Philip Russom opines (emphasis mine):
What’s driving change in data warehousing (DW) and business intelligence (BI)? There are obvious scalability issues, due to burgeoning data, reports, and user communities. Plus, end-users need more real-time and on-demand BI. For many organizations, integrating existing systems into DW/BI is a higher priority than putting in new ones. And the “do more with less” economy demands more BI at lower costs. Hence, most drivers of change in BI and DW concern four Mega-Trends: size, speed, interoperability, and economics.
Depending on which universe of enterprises and vendors you’re looking at, Philip’s claim of “most” may be technically true. But from where I sit, Philip omitted two other crucial trends: new kinds of data and increased analytic sophistication.
A year ago, I divided data into three kinds:
- Human/tabular, which is what Philip’s comments seem to be focused on.
- Human/nontabular, e. g. what is best handled via text analytics.
- Machine-generated, such as web log or sensor data.
Most organizations on the planet could benefit from better understanding or exploiting their human-generated tabular data. But even so, many of the best opportunities to add analytic value come from capturing and analyzing fundamentally newer kinds of information.
I further would suggest that analytic sophistication is going up, for at least two reasons:
- New kinds of data call for or at least allow new kinds of analytics.
- Better price-performance (on bigger data sets) allows for more sophisticated analytic techniques.
Some of the best examples of these trends, especially the second one, may be found in what I recently called analytic profiling.