I’m on two overlapping posting kicks, namely “lessons from the past” and “stuff I keep saying so might as well also write down”. My recent piece on Oracle as the new IBM is an example of both themes. In this post, another example, I’d like to memorialize some points I keep making about business intelligence and other analytics. In particular:
- BI relies on strong data access capabilities. This is always true. Duh.
- Therefore, BI and other analytics vendors commonly reinvent the data management wheel. This trend ebbs and flows with technology cycles.
Similarly, BI has often been tied to data integration/ETL (Extract/Transform/Load) functionality.* But I won’t address that subject further at this time.
*In the Hadoop/Spark era, that’s even truer of other analytics than it is of BI.
My top historical examples include:
- The 1970s analytic fourth-generation languages (RAMIS, NOMAD, FOCUS, et al.) commonly combined reporting and data management.
- The best BI visualization technology of the 1980s, Executive Information Systems (EIS), was generally unsuccessful. The core reason was a lack of what we’d now call drilldown. Not coincidentally, EIS vendors — notably leader Comshare — didn’t do well at DBMS-like technology.
- Business Objects, one of the pioneers of the modern BI product category, rose in large part on the strength of its “semantic layer” technology. (If you don’t know what that is, you can imagine it as a kind of virtual data warehouse modest enough in its ambitions to actually be workable.)
- Cognos, the other pioneer of modern BI, depending on capabilities for which it needed a bundled MOLAP (Multidimensional OnLine Analytic Processing) engine.
- But Cognos later stopped needing that engine, which underscores my point about technology ebbing and flowing.
I’m not as familiar with the details for MicroStrategy, but I do know that it generates famously complex SQL so as to compensate for the inadequacies of some DBMS, which had the paradoxical effect of creating performance challenges for MicroStrategy used over more capable analytic DBMS, which in turn led at least Teradata to do special work to optimize MicroStrategy processing. Again, ebbs and flows.
More recent examples of serious DBMS-like processing in BI offerings may be found in QlikView, Zoomdata, Platfora, ClearStory, Metamarkets and others. That some of those are SaaS (Software as a Service) doesn’t undermine the general point, because in each case they have significant data processing technology that lies strictly between the visualization and data store layers.
Related link
- Context for this post may be found in my piece on The two sides of BI. (August, 2013)
