Every Forrester Wave is an in-depth snapshot of an entire vendor market segment at a particular point in time. 

Yesterday was that point in time for the latest update to the Forrester Wave for Enterprise Data Warehousing (EDW) Platforms. We just published this update after a grueling 8-month process of revisiting the criteria, scales, and weights associated with the most differentiating features of EDW vendors’ complex solution value propositions. Clearly, the EDW market has evolved considerably in the 2 years since we published the first installment of the Wave. 

At the highest level of analysis, what’s new? For starters, more vendors made the crucial inclusion criterion of having at least 100 customers with in-production EDW deployments. The field this time around included all seven vendors in the 2009 Wave, plus Greenplum (now part of EMC) and Vertica. Clearly, big-brand EDW mergers and acquisitions consolidated the original seven down to 5, as IBM acquired Netezza and SAP purchased Sybase. Please bear in mind that, as these acquisitions were reasonably recent, we chose to evaluate the acquired vendors’ offerings separately from their new parents' pre-acquisition EDW offerings in this latest Wave (it's worth noting that neither IBM nor SAP has slightest intention of discontinuing their pre-acquisition EDW portfolios--if anything, they're both now evolving those product families even more aggressively post-acquisition). 

 The Twittersphere keeps chirping with definitional disputes about what exactly constitutes an enterprise data warehouse (EDW). This is the sort of debate that we geekier analysts love to engage in, since it gives us a chance to beat our chests and brandish our superior powers of cogitation. 

Since I have exposed skin in this game, I’ll flex my cognitive muscle a little bit more for those who wonder what Forrester’s position is on all this. Given that my update to the Forrester Wave™ for EDW platforms will come out in a month or so, this is probably a good time to level-set the discussion. In this post, I will also point to some trends that are pushing the boundaries of what an EDW is and can do for you. 

Some have argued that an EDW requires a DBMS, but is not, in itself, a DBMS. I’ve heard it said that a DBMS only becomes an EDW when it incorporates a schema and stores data. Still others argue that an EDW is something entirely distinct from a DW (without the “enterprise”) modifier, a data mart, or an operational data store (ODS). 

I find all of these perspectives hairsplitting and misleading, in that they blur the actual distinctions among these architectural constructs. Paradoxically and obliquely, though, they all hint at the rapid evolution of the EDW into something more protean and virtualized. 

Can you feel it? The Twittersphere is spinning faster on its axis, anticipating the next Forrester TweetJam, Wednesday, December 15, 12:00 p.m. to 1:00 p.m. US Eastern Time. Our topic will be “Advance Your Analytic Strategies.” We’ll have a Forrester tweeting squad that includes yours truly plus any or all of the following Forrester colleagues: Rob Karel, Boris Evelson, Clay Richardson, Gene Leganza, Noel Yuhanna, Holger Kisker, Leslie Owens, Suresh Vittal, William Frascarelli, David Frankland, Joe Stanhope, Zach Hofer-Shall, and Henry Peyret.

Clearly, advanced analytics is an important theme and huge space that sprawls across many Forrester analysts’ focus areas. What I’m about to present are the humble opinions of one Forrester analyst — c’est moi — for whom this is the heart and soul of his focus going forward. These thoughts, presented under each of the proposed TweetJam questions, will give you a foretaste of what I’ll tweet on that session in just a few weeks: 

• What exactly is and isn’t advanced analytics? 

IBM’s acquisition of Netezza was a must for both vendors if they wish to grow their shares of the data warehousing (DW) market. They have a common archrival, Oracle, that is fielding an increasingly formidable appliance-based product portfolio, threatening both vendors’ long-term positions in this dynamic market. This announcement comes the same week as the annual Oracle OpenWorld conference, and we await word this week from Larry Ellison, Mark Hurd, and company on how they intend to respond (and you better believe they will).

First, here are the facts we have so far on IBM’s acquisition of Netezza. Big Blue is buying the publicly traded Marlborough, Massachusetts-based pure play for $1.7 billion. The deal, which represents $27 a share, 9.8% over Friday’s closing price and a near 85% premium over its 3-month trading average—is set to close in the fourth quarter. Netezza was founded 10 years ago, has around 350 customers in all major vertical industries, and employs around 500 personnel. Netezza has extensive partnerships with complementary technology providers, including, most notably, with IBM, which provides the hardware underlying its TwinFin DW appliances.

Predictive analytics is not just about forecasting what’s coming down the pike. It’s also about keeping the bad alternative futures from happening. If you can see the nasty things that might happen far enough in advance, you have a better chance of neutralizing or squelching them entirely.

In fact, many real-world applications of predictive analytics are “interdictive,” a term often used in military and law enforcement contexts to refer to tactics that delay, disrupt, or shut down an adversary’s forces or supply routes before they can do damage. Anti-fraud is one of the principal interdictive applications of predictive analytics technology. Companies everywhere rely on data mining to determine who’s been engaging, alone or in groups, in stealing money, supplies, finished goods, cellular airtime, and other valuables — and also where they’re likely to strike next. Likewise, anti-terrorism efforts rely on predictive models to sift through massive collections of historical and real-time intelligence in a Jack Bauer-like race against time and imminent disaster. You best believe that social network analysis is a key weapon in your arsenal for predicting and interdicting these sorts of malignant social patterns.