Top Three Essentials of Exploratory Data Visualizations

By CIOReview | Friday, November 23, 2018

Visualization is a critical element of the data science family. Most leaders are in favor of processing massive amounts of data using algorithms and AI techniques to extract the maximum out of it. It becomes essential to organize the visual elements of analysis into a comprehensive structure. Exploratory data visualization is necessary to summarize the main characteristics often using a visual procedure.

Exploratory data visualizations (EDVs)

Exploratory Data Visualizations are the type of visualizations an organization assembles when they do not have a clue about what information lies within the data they have collected. Here comes the role of data scientists who conduct qualitative research and use the appropriate exploratory data analysis (EDA) tools and techniques to dig out the most relevant information and suggest a direction for further study. It is essential to hire the right people to complete EDA as only they will have the motivation and patience to go through the large data sets.

The significance of partnering with the right business experts

The right business partners are the primary and crucial step in a successful EDA effort. It would be a big mistake to entirely rely upon the data science team and assume that they have all the capabilities to do the analysis effectively. After a period, data scientists should cultivate a conversational understanding of how a business will work. Business experts in an organization are better equipped to interpret EDVs once they understand what they are looking for, so it is highly recommendable to engage them as soon as possible rather than relying entirely on savvy business members of your data science team. Include members who have in-depth knowledge of their job and the circumstances of the EDA scenario. It is of utmost importance to find the best business expert for the purpose and make sure that the EDA efforts are a priority.

Data Visualizations

After assembling the qualitative data scientists, one needs to focus on the EDA and target the right business expert. Below mentioned are three best practices for building the data visualizations.

• Start with interface and graphics: Enterprises have to engage their business experts as soon as possible and should not underestimate that the experts do not know the tool’s graphical user interface. If needed, the enterprise must give a gist of how the interface works.

• Use R, Python or similar to build custom visualizations: If an organization has an advanced tool that comes with high-powered graphical interfaces they must go for it as every business in unique and custom visualization can be an added factor to bring the most significant insights.

• Iterative Approach: To arrive at a decision or the desired result, organizations must repeat rounds of analysis. The aim is to bring the desired resolution or outcome closer to discovery with each iteration or repetition.