Data: Inventory vs. Insights

Many BI veterans and industry observers are mystified that lots of BI engagements start with data inventories, as we’re reminded here:

Having terabyte upon terabyte of information is irrelevant if that data is unrelated to current business issues. The problem with a data inventory activity is that identifying and counting data elements in different systems and applications won’t necessarily solve any problems. … The point of a data inventory isn’t to pick through data because it exists, but to inventory the data people actually need.

The key is to figure out a way to make the exercise worthwhile and useful.  If the inventory results in answers to questions the business isn’t asking, then the cost and effort of an inventory will be difficult to justify. Business value arises when a team starts with an understanding of the type of questions the business is asking, and then investigates how data assets can be leveraged to get answers and actionable insights. That is, the inventory should be focused on ensuring the right data is available to answer important questions, and identifying additional data that may be needed.

As relevant data assets are investigated and profiled, they absolutely should be cataloged to avoid re-inventing the wheel on the next project and minimizing the effort required the next time an executive orders up an inventory. This “just-in-time inventorying” approach ensures that already-strained teams don’t waste effort on legacy data assets that are only of historical (and perhaps regulatory) interest and provide no answers or clarity to  support current business decisions.