The difference between exploring data and explaining data

Many businesses across multiple industries leverage data to provide insights into crucial organizational aspects, such as sales, audience engagement, customer service, and the performance of products and services. With the massive volume of information collected on a daily basis, it’s imperative that businesses find ways to analyze and understand it to guide and inform decision-making. 

It can be difficult to analyze those results, find actionable insights, and present them in a helpful way to stakeholders because businesses find themselves lost between two distinct ways of handling information: exploratory and explanatory data analysis. These two types of analysis  are separate but not contradictory stages within the analytics process. They’re equally vital aspects when it comes to data contextualization and storytelling and if you want to convey data insights as effectively as possible, it’s crucial to know how they differ. 

The Process and Purpose of Analysis

The goal of data analytics is to interpret data and turn it into easy-to-understand knowledge that will drive decision-making. It takes information from disparate sources, including numbers, observations, and facts, and turns it into coherent and actionable insights. The data process typically includes the following steps:

  • Data requirements specification: the data needed for analysis is usually based on a question or a hypothesis which determines how the data will be inputted. 
  • Data collection: the process of gathering information with an emphasis on accurate and honest collection.
  • Data processing: This step means structuring the data as needed for whatever analysis tools are being used. 
  • Data cleaning: the process of correcting or preventing errors such as inaccuracies, duplications, or incomplete information.
  • Data analysis: the process of understanding, interpreting, and gaining insights.
  • Communication: reporting the analysis in a format that will support decisions and further action.

Exploratory and explanatory analysis are both part of the analysis process, but serve distinct purposes in helping stakeholders understand data and the story they’re telling. However, to be an effective data communicator, it’s important to weave data into a story, making it easier for an audience to understand and remember.

What Is Exploratory Data Analysis?

Exploratory data analysis helps users get familiar with the data and make sense of them. This might involve looking at it from different angles to identify any patterns or trends. Tools such as spreadsheets, dashboards, or data visualizations to reveal trends, outliers, anomalies, and other points of interest are typically used for exploratory analysis.

In exploratory data analysis, clues and evidence are used to draw insights from the gathered information. It tries to find meaningful relationships between variables, but its implications may be limited. Although it gives you conclusions or answers to your hypotheses, it does not provide you with actionable insights just yet. For example, if your hypothesis is that customers leave your website after arriving on a certain landing page, you would input bounce rates, time on page, content from that landing page, etc, into a tool that organizes and lays out this information in such a way to allow for further analysis. 

What Is Explanatory Data Analysis?

This phase happens after exploratory analysis when you’ve identified something interesting or insightful from the collected information and are ready to share it with an audience. Using the above website example, this is the stage where you would dig into all of the factors that contributed to the user drop-off to understand why it’s happening and how you might fix it.

From there, you would have an understanding of the actionable insights needed to make any necessary adjustments. And, the most critical part of this stage is communicating those insights to the appropriate stakeholders and decision makers who can hopefully take the necessary actions to make positive change. Again, the important thing to remember here is that you’re weaving data into a larger story. You want your audience to understand and remember what you’re telling them – which is where storytelling comes in – and back it up with the evidence of your data.


Data analytics can be a challenge for many businesses and requires a firm understanding of the required process – from requirements to communication – and the difference between exploratory and explanatory analysis. While they go hand-in-hand, they play different roles in  the analysis process and help you arrive at different conclusions. Exploratory data analysis can help you get familiar with data and identify patterns and trends while explanatory analysis tells you the “why” and gives you the actionable insights needed to drive decisions.

Data analysis is critical to helping businesses move forward and maximize their outcomes but can be difficult for a business to manage on its own. At Gemini Data, we can help your business solve your biggest data challenges, enabling you to understand and share data stories. We can help you connect the dots, contextualize your data, and drive actionable insights. Contact us today to learn more. 

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