Guesstimates are often essential for market sizing and trending. To be useful, especially where primary data are lacking, they demand a valid conceptual framework. 

Like you, I’m looking forward to the responses to Boris Evelson’s quick Web-based survey, which you can access from his most recent blogpost.It’s always a challenge to assess how truly pervasive BI is—and pervasive it could potentially become.

To generate a valid first approximation, Boris scoped his blog comments and quick survey to “traditional BI” applications (i.e., historical reporting, query, dashboarding). He scoped his estimate only to large enterprise and midmarket firms (i.e., those with 100 or more employees) and only to BI usage in the US.

In order to keep this task manageable, Boris excluded some use cases that are often included in the “traditional BI” category: spreadsheets and other “homegrown” analytics apps; BI embedded in line-of-business apps; and non-interactive, static, published BI outputs. He leveraged both public and Forrester-gathered primary data to gauge how many actual and potential BI users there might be.

Scoping it as he did, Boris estimated that slightly more than 1.5 million people in the US are using traditional BI applications, which is between 2-3 percent of the employees of BI-implementing firms. He suspects the actual percentage might be as high as 6-8 percent of employees, but he’s not sure. That’s why he’s running the Web-based quick survey.

What’s the perfect database management system (DBMS) architecture, where analytics is concerned? It seems as if everybody in business intelligence (BI), data warehousing (DW), and related areas has their own opinion on this topic. In fact, it’s more than just opinions. In the database wars, we have huge communities of vendors and users with substantial investments in one or more approaches—from traditional relational DBMSs to column-oriented, in-memory, dimensional, inverted indexing, and other approaches. 

In the analytic database wars, new architectures are springing up everywhere, each with its own devotees and differentiators, and each with a go-to-market message that takes potshots at established approaches.  The “No SQL” movement is just the latest coalition to emerge in this titanic struggle, and is essentially a loose coalition of diverse approaches rallying around a common theme: that traditional RDBMSs and their SQL-based access languages are unfit, or at least, maladapted to the new world of cloud, social networking, and Web-oriented analytics applications. 

DW industry analysts such as myself are of course embedded in these wars—we’re the neutral observers often caught in the crossfire. Of course, this is not a new battle. The columnar database industry has been around for many years and positioned itself as the chief high-performance, low-footprint analytics contender to  traditional row-based RDBMSs. And columnar has steadily encroached on relational’s turf, not only through established columnar-based DW vendors such as Sybase, Vertica, and ParAccel, but also through recent adoption (albeit in limited fashion) by the likes of Oracle, SAP, and others into their analytics architectures. 

Social networks have their foundations in the space-time continuum—you know, the funky coordinate system  that Einstein was so keen about.

Social network analysis is all about looking for patterns of “proximity” among people, considered in their cultural capacities as influencers and followers, innovators and imitators, first-movers and late adopters. Down deep, I consider social network analysis an important new branch of decision support systems as a discipline. The core question is: What unique situational chemistry causes various people, individually or collectively, to make various decisions at various places and times?

That’s where space and time enter the social network analysis equation. It’s not enough that I look up to your shining example and take my lead from what you say and do. It’s just as important that we be in the same city, neighborhood, or room. More than that, it’s important that you and I actually cross paths in order for you to actively influence me to buy that latte, or for you to calm me down and thereby stop me from storming out the door and severing my relationship with a retailer who has ignored my complaints one time too many.

It would be an understatement to say that data management is a hot topic today.   Master data management, data quality management, metadata management, data integration and data governance have all emerged as high priorities for many global IT organizations. Often times, these data management efforts are paired with investments in business intelligence and facilitated by data warehousing strategies.

Once the strategy, business case, and supporting architectures and organizations are defined (no easy task in and of itself), the next inevitable question is then, which vendors should IT leaders partner with to enable these strategies? There are pure play and best of breed MDM, data quality, BI and DW vendors that offer unbiased, agnostic approaches, eliminating any vendor lock-in or reliance on database platform or enterprise applications. On the other hand, a single platform vendor can offer better ease of integration with existing IT infrastructure than the best of breed alternatives.

These considerations lead us to a major platform vendor, like SAP.  Similar to its mega-platform competitors, IBM and Oracle, SAP offers a deep and wide set of data management, BI and data warehousing solutions that promise not only integration within these products, but more notably - across its broader product portfolio of enterprise applications.