Keith Henry, vice president, Manufacturing Global Industry Solutions, for Teradata, a Miamisburg, Ohio-based provider of data warehousing and business analytics products and services, recently talked with IndustryWeek about the growing importance of business intelligence and how manufacturers are making the most of the massive amounts of information at their disposal.

Q: When Teradata was founded 30 years ago, a terabyte-size database was considered large. Today, we're starting to see petabyte-size databases. Just how large is a petabyte, and what does that say about the enormity of information available to manufacturers?

A: A petabyte is almost a number too big to imagine, but it's technically 1,024 times a terabyte, which is one quadrillion bytes of information. To put that into PC terminology, if every PC had a 50-gigabyte hard drive, then you'd need 20,000 PCs just to hold 1 petabyte. And we've got customers now that have five to 10 petabytes and growing. So the information explosion is here.

Q: You've said that the "days of true operational business intelligence are here." What do you mean by that?

A: There's some new thought out there that the real competitive advantage going forward is going to be operational business intelligence (BI). By that I mean the ability to take your transactional and other corporate data that you need and be able to analyze that information and do it in near real-time and then to be able to drive the insight that you get from that operational BI down to the levels of decision makers. Tom Davenport's book "Competing on Analytics: The New Science of Winning" pointed out that companies like Wal-Mart, Harrah's Gaming and Discover [Financial Services] have spent a lot of time and money creating [BI] capabilities and they now consider that a competitive advantage.

Q: How are manufacturers putting into operation the business intelligence available to them?

Keith Henry, vice president, Manufacturing Global Industry Solutions, for Teradata

A: At Ford, the business challenge was the sheer magnitude and the speed of their supply chain. Just a few years ago they were managing 250,000 parts, which would break down into 892,000 SKUs, and all of those parts were flowing through their system at different velocities, and everybody was just focusing on today's backorder problems rather than trying to prevent tomorrow's problems. So [Ford and Teradata] created the data warehouse and put the necessary detailed information into it, and an analytics team worked on predicting and projecting the inventory levels at any point in the supply chain by any part number. They created a predictive alert system in order to notify everybody of critical stockout situations before they occur. Then, operationally, they would recommend priorities down to the line workers so they were able to reduce their inventories, reduce stockouts and improve the overall efficiency of their supply chain.

Another good example is the large grocery store chains, which have become very efficient at maintaining and analyzing the point-of-sale data rolling off their scanners. Increasingly it's daily, but some of the big retailers are now taking that data on a 15-minute interval. And they are literally making real-time decisions and building analytic models looking for patterns of behavior, A in the supply chain and B in the demand chain, or in the consumer behavior.

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