For years vendors and pundits have heralded the age of self-service business intelligence (BI) thanks to the arrival of the latest generation of BI tools. First, they talked about how dashboard, OLAP and ad-hoc query were going to enable business people to create their own reports; now they proclaim that data discovery and data visualization tools are the answers to all your problems.
While it’s true that each new generation of BI tools is more powerful and easier to use, when you get past the hype and look at real implementations, you’ll see that the most pervasive BI remains the spreadsheet (which is often embedded in data shadow systems or spreadmarts).
There’s nothing new here. People have been writing about the limitations of self-service BI for years:
- Eckerson: Self-service business intelligence not a give-and-go affair, 2013
- The Elusive Promise of Self-Service BI, Sep 14, 2009
- Self-service business intelligence tools failing to make headway at most organizations, Jan 27, 2009
- Moving BI to the Enterprise, Sep 1, 2005
What does the failure of self-service BI mean for Big Data? Big problems. Unless self-service and pervasive BI become the norm, enterprises will never productively tap the business ROI from Big Data no matter how much they invest in it.
If enterprises are to implement self-service BI, whether for Big Data or the traditional data in their enterprise, then they have to understand what it is and what it is not. Once enterprises understand what they are trying to achieve, then they are likely to do things differently to avoid the traps that each wave of self-service efforts have encountered.
Stay tuned, my next few posts will explore these issues in more detail.