The advent of digitalization brought with it a slew of new ramifications. Customers’ sentiments can now be accessed in real time by businesses. Beyond that, it has the ability to generate data that allows businesses to better understand consumer behavior, satisfaction rates, and preferences.
With one caveat: data is meaningless unless you know what data points are essential, how to analyze them, and why they are contextually important. All of this boils down to data literacy.
Based on Gartner’s survey, by 2023, data literacy will become essential in driving business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs. The basic of data literacy is distinguishing between exploratory analysis and explanatory analysis.
This article will discuss the differences between exploratory and explanatory analysis and why both are important for businesses as the basis of data literacy.
The Objective of Data Analysis
The main objective of data analysis is to transform the data into wisdom for the decision-making process. The data is a collection of raw records, facts, and observations. While the information is the results of the processed, organized, and analyzed data. Then, we can use the information to build understanding and insight, or knowledge, after you have it. Finally, wisdom is achieved when information is used and informs decisions and actions.
Communication of knowledge and insights to stakeholders and decision-makers is probably the most crucial stage in moving from data to wisdom; they are the ones who must use the knowledge and insights to guide actions and plans. When presenting and explaining data, people frequently fall into a mistake by overburdening their audience. Keep in mind that your audience’s attention span is limited, so make sure you communicate effectively.
The differences between exploratory analysis and explanatory analysis
People get too wrapped up in presenting their exploratory analysis when they should be focusing on their explanatory analysis instead, whether it’s due to excitement or a need to defend the importance and validity of their ideas.
Exploratory analysis is what you do to better understand and familiarize yourself with your data while generating information. The exploratory analysis focuses on establishing links between variables and spotting patterns and outliers, whether we start with a hypothesis or query or examine the data to see what could be interesting. The Six Sigma project might involve the analysis activities in the Analyze phase, such as hypothesis tests, correlation study, or regression analysis.
When we have discovered something intriguing and want to learn more about it, we conduct an explanatory analysis. We concentrate on what happened (information) and why during explanatory analysis (knowledge). Explanatory analysis’ main outputs are insights, which, when used to guide decision-making and actions, create wisdom.
While the distinctions between exploratory and explanatory analysis may appear self-evident, you’d be amazed how many people seem to get them mixed up. According to Cole Nussbaumer Knaflic, Exploratory analysis is like hunting for pearls in oysters. “It’s possible that we’ll have to open 100 oysters (test 100 different hypotheses or analyze data in 100 different ways) to locate two pearls. When expressing our analysis to our audience, we want to be in the explanatory area, which means you have a specific item you want to explain, a specific tale you want to tell – probably about those two pearls.”
It can be tempting to show the audience everything after doing all the work in our exploratory analysis – either out of enthusiasm or as evidence of all the work you performed and the robustness of the exploratory analysis. However, this serves to overburden the audience and reduce communication effectiveness by drowning your ideas (and audience) in a sea of data.
Instead of simply showing all of the data – all 100 oysters, to continue with the metaphor – we should present explanatory data, taking the time to transform the data into information and knowledge and highlighting the essential insights (the pearls). When we concentrate on the exploratory analysis, your audience will reopen all of the oysters. Instead, focus on providing the nuggets of information and knowledge that the audience requires.
It’s a little more complex than our expectation to communicate successfully using data. We must know who your target audience is, how data-savvy they are, and their primary issues or wants. We must also tell a story; storytelling is one of the most effective techniques to stimulate knowledge and insights into wisdom. Check out our Data Storytelling and Visualization course to learn how to communicate effectively and convincingly with data, from visualizations and narratives to presentation delivery.