Interest in advanced analytics is a good thing, says Stephen Brobst, CTO of data warehousing vendor Teradata. But don't expect much value from prebuilt analytic applications and simplistic spreadsheet analyses, he warns. To gain broader access to truly valuable insight, Brobst says we have to move away from walled-off warehouses and programming-driven analysis tools.
InformationWeek: There's growing interest in advanced analytics, but the shortage and expense of expertise has led many vendors to offering prepackaged industry solutions and applications. What are your thoughts on prebuilt analytic applications?
Stephen Brobst: Industry data models absolutely make sense. Customers and vendors are both moving in that direction because you don't need to build an industry data model from scratch. You can also prepackage analytics -- things like reports, key performance indicators and balanced scorecards -- but that's not where the value is. If look at truly innovative companies, let's take eBay as an example, 85 % of the resources they use for their data warehouse are for answering new questions. The answers to questions you already know are cheap because you can prepackage them and optimize the queries. You may have to keep running those report for regulatory purposes or just to keep your fingers on the pulse of the business, but once you run them a few times there are no new insight.
In my view, you want a set of tools that will let you ask questions you haven't asked before yet get answers efficiently without having preoptimized the query. If something is preoptimized, that inherently means that you already knew the question and the probable answers to the question.
InformationWeek: That assumes you have analytic experts available to ask new questions. Hasn't the scarcity and cost of expertise limited the use of advanced analytics?
Brobst: I think there's a cultural change going on whereby the MBAs coming up from the top universities are more quantitatively capable than they used to be. They are comfortable using analytic tools. The problem is that the tools they learn tend to be Excel and variations of Excel. It's better than the old style of qualitative decision making; they are data driven, but they don't really know how to do deep analytics. It's sort of spread-mart analytics, not tools that would enable you to do things like predictive forecasting beyond a linear regression you could do in Excel.
Excel is the most common BI tool in the world, but there are all kinds of governance challenges with spreadsheets. And though spreadsheets may be easy to use, they're not an example of best-in-class analytic capabilities. We have to raise the bar a little. The level of analytic sophistication is rising, but we have to get people out of the mindset that a spreadsheet is the answer.
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