Brett Sheppard briefed author Cindi Howson in The Briefing Room to discuss Tableau’s data discovery tools. …
Brett Sheppard briefed author Cindi Howson in The Briefing Room to discuss Tableau’s data discovery tools.
Visual data discovery seems to be all the rage this year with new products and high-growth companies. What’s driving this interest – the pretty pictures or the self-service? And will this new category of tools finally take business intelligence (BI) mainstream or are we simply trading spreadsheet chaos for another kind of chaos?
There is still a fair amount of confusion about what is visual data discovery and what it is not, so I’ll start with a definition:
Visual data discovery tools speed the time to insight through the use of visualizations, best practices in visual perception, and easy exploration. Such tools support business agility and self-service BI through a variety of innovations that may include in-memory processing and mashing of multiple data sources.
Some befuddled BI teams though are shrugging their shoulders and asking, “Isn’t that what ad hoc business queries were supposed to do?” Well, yes, to a degree. Two of the biggest differences in business query tools and visual data discovery tools are the use of graphs and the degree of user autonomy. In a business query tool, a user can certainly add a bar chart to a dense page of numbers. But the chart is an after-thought. In fact, according to a TDWI survey last year, users spend two-thirds of their time analyzing data in tabular versus chart form. This may be appropriate when you need a precise number (How many widgets do we have on hand?), but not when you are trying to identify patterns, trends, and anomalies. With visual data discovery tools, the query and visualization process are one in the same. Drag a time period onto the page and up pops a trend line. Add a product category, and perhaps that trend line is now automatically converted to a trellis or small multiple chart. Research has shown that when data is represented graphically, we use less cognitive resources to make a decision and retain information better. So these graphs are more than just pretty or engaging; it’s about speeding the time to insight.
The other big distinction with visual data discovery tools from business query tools is the degree of user autonomy. Business query tools generally require a metadata layer that IT will often design and build. This metadata layer provides a layer of abstraction from the physical database schema with potentially hundreds of tables. With a visual data discovery tool, business users are often working with a subset of data, either a flat file or spreadsheet, so IT is not a bottleneck. If a real-time query is involved, the visual data discovery tool may automatically model a metadata layer, giving its best guess at what’s a metric and what’s a dimension, again with little to no IT support. Somebody would have to write the SQL for the initial query and define the joins, but once extracted, the data is often loaded into an in-memory engine.