In this excellent post Davenport outlines what it takes to succeed in Big Data projects. Contrary to popular belief you don’t need an army of Math PhDs to kick-start your Big Data Program. Some smart thinking on selecting technology and assembling team along with good Project Management will go a long way in making it successful.
In Davenport’s own words
Of course, in addition to good project management, and the other factors, you need some good fortune. Big data projects involve new technology and new development approaches, and are inherently risky. And if you’re doing significant data exploration or discovery with big data, you will occasionally fail—which is not really a problem if you learn from the failures. Big data projects are still more like R&D than production applications. But those organizations that combine conventional project management wisdom with some of the big data wrinkles will have a leg up on success.
This is in line with what ThrivOn sees in the market with its own Big Data implementations. Although it is Big Data, the mantra should be start with a small business use case, iterate a lot and be ready to fail and start again. Its better for iterations to fail rather than the entire project.