If you’re not familiar with the term “exploratory data visualization” or “EDV,” you’re in for a treat. We often think of data visualization as the final step in a data project or process. You’ve gathered, scrubbed, and organized your data sets; you’ve paired related metrics and organized them into a cohesive structure. The next step? Polish off the aesthetics and create a visually stunning, easy-to-navigate dashboard.
But what if data visualization served a greater purpose? What if it was more than visually appealing graphs and color-coordinated charts? The fact of the matter is, saving visualization for the last step in your data analysis process could be holding you back. Here’s how: Data visualization is, for many organizations, an afterthought. In reality, data visualization is significantly more than charts and graphs; it’s a tool for data scientists to not only make data easier to understand, but a tool that can make the analysis process faster, simpler, and more insightful. This is where Exploratory Data Visualization shines.
With the help of an EDV, you can gain a better understanding of your organization’s data from the outset. You can find new insight and correlations that you didn’t know existed before and use them to benefit not only our dashboard, but your organization as a whole.
Why visualize exploratory data analysis?
Understanding EDV starts with an understanding of EDA (exploratory data analysis). In short, EDA is a process by which a data scientist seeks to understand data after gathering, cleaning, and sorting it. The added benefit of visualization is that visualized data is ultimately easier to understand than data in simply numerical form. Visualization facilitates analysis of data because it helps viewers and stakeholders grasp complex data concepts faster, paving the way to uncover more insights.
EDVs are the first opportunity you have to apply data visualization to your data in a meaningful way. When creating an EDV, your goals should be:
- To highlight the relationships between variables
- To find hidden correlations between data sets
- To generate a deeper understanding of the data at hand
In a way, EDVs are structured to provide “clues” that lead to deeper analysis and greater insight. They are most useful when you aren’t sure what the data is telling you, but are also beneficial for identifying “blind spots” to ensure that you haven’t missed any of the important implications that your data could convey.
Getting buy-in for visualizing exploratory data analysis projects
A successful exploratory data visualization project needs support, and requires some data-minded brain power behind it. They often require a substantial understanding of data science and data visualization, as well as the right tools to create and share your EDV.
If data visualization is an afterthought in your organization, now is the time to get buy-in so you have access to the best possible resources to execute your visualization strategy. In order to get the support your EDV will need, consider its purpose, and how that purpose translates to stakeholders.
One of the most legitimate reasons for initiating an EDV strategy is this: “We need to understand our data better, and actually know what it’s telling us.” By combing through your data and doing a side-by-side comparison of seemingly unrelated factors, you can show stakeholders and higher ups not only the power of data, but how you can maximize it before you truly understand its implications.
Best practices for visualizing your exploratory data analysis
Once you have the right team, tools, and support you need to create a successful EDV, it’s time to dig tin and create the visual counterpart of your data exploration! Here are a few best practices to keep in mind:
- Pick a tool that lets you create customized graphics and visualizations. The right data visualization tool goes a long way. Every organization has their own unique KPIs and goals, so it stands to reason that your visualizations will be unique too. If you feel like your data visualization is limited, your insight will probably be limited as well. By having a toolbox on hand that allows you to customize each graphic and visualization, you can maximize your time and resources during the EDV process.
- Understand your tools, and make sure your team does too. Data analysis and visualization tools are second nature to data scientists, but it’s possible that not everyone on your team will come to the table with data skills. Just because someone is a business expert doesn’t mean he or she is a data (or data visualization) expert. Take the time to train everyone on your team so they understand how your data tools work, and are comfortable navigating them. This will make it easier in the long run for them to identify correlations and draw insight from the EDV.
- Focus on using the right charts for the right data. Data visualization allows you to communicate data in a way an Excel spreadsheet never could, but data visualization only works when you pair data with appropriate visual aids. When looking at data, think about what you are trying to achieve and what the data needs to communicate. Is a bar chart your best option, or would a sparkline or pie chart be more appropriate? At the end of the day, the best graphs are the ones that allow viewers to understand data quickly and completely.
- Approach your EDV with an iterative mindset. In other words, start small and build. Exploratory data visualizations are exactly what you think – exploratory. This means you’ll be doing several rounds of exploration with your team of experts. Start small, get your feet wet, then build on that success and start comparing larger and more complex pieces of data. This will not only help your team acclimate to the EDV process, but allow you to engage with the data in a more meaningful way.