

Descriptive analytics forms the base of the data analysis workflow and is typically the first step in understanding your business or organization’s data. Here are some situations when you might use descriptive analytics:

Understanding Past Behavior: Descriptive analytics is essential for understanding what has happened in the past. If you need to understand past sales trends, customer behavior, or operational performance, descriptive analytics is the tool you’d use.

Reporting Key Metrics: Descriptive analytics is used to establish and report key performance indicators (KPIs). It can help in tracking and presenting these KPIs in dashboards or regular reports.

Identifying Patterns and Trends: If you need to identify patterns or trends in your data, descriptive analytics can provide these insights. This might include identifying seasonality in sales data, understanding peak operational times, or spotting trends in customer behavior.

Informing Business Decisions: The insights provided by descriptive analytics can inform business strategy and decision-making. By understanding what has happened in the past, you can make more informed decisions about what steps to take in the future.

Benchmarking Performance: Descriptive analytics can be used to compare current performance against historical data. This can be used for benchmarking and setting performance goals.

Auditing and Regulatory Compliance: In sectors where compliance and auditing are essential, descriptive analytics can provide the necessary data and trends over specific periods.

Initial Data Exploration: When you first acquire a dataset, descriptive analytics is useful to understand the structure of the data, the relationships between variables, and any apparent anomalies or outliers.

Examples of Descriptive Analytics

Examples of Descriptive Analytics are as follows:

Retail Industry: A retail company might use descriptive analytics to analyze sales data from the past year. They could break down sales by month to identify any seasonality trends. For example, they might find that sales increase in November and December due to holiday shopping. They could also break down sales by product to identify which items are the most popular. This analysis could inform their purchasing and stocking decisions for the next year. Additionally, data on customer demographics could be analyzed to understand who their primary customers are, guiding their marketing strategies.

Healthcare Industry: In healthcare, descriptive analytics could be used to analyze patient data over time. For instance, a hospital might analyze data on patient admissions to identify trends in admission rates. They might find that admissions for certain conditions are higher at certain times of the year. This could help them allocate resources more effectively. Also, analyzing patient outcomes data can help identify the most effective treatments or highlight areas where improvement is needed.

Finance Industry: A financial firm might use descriptive analytics to analyze historical market data. They could look at trends in stock prices, trading volume, or economic indicators to inform their investment decisions. For example, analyzing the price-earnings ratios of stocks in a certain sector over time could reveal patterns that suggest whether the sector is currently overvalued or undervalued. Similarly, credit card companies can analyze transaction data to detect any unusual patterns, which could be signs of fraud.

Advantages of Descriptive Analytics

Descriptive analytics plays a vital role in the world of data analysis, providing numerous advantages:

• Understanding the Past: Descriptive analytics provides an understanding of what has happened in the past, offering valuable context for future decision-making.
• Data Summarization: Descriptive analytics is used to simplify and summarize complex datasets, which can make the information more understandable and accessible.
• Identifying Patterns and Trends: With descriptive analytics, organizations can identify patterns, trends, and correlations in their data, which can provide valuable insights.
• Inform Decision-Making: The insights generated through descriptive analytics can inform strategic decisions and help organizations to react more quickly to events or changes in behavior.
• Basis for Further Analysis: Descriptive analytics lays the groundwork for further analytical activities. It’s the first necessary step before moving on to more advanced forms of analytics like predictive analytics (forecasting future events) or prescriptive analytics (advising on possible outcomes).
• Performance Evaluation: It allows organizations to evaluate their performance by comparing current results with past results, enabling them to see where improvements have been made and where further improvements can be targeted.
• Enhanced Reporting and Dashboards: Through the use of visualization techniques, descriptive analytics can improve the quality of reports and dashboards, making the data more understandable and easier to interpret for stakeholders at all levels of the organization.
• Immediate Value: Unlike some other types of analytics, descriptive analytics can provide immediate insights, as it doesn’t require complex models or deep analytical capabilities to provide value.

Disadvantages of Descriptive Analytics

While descriptive analytics offers numerous benefits, it also has certain limitations or disadvantages. Here are a few to consider:

• Limited to Past Data: Descriptive analytics primarily deals with historical data and provides insights about past events. It does not predict future events or trends and can’t help you understand possible future outcomes on its own.
• Lack of Deep Insights: While descriptive analytics helps in identifying what happened, it does not answer why it happened. For deeper insights, you would need to use diagnostic analytics, which analyzes data to understand the root cause of a particular outcome.
• Can Be Misleading: If not properly executed, descriptive analytics can sometimes lead to incorrect conclusions. For example, correlation does not imply causation, but descriptive analytics might tempt one to make such an inference.
• Data Quality Issues: The accuracy and usefulness of descriptive analytics are heavily reliant on the quality of the underlying data. If the data is incomplete, incorrect, or biased, the results of the descriptive analytics will be too.
• Over-reliance on Descriptive Analytics: Businesses may rely too much on descriptive analytics and not enough on predictive and prescriptive analytics. While understanding past and present data is important, it’s equally vital to forecast future trends and make data-driven decisions based on those predictions.
• Doesn’t Provide Actionable Insights: Descriptive analytics is used to interpret historical data and identify patterns and trends, but it doesn’t provide recommendations or courses of action. For that, prescriptive analytics is needed.

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Types of analytics explained — descriptive, predictive, prescriptive, and more

Most business leaders have a general understanding of data analytics and many companies have departments dedicated to gathering and interpreting information about customers, processes, and markets. But there is more than one kind of analytics — and each tells a different story about your business. Understanding the different types of analytics can help you choose the ones that will benefit your business most and ultimately drive business objectives.

This post will explore the three most common types of data analytics and one less known model. This information will help you gain better insight into what your data says about your business so you can make adjustments to meet your goals.

Descriptive analytics

Predictive analytics, prescriptive analytics, diagnostic analytics, the future of analytics, types of business analytics.

The process of business analytics is an essential tool for interpreting and applying the vast amount of data your company collects and organizes. From customer behavior and conversion rates to revenue and business processes, the information generated by your company’s operations has to tell a helpful story to benefit you. Business analytics is the process that helps turn those data points into actionable insights.

The four different types of business analytics are descriptive, predictive, prescriptive, and diagnostic. Exploring the distinctions between these models can help you learn how to use each to support your business goals.

Descriptive analytics examines what happened in the past. You’re utilizing descriptive analytics when you examine past data sets for patterns and trends. This is the core of most businesses’ analytics because it answers important questions like how much you sold and if you hit specific goals. It’s easy to understand even for non-data analysts.

Descriptive analytics functions by identifying what metrics you want to measure, collecting that data, and analyzing it. It turns the stream of facts your business has collected into information you can act on, plan around, and measure.

Examples of descriptive analytics include:

• Annual revenue reports
• Survey response summaries
• Year-over-year sales reports

The main difficulty of descriptive analytics is its limitations. It’s a helpful first step for decision makers and managers, but it can’t go beyond analyzing data from past events. Once descriptive analytics is done, it’s up to your team to ask how or why those trends occurred, brainstorm and develop possible responses or solutions, and choose how to move forward.

Predictive analytics is what it sounds like — it aims to predict likely outcomes and make educated forecasts using historical data. Predictive analytics extends trends into the future to see possible outcomes. This is a more complex version of data analytics because it uses probabilities for predictions instead of simply interpreting existing facts.

Use predictive analytics by first identifying what you want to predict and then bringing existing data together to project possibilities to a particular date. Statistical modeling or machine learning are commonly used with predictive analytics. This is how you answer planning questions such as how much you might sell or if you’re on track to hit your Q4 targets.

A business is in a better position to set realistic goals and avoid risks if they use data to create a list of likely outcomes. Predictive analytics can keep your team or the company as a whole aligned on the same strategic vision.

Examples of predictive analytics include:

• Ecommerce businesses that use a customer’s browsing and purchasing history to make product recommendations.
• Financial organizations that need help determining whether a customer is likely to pay their credit card bill on time.
• Marketers who analyze data to determine the likelihood that new customers will respond favorably to a given campaign or product offering.

The primary challenge with predictive analytics is that the insights it generates are limited to the data. First, that means that smaller or incomplete data sets will not yield predictions as accurate as larger data sets might. Getting good business intelligence (BI) from predictive analytics requires sufficient data, but what counts as “sufficient” depends on the industry, business, audience, and the use case. Additionally, the challenge of predictive analytics being restricted to the data simply means that even the best algorithms with the biggest data sets can’t weigh intangible or distinctly human factors. A sudden economic shift or even a change in the weather can affect spending, but a predictive analytics model can’t account for those variables.

Prescriptive analytics uses the data from a variety of sources — including statistics, machine learning, and data mining — to identify possible future outcomes and show the best option. Prescriptive analytics is the most advanced of the three types because it provides actionable insights instead of raw data. This methodology is how you determine what should happen, not just what could happen.

Using prescriptive analytics enables you to not only envision future outcomes, but to understand why they will happen. Prescriptive analytics also can predict the effect of future decisions, including the ripple effects those decisions can have on different parts of the business. And it does this in whatever order the decisions may occur.

Prescriptive analytics is a complex process that involves many variables and tools like algorithms, machine learning, and big data. Proper data infrastructures need to be established or this type of analytics could be a challenge to manage.

Examples of prescriptive analytics include:

• Calculating client risk in the insurance industry to determine what plans and rates an account should be offered.
• Discovering what features to include in a new product to ensure its success in the market, possibly by analyzing data like customer surveys and market research to identify what features are most desirable for customers and prospects.
• Identifying tactics to optimize patient care in healthcare, like assessing the risk for developing specific health problems in the future and targeting treatment decisions to reduce those risks.

The most common issue with prescriptive analytics is that it requires a lot of data to produce useful results, but a large amount of data isn’t always available. This type of analytics could easily become inaccessible for most. Though the use of machine learning dramatically reduces the possibility of human error, an additional downside is that it can’t always account for all external variables since it often relies on machine learning algorithms.

Another common type of analytics is diagnostic analytics and it helps explain why things happened the way they did. It’s a more complex version of descriptive analytics, extending beyond what happened to why it happened.

Diagnostics analytics identifies trends or patterns in the past and then goes a step further to explain why the trends occurred the way they did. It’s a logical step after descriptive analytics because it answers questions like why a certain amount was sold or why Q1 targets were hit.

Diagnostic analytics is also a useful tool for businesses that want more confidence to duplicate good outcomes and avoid negative ones. Descriptive analytics can tell you what happened but then it is up to your team to figure out what to do with that data. Diagnostic analytics applies data to figure out why something happened so you can develop better strategies without so much trial and error.

Examples of diagnostic analytics include:

• Why did year-over-year sales go up?
• Why did a certain product perform above expectations?
• Why did we lose customers in Q3?

The main flaw with diagnostic analytics is its limitation of providing actionable observations about the future by focusing on past occurrences. Understanding the causal relationships and sequences may be enough for some businesses, but it may not provide sufficient answers for others. For the latter, managing big data will likely require more advanced analytics solutions and you might have to implement additional tools — venturing into predictive or prescriptive analytics — to find meaningful insights.

The use of analytics in business is not new, but it is on a steep growth trajectory. Fueled by huge data sets streaming in from the IoT, advancements in AI, and the growth of self-service BI tools, the use of analytics in business has yet to peak.

The US Bureau of Labor Statistics predicts huge growth in the number of research analysts in the coming years, projecting a “must faster than average” growth rate of 19%. Additionally, some of the industry’s top experts in data science and analytics predict the ideal candidate for businesses in the future will be a person who can both understand and speak data.

Even as the need for analytics experts grows, the market for self-service tools continues to escalate as well. A report from Allied Market Research expects the self-service BI market to reach $14.19 billion by 2026, and Gartner cites the growth of business-composed data and analytics, that focuses on people, “shifting from IT to business.”

Implementing more advanced analytics — and for some businesses bringing analytics into business strategy — will continue to become more important for companies of all sizes.

Get started with business analytics

The four types of data analytics give you tools to understand what happened (descriptive), what could happen next (predictive), what should happen in the future (prescriptive), and why something happened in the past (diagnostic). Your ability to make strategic, data-driven decisions for your business depends on the facts you gather and how you use them.

When you’re ready to go further than simple data collection, choose the type of analytics that best fits your business’ needs. Ask yourself what question you need answered or what decision you need to make, and start with the right type of analytics.

Adobe Analytics helps businesses of any size, and in any industry, turn data into business intelligence. Collect and organize data all in one place and put the power of AI to work in analytics that create meaningful, actionable insights.

Watch the video or request a free, custom demo to see how Adobe can harness the power of analytics for your brand.

11 Mar Descriptive, explanatory and predictive analyses

One typology of statistical analyses is based on their purpose:

• Descriptive analyses, to describe the variables, either individually (descriptive statistics), or by cross-tabulating them with another variable (by performing univariables analyses)
• Explanatory analyses, to determine the influence of one or more variables on another (for example using an Odds Ratio)
• Predictive analyses, to classify patients into two groups based on their characteristics

pvalue.io uses this typology to describe the type of analysis to implement.

Descriptive analyses

Descriptive analyses involve the analysis of single-variable statistics and univariables analyses . These analyses are useful for quickly identifying notably extreme data or outliers, and obtaining p-values. Results are displayed in tables and figures. The descriptive analyses are crucial to get the Table 1 of all medical scientific articles (univariable analysis of all patient characteristics according to exposure/treatment).

Explanatory analyses

Explanatory analyses use complex statistical models such as regressions. These analyses make it possible to determine the strength of the association between a response variable (explained variable Y) and one or more explanatory variables (X). They also make it possible to test the statistical significance of this association (in the form of a p-value). The strength of the association can be:

• Odds Ratios for logistic regressions
• Hazard Ratios for Cox’s Model
• Estimates or a coefficients for linear regressions

When there is only one explanatory variable, then the analysis is univariable, otherwise, it is multivariable. In this type of analysis, it is important that the result of the modeling is simple to interpret, which is why pvalue.io prefers to propose to split an explanatory variable whose linearity cannot be assumed rather than transforming this variable into a natural spline. This is also why the coefficients of the extraneous variables are not displayed.

Predictives analyses

The objective of a predictive analysis with pvalue.io is to develop a prediction model, like a score. These models aim at classifying a given patient according to her demographic, clinical or para-clinical characteristics. With a predictive analysis, we obtain the probability that the patient is classified in one group rather than the other. This type of analysis makes it possible to answer the question: what is the probability that a male patient, 68 years old, smoker, asymptomatic, last vaccination performed 3 months ago has a long covid?

Prediction models are based on multivariable models (multiple logistic regression). Unlike explanatory analyses, for which the results must be simple to interpret, for predictive analyses, what is most important is the ability to correctly classify patients (good discrimination: high area under the curve, and good calibration: the probability that they are classified in the right group is correct). This is why pvalue.io proposes to transform into a natural spline the variables for which the log-linearity hypothesis is not assumed: even if the interpretation is tricky, the prediction is more accurate.

Once this model is developed, it is necessary to validate it by estimating the performance of the model on other patients than those used to develop it.

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