The Definitive Guide to Exploratory Analysis
When you’re faced with complex business questions and large, varied datasets hold the answers, it’s easy to arrive at incorrect or irrelevant conclusions. We all approach data analysis with assumptions, which may or may not be accurate, and identifying inaccurate assumptions can be difficult if you don’t have a solid understanding of the data to begin with.
This is where exploratory analysis shines.
Exploratory analysis ensures that the insights gleaned from the analysis process are accurate, correctly interpreted, and relevant to the business question. Once this foundation is laid, advanced techniques such as machine learning can be used more reliably to thoroughly mine massive datasets.
In this guide, we explain the reasons why teams benefit so profoundly from exploratory analysis, cover the various types of exploratory techniques, and discuss which roles should be involved in the process. We also take a look at one of the most essential prerequisites for successful exploratory analysis: curiosity.
What is Exploratory Analysis?
Exploratory analysis is an approach to analyzing large datasets that focuses on investigation and summarization and typically uses visualization techniques to present findings. It’s done before diving into machine learning or statistical modeling to identify what insights the data might reveal before moving forward with the process. Exploratory analysis also makes it easier to spot patterns, see anomalies, and test hypotheses.
The Purpose of Exploratory Analysis
Why conduct exploratory analysis prior to taking more advanced actions? If you jump directly to machine learning techniques or statistical modeling, you may involuntarily bring along assumptions that impact the findings. Incorrect assumptions can lead to inaccurate findings, which will lead to poor decision-making.
Additionally, exploratory analysis allows you to get a bird’s eye view of the data to quickly find patterns, outliers, anomalies, and relationships that might bring up additional questions or provide a broader perspective.
Exploratory Analysis Techniques
There are four general categories of exploratory analysis techniques, with a variety of specific techniques in each category. Let’s take a deeper look at each.
1. Univariate non-graphical
Univariate non-graphical analysis is the simplest type since there’s only one variable involved and no visualization. Univariate analysis is used both to describe data and to spot patterns and outliers. Univariate non-graphical techniques include:
- Categorical — Categorical simply describes the range of values and the frequency of occurrence for each value.
- Spread — The spread of a distribution measures how far away from the center data values are likely to be found. Spread measures include variance, standard deviation, and interquartile range.
- Central tendency — The central tendency of a distribution is related to typical or middle values such as the mean, median, and/or mode.
2. Univariate graphical
It’s difficult (or sometimes impossible) to get a full understanding of the data without a visual representation. For this reason, graphical types of techniques are usually preferred. With univariate graphical techniques, there’s still only one variable involved, but visualizations are used. Here are a few examples:
- Box plots — Box plots are ideal for presenting information about central tendency and can reveal measures of location and spread. They also excel at showing symmetry and identifying outliers.
- Histograms — A histogram is a bar plot that can show you the shape of the data as well as central tendency, spread, modality, and outliers. Histograms are one of the easiest and quickest ways to gain an understanding of the data.
- Stem and leaf plot — A stem and leaf plot is a simpler form of the histogram which shows all the data values and the shape of the distribution.
3. Multivariate non-graphical
Multivariate data includes more than one variable. With multivariate non-graphical techniques, you can explore more variables, but there’s no visualization of the data. Relationships in the data are shown via cross-tabulation or statistics.
- Cross-tabulation — Cross-tabulation is ideal for categorical data. When two variables are involved, cross-tabulation creates a two-way table with column headings for one variable and row headings for the other variable.
- Statistics — Various statistical techniques can be used for multivariate non-graphical analysis, including univariate statistics (where one categorical variable and one quantitative variable exist), and correlation and covariance (where there are two quantitative variables).
4. Multivariate graphical
Multivariate graphical techniques display relationships between two or more variables with visualizations. It’s the most commonly used type of exploratory analysis since most real-world applications involve multiple variables and understanding the data requires visualization. Here are a few common techniques.
- Scatter plot — A scatter plot uses dots to represent the values for two variables. One variable’s data is presented in one color and plotted along the x-axis and the other is presented in another color and plotted along the y-axis.
- Bar chart — A bar chart is another option for presenting categorical data. Rectangular bars are assigned varying lengths that are proportional to the values they represent.
- Heat map — A heat map is an excellent tool for visualizing complex statistical data. It reveals the amount or intensity of a phenomenon as a color gradient in two dimensions.
Which Roles Need the Ability to Do Exploratory Analysis?
Traditionally, exploratory analysis has fallen under the realm of data scientists. But the reality is that just about everyone in an organization has a valuable contribution to make to exploratory analysis. For this reason, the most effective data analysis is collaborative. Let’s explore how each role contributes to the exploratory analysis process.
Data Engineers — Data engineers build custom integrations to connect datasets to the cloud data warehouse and manage the data pipeline. They also develop machine learning endpoints, maintain the data platform, and perform data warehouse optimization.
Analytics Engineers — Analytics engineers apply software engineering best practices to ensure that data is cleaned and transformed for analysis. Additionally, they maintain data documentation and definitions and train business users on how to use data visualization tools.
Data Analysts — Data analysts work with business users to understand requirements. They seek to uncover insights into important business questions business teams are asking. Data analysts also build dashboards that business users can use as a starting point for further exploration.
Business Analysts — The business teams who are closest to the meaning of the data bring a unique and crucial perspective to the exploratory analytics process. They define business requirements and use analytics tools to explore data to inform daily decision-making.
Curiosity: A Prerequisite for Exploratory Analysis
Effective exploratory analysis requires curiosity. The most valuable insights are gained from asking questions and digging into the answers by asking follow-up questions. The curiosity that drives these follow-up questions (“But why is this phenomenon occurring?” “What’s really driving this trend we’re seeing?”) is what leads to deeper insights that uncover game-changing opportunities.
Initial insights are just a part of the iterative decision-making process. They can help alert teams to where they should direct their attention. But while analysis can reveal a statistical fact, anomaly, or prediction, curiosity is what marries that information with human context for more meaningful insights.
For this reason, exploratory analysis requires a culture of curiosity. Organizations must encourage and support an approach that enables all stakeholders to inquire more deeply and seek the insights underneath initial findings.
Data Flow for Exploratory Analysis
For effective exploratory analysis, your analytics platform must allow you to conduct analysis with visualizations early in the process. Let’s take a look at a typical data flow and where exploratory analysis comes in.
Extract & Load— The first step in any data flow is to get the data out of the various sources where it lives and into the cloud data warehouse. Tools like Fivetran are used at this level of the data tech stack.
Transform — Once in the CDW, data is transformed, tested, and documented. In this stage of the flow, tools like dbt integrate, clean, de-duplicate, restructure, filter, aggregate, and join data so it’s prepared for analysis. This stage is also where exploratory analysis is first used. Tools like Sigma work seamlessly with dbt and other data transformation tools to explore data for a broad understanding, find answers, share discoveries, and write data models.
Present — After the data is standardized and tested with dbt and similar solutions, machine learning can then mine the data quickly, and tools like Sigma step back in to create reports, dashboards, visualizations to present the findings.
As you can see, exploratory analysis is an iterative process that guides the data flow more effectively. As a result of introducing exploratory analysis early in the process, you can have greater confidence in your findings.
Sigma for Exploratory Analysis
Sigma is ideal for exploratory analysis because it is built for a collaborative exploratory process. It includes built-in features that make it simple for users to securely share, reuse, and build on the work of other authorized users. Sigma also accelerates the data exploration process its intuitive spreadsheet-like interface that automatically translates inputs to SQL.
Data and analytics engineers can move faster without having to write SQL from scratch, while even non-technical business users can easily conduct exploratory analyses. Additionally, because Sigma sits on top of the CDW and doesn’t rely on extracts, it can handle the massive data sets involved in exploratory analysis without crashing. With Sigma, anyone can explore data down to the most granular level without any limitations.
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