Tableau explains your data with new natural-language tool

0 Posted by - 19th September 2019 - Technology

Self-service analytics tools have long empowered users to produce data visualizations without the need for IT intervention. Recent advances, such as data prep automation, have further lowered the barrier of entry, but this push to democratize analytics surely has its limits. After all, users still have to interpret the data visualizations they produce.

Tableau’s 2019.3 software update, released Wednesday, aims to address this issue with a new feature called Explain Data that seeks to tell the story behind the chart, delivering analysis in clear language to those without the statistical expertise to do it for themselves.

In a way, Explain Data is the counterpart to Ask Data, which Tableau included in its 2019.1 release in February. Ask Data enables Tableau users to describe in a chat window the visualizations they want to see. It then takes this natural language input, uses it to construct a visualization, and allows users to refine the chart or to layer on more data interactively.

With the new feature, users click on a data element in a visualization — one point in a scatter plot, say, or one bar in a time-series chart — and then click on the “Explain Data” icon. A dialog box appears offering one or more explanations for why that element differs from the others, or deviates from the trend. Tableau does this by testing hundreds of patterns and potential explanations, presenting only the most statistically significant.

In a demonstration of the tool, Tableau’s Chief Product Officer François Ajenstat pulled up a visualization of usage of a Boston bike share service by month. February is a slow month for cycling — it’s cold in Boston — but there’s more to it than that, as Explain Data revealed: In February, a disproportionately high number of the rides were around Cambridge. Students, it seems, will cycle whatever the weather. Armed with such insights, the bike share company could shelter most of its bikes from snow and salt, leaving the rest where they are most likely to be used through the winter.

Explain Data can also identify the reasons behind outliers on a scatter plot — in Ajenstat’s demo, revealing that a single month-long ride was the reason for an abnormally high average ride duration for one rental station.

read more at by Peter Sayer