Interesting quotes and links about economic condictions and BI

"BI has remained a number-one priority in good times and bad for the last three or four years. Whether you're reading Gartner or a variety of other people who track these things, people are just trying to do everything they can to have better operational efficiency," he indicates. "They need to grow their sales and cut their staff and slash expenses at the same time. Look at Starbucks. They just said they want to close something like 400 stores. Obviously, they want to keep the profitable ones open and close the unprofitable ones. They want to know where they can best allocate resources to open new stores. Only business intelligence can give them that kind of insight."

Is Open Source BI at a Tipping Point?
11/5/2008
By Stephen Swoyer
http://www.tdwi.org/News/display.aspx?ID=9207

The building blocks of (and biggest impediments to) analytic success, proponents say, are the same people and process issues that frustrate all would-be transformative efforts. You must understand your business processes. You must have done the infrastructure heavy lifting to optimize those processes (by delivering timely connectivity to operational systems or by ensuring the quality of the data that you're feeding into your analytic pipeline). You must have creative, talented folks on staff. Without these elements, even best-in-class analytics (with the most elegant data models or smartest algorithms) won't help you.
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"Our point of view is grounded in convergence -- the convergence of analytics, business processes, and the IT architecture. No longer can we approach our customers by only focusing on analytics; we have to broaden our view a bit and look at the bigger picture -- [such as] what is the existing business process that we're looking to optimize, or how can we integrate predictive analytics into the IT infrastructure that supports the business process," says Chris Hamlin, vice-president of enterprise solutions with statistical analysis powerhouse SPSS Inc.
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"For our customers, that means understanding those [business processes and] making investments in really smart people, people with enterprise architecture experience, [and] people who understand Web infrastructure."
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"It starts with the data itself. You're always measuring things. Stepping back, you need to look at what you're measuring. Are you measuring what matters most? What else could you measure? Where do you perhaps have a blind spot?" says Ann Milley, senior director of analytics strategy with SAS.
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It's tempting, Milley says, to sift through everything. There's a finite amount of what she calls "analytic bandwidth," however. "There's a lot of data out there, so … what it comes down to is a case of analytic bandwidth. There's a shortage of analytic talent that can really help you tap into the value of your data. If you're measuring everything -- if you're not trying to understand what you're measuring and prioritize it accordingly -- you're going to saturate that bandwidth."
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Analytic talent is a rare and pricey commodity -- one that tends to be difficult to attract (and considerably more difficult to retain) during times of economic tumult. The paradox of economic hardship, as Gartner Inc. and other industry watchers like to point out, is that companies that have the means will in many cases pay more to acquire and retain top IT talent.
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"Sometimes it's a good thing to outsource. Bring in a fresh perspective and a new set of eyes. Most of our Global 2000 [customers] typically have some analytic expertise, but you've still got departments where analytics isn't as widely adopted," she says. "Most companies know that they've got to do … something about the problem that they want to solve. If it's just a matter of analytic bandwidth, it might be time to say, 'Let's go outside of our organization and tap into someone else.' It might be temporary, but if we get good results, it might be project after project."

There's much to be said for a fresh perspective, she argues. "We get cases where [customers have] brilliant model builders but someone else will say, 'You know what? This is the way we've been doing it for a long time. We just want you to come in and take a look at our model and our approach and tell us if there's anything we can do to make it better.'"

There's a further wrinkle here. According to SPSS' Hamlin, one goal of any analytic investment should be to make it pervasive: to expose it to as many consumers as possible and to present it in such a way that it can meaningfully influence (and in many cases drive) decision-making. In this regard, then, analytic technology must above all be usable, either on a standalone basis (via a spreadsheet or analytic workbench, for example), or embedded in third-party software.
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"One area that we've paid a lot of attention to is how we deploy analytics or guidance around a decision, or how we integrate [analytics] around a business process. That means embedding analytics as part of the process" she asserts.

"If I am a call center agent for an insurance company and I have to walk a customer around a series of questions to help them determine how to handle their claim, I'm not focused on the predictive process that's going on in the background [that] drives those questions and determines where to route that data," Hamlin continues. "A real focus for us has been how do we prepare an environment and build an environment where predictive analytics are being deployed seamlessly into where people already work?"

Analytic Insight: It's All About People and Processes
11/12/2008
By Stephen Swoyer
http://www.tdwi.org/News/display.aspx?ID=9212