Visual data exploration

In a presentation at Tableau Conference 2017 in Las Vegas, Tatiana Gabor, an analytics manager for the revenue team at music streaming company Spotify, said her team of analysts starts every project by visually exploring the available activity data collected on Spotify users. The team analyzes patterns in user behavior to understand how people respond to changes in the Spotify platform and to develop new ways to keep users engaged.

The most important benefit of visual data exploration is it enables you to assess the quality of your data, said Gabor, who works at Spotify’s U.S. headquarters in New York. You can immediately see outliers or clusters of data points that may not be realistic based on an analyst’s domain knowledge, she noted. Analysts can follow up on either of those issues and, if necessary, correct for them before beginning formal analysis.

The visual  approach also highlights important aspects of data sets. For example, it shows the “shape” of data, such as whether it has a normal distribution or a long tail in either direction. It can also illuminate correlations between two variables. Of course, correlation doesn’t equate to causation, but identifying potential trends by visually exploring data can lead analysts to examine relationships between variables that they might not have thought to look at otherwise, according to Gabor and other conference speakers.

Peter Gilks, director of product insights for the Spotify revenue team, said during the presentation that any data analysis must stem from a hypothesis or a set of questions a company wants to answer. An analyst could start by just punching in queries written in R or Python — but that approach may lead to missed insights, Gilks cautioned. He said visual data exploration allows analysts to better shape their hypotheses from the beginning by highlighting patterns or trends in the data.

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