I love reporters. As someone with an M.A. in journalism who then evolved into an analyst, I recognize that both professions occupy approximately the same tier in the industry food chain. In fact, many IT industry analysts were trade press reporters at one point in their careers, and it’s not uncommon for analysts to go back into media institutions later on.

When great longtime IT reporters, such as Computerworld’s Jaikumar Vijayan, call me up to get my thoughts, I’m just as interested in their take on what’s important. Jai recently published an excellent article with my predictions, plus those of another analyst, on the year ahead in analytics. To the jaded reader, these sorts of year-end look-ahead articles may feel like perfunctory rehashes of stuff we’ve been telling them for quite some time, perhaps with a trendy new buzzword thrown in to keep it remotely glance-worthy.

I try not to repeat myself too much. Rather than regurgitate the statements I made in the phone interview with Jai, I’ll highlight how I’m addressing the principal business-analytics trends that I discussed with him — self-service, pervasive, social, scalable, cloud, and real-time—in our 2011 Forrester research agenda:

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.

In the analytics wars, one of the quasi-metaphysical topics I try to avoid is debating the distinctions between “information,” “intelligence,” and “insight.”

Social networks have always been with us, of course, but now they’ve gained concrete reality in the online fabric of modern life.

Social network analysis has, in a real sense, been with us almost as long as we’ve been doing predictive analytics. Customer churn analysis is the killer app for predictive analytics, and it is inherently social. It’s long been known that individual customers don’t always churn themselves—i.e., decide to renew and/or bolt to the competition—in isolation. As they run the continual calculus called loyalty in their heads and hearts, they’re receiving fresh feeds of opinion from their friends and families, following the leads of peers and influencers, and keeping their fingers to the cultural breeze. You could also make a strong case for social networking—i.e., individual behaviors spurred, shaped, and encouraged within communities—as a key independent variable driving cross-sell, up-sell, fraud, and other phenomena for which we’ve long built predictive models.

The other day, a Forrester client was asking me for educated guesses on how fast the average enterprise data warehouse (EDW) is likely to grow over the next several years, and as I was working through the analysis, I couldn’t avoid the conclusion that social network analysis—for predictive and other uses—will be an important growth driver (though not the entire story). I’d like to lay out my key points.