The business best seller Competing on Analytics made the case that there are big rewards for organizations that embrace data-driven decision-making. Offering a preview of his soon-to-be-released follow-up, Analytics at Work: Smarter Decisions Better Results, co-author Thomas H. Davenport , professor of information technology and management at Babson College, recaps the book's five-stage "DELTA" model for assessing and improving analytical decision-making.

What was the impetus for your new book, and why was a follow-up needed to Competing on Analytics?

I wrote the original "Competing on Analytics" article in 2006, and much to my astonishment it was the number-one reprint for Harvard Business Review that year. Then when Jeanne Harris and I released [the book] in 2007, it sold more than 100,000 copies and ended up being translated into 13 languages. Clearly the world wanted more on this topic, but we discovered that while many were interested in this idea of how you compete on analytical capabilities, there were a lot more people who said, "Our organization doesn't want to build its entire strategy around analytics, but we would like to become more analytical and make more analytical decisions." So Analytics at Work is a broader book on how you create and promote analytical capabilities even if you don't expect to be a Harrah's, Capital One, Progressive Insurance [or one of the other strategically analytic companies detailed in our first book].

"Analytics" is certainly becoming a big buzz phrase for vendors. But is developing analytic capabilities a matter of acquiring technology or does it have more to do with developing skills and expertise?

Technology is important, and it's good that it's increasingly available. But the book talks a lot more about the non-technical factors that go into developing analytical capabilities. Data, for instance, is closely intertwined with a lot of technical issues. But there are also a lot of data governance issues and challenges that aren't particularly technological. In our original research for Competing on Analytics, we found that the companies that are really good at analytics are doing a lot more than just buying technology from SAS or MicroStrategy or [SAP]BusinessObjects. You're starting to see that with the rise of services, as well. IBM and Accenture, for instance, have announced that they are building up their analytical consulting capabilities dramatically. So people certainly realized that it's not just a matter of technology.

What's your definition of analytics, and how would you distinguish them from business intelligence?

I think of analytics as a subset of BI based on statistics, prediction and optimization. The great bulk of BI is much more focused on reporting capabilities. "Analytics" has become a sexier term to use -- and it certainly is a sexier term than "reporting" -- so it's slowly replacing "BI" in many instances.

So how do companies develop statistics, prediction and optimization capabilities without hiring people who have been trained to do those things?

That's really tough, and that's why the "A" in our DELTA model is for "analysts." The key factor you find in companies that are really good at analytics is that they have a lot of really smart, analytical people. The companies that can attract those people -- whether they are from this country or other countries -- are going to be the ones that are most successful. There is also a fast-growing industry to outsource analytical capabilities to places like India, China and so on.

Some vendors say we can bake analytics into software and systems while others say that prebuilt analytics don't tend to yield innovation or competitive advantage. What's your stance on that debate?

There is a long-term trend toward what some describe as operational BI or the embedding of analytics into transaction systems. But even that is going to require smart people within your organization. You see it in companies that are putting in SAP Advanced Planning and Optimization (APO) and Supply Chain: Ultimately you may end up needing fewer people, but they have to be twice as smart. I don't think there's much doubt that to make analytics succeed, you're going to need smart people. You could argue that one of the reasons we got into trouble in this financial crisis is that we had all these automated systems for mortgages and so on and not enough people who could monitor them over time and explain them clearly to managers.