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Tracking Big Data’s Progress, Adoption

January 22, 2014

Tom Davenport is carving an even deeper niche of expertise on big data and analytics with his most recent books. Last fall he published a primer of sorts, (co-authored with Jinho Kim), Keeping Up with the Quants and a related article in Harvard Business Review – which probably opened some doors toward better understanding of analysts and why their role is so critical.

We know that Tom is a prolific writer—he’s written or co-authored 15 best-selling business books, several building on his pioneering work on data analytics, Competing on Analytics—now, his work is continuing to dive into the subject.

In his forthcoming book, Big Data at Work, Dispelling the Myths, Uncovering the Opportunities, Tom offers dozens of company examples, including UPS, GE, Amazon, United Healthcare, Citigroup, proving that big data has grown up and is more than hype.

It’s a progression that Tom has spoken about for the past few years. In 2012, Tom descdribed a “five-tier analytics pyramid, from least mature to most mature” that most businesses can align with.

“At the base is Stage 1, those who are “analytically impaired;” Stage 2 is for “localized use;” Stage 3, are those with “analytical aspirations;” Stage 4, is for “analytical companies,” and Stage 5 is reserved for real “analytical competitors.” Most businesses are at Stage 2, where data is still used locally, not shared among partners.
A few leading-edge companies such as P&G, are at Stage 5. It was at Stage 4 for a long time, but CEO, Bob McDonald, pushed it ahead, Davenport said. Delta is another leader in analytics, but like most others, it is still using “small data” resources. That is the case even for analytics “old-timers” like Marriott, Royal Bank of Canada and UPS as well as companies that have turned around, like Caesars, Tesco and MCI. ..LinkedIn is much farther along the curve and knows that “Web 3.0 is about data.” Eventually, Davenport expects all businesses to find benefits in analytics, especially as they scale up to greater data volumes.”
By last year, many more businesses were moving up the pyramid and refining their goals. As Tom told those who attended the MIT CDB conference last May:

“The goals of big data vary among enterprises. Some, like Macy’s and Caesars, want to make business decisions faster. Others, like Citi, want to make decisions at lower costs. And still other companies, like United Healthcare, are looking to make better decisions with more data. The final group, including GE and Novartis, want to sue informatics for developing new products and service innovation.”

I’m finding it interesting to watch the progression from limited business efforts and one-off projects to really using big data and analytics to spur businesses into new fields and competitive advantages.”

You can read more about Tom’s latest ideas on how CIOs can learn from the Quants here. Also see my recent Q&A with him where he explains why businesses still struggle to wring value from their big data efforts.

How well is your enterprise mining big data? What are your biggest challenges? We’d like to hear your ideas and comments.