Data Exploration vs. Data Presentation. Learn the difference!

Data exploration and data presentation have two distinct functions but are both important when delivering data solutions.

Data Exploration vs. Data Presentation. Learn the difference!
Photo by Kaleidico / Unsplash

First, let's establish some common ground by defining these two key concepts:

Data exploration refers to the process of performing in-depth analyses of data to discover previously unknown trends, relationships or patterns.

Data presentation is the act of informing an audience about what you've found in a way that helps them understand what it all means.

Tableau, PowerBI, Looker, and Qlik are just some of the many tooling options you have at your disposal for data exploration. Visual analytics software gives analysts and business users an advanced tool for slicing data, which speeds up the process of finding new information. Because the user has to deal with multiple data sources and doesn't know where the analysis will lead, your choice of software must be able to adapt to their needs and have a wide range of features.

When compared to other types of problems, data presentation has its own unique set of circumstances, objectives, and target audiences. Consider the amazing data stories presented by the likes of FiveThirtyEight, Bloomberg, among others. These data journalists frequently provide excellent examples of data presentation, complete with narrative guidance, eye-catching graphics, and detailed written explanations.

In light of these examples, it is clear that even the most sophisticated data exploration tool will fall short when it comes to providing a visually appealing representation of data. If you want your data to be presented in a way that is interesting and engaging to your audience, you will need a tailored solution.

In this article, I'll show you six main ways in which data exploration and data presentation are different, giving you more insight into both topics.

1. Who is the information intended for?

As the target audience, the data analyst is also the main person who reads any exploratory data analysis. The analyst is the one controlling the variables and observing the outcomes. They must use hypothesis formulation, data analysis, and result visualization in rapid feedback loops.

The author of the analysis is not the target audience for a data presentation. Instead, the audience is a specific subset of end-users. As the people on the front lines of making business decisions, these end-users aren't necessarily analytically minded and may have trouble making the connection between an analysis and how it affects their work.

2. What is the message?

The process of data exploration is all about digging for hidden insights. This is like a puzzle that the analyst is attempting to solve.

Making sense of data requires solving a puzzle and then showing that solution to people who can do something with it. Authors of data presentations should have a clear goal and point of view and lead the reader through the material.

3. Provide an explanation for the data's significance

Sometimes, the results of an analysis performed with data exploration tools will be obvious to the analysts performing the work. Even a small improvement in conversion rates (say, 1%) can have a significant impact on your business and strategy. The analysts' primary task is to determine why this is occurring.

The burden of justifying analytical findings falls more heavily on data presentations. The author of a data presentation needs to provide more background information and explanations at the outset when the target audience is less knowledgeable about the subject matter. What kind of metrics do we use to evaluate the effectiveness of the conversion process? Is it significant if there's a 1% shift? We need to know how this shift will affect our company.

4. Visualizing the data

Exploration of data requires intuitive visualizations that can show multiple dimensions in order to reveal hidden patterns.

It's crucial that data visualizations are easy to understand and use when presenting information. No one in the audience wants to take the time to figure out what each chart or visual means. It is important to use chart types that work and provide a brief introduction to the data's structure and how to read it.

5. Next best actions

Data exploration can help you ask more insightful questions. When you take the time to make your questions more specific, you'll get new ideas and a better understanding of how your company works.

Ultimately, the goal of any data presentation should be to help decision-makers make better choices. The majority of the education (via data exploration) will have been completed, leaving only the equally challenging task of sharing the insights and subsequent actions.

6. Create and share data insights

Analysts often work alone to collect data, integrate data from different sources, and delve into the data to find insights, making data exploration a solitary activity. Unless new insights are discovered that must be shared, data exploration is typically done in isolation.

Sharing data through a presentation is a social, group effort. Value is unlocked when data-driven discoveries are communicated to those who can put them in perspective. This is not a failed analysis, but rather an exercise in dialogue.

A piece on Data Storytelling

There is something between the extreme ends of data exploration and data presentation. I believe data storytelling lies at this intersection. There is more to data stories than just telling or finding. This is the point where the message meets the exploration, and it presents an excellent opportunity to explain the data in a guided, narrative fashion.


There are advantages to both data exploration and data explanation, and understanding them is crucial for implementing any data-driven solution.