Exclusive Interview: Doug Laney on Big Data and Infonomics

Doug Laney is vice president and distinguished analyst with the Gartner Chief Data Officer (CDO) research and advisory team. Doug researches and advises clients on information monetization and valuation, open and syndicated data, analytics centers of excellence, data governance and big-data-based innovation. He is the author of Gartner's enterprise information management maturity model and is a two-time recipient of Gartner's annual thought leadership award. I recently had a chance to catch-up with him.

Gregory Piatetsky: You are well known for coming up with 3V of Big Data (Volume, Variety, and Velocity) in 2001. Gartner dropped Big Data from its Hype Curve in 2015. Is Big Data still an important concept ? If so, how many Vs do you see now (others have added more Vs, including Veracity, Value, ... )

Doug Laney: Big Data is still on some of the Gartner Hype Cycles, just coming out of the "trough of disillusionment" and approaching the "plateau of productivity." Note that we publish multiple Hype Cycles for various technology domains. So, yes, Big Data is more important than ever--just that it has become a more mainstream concept. Most high-value analytic solutions today involve an aspect of Big Data.

As for the three V's, velocity is becoming much more of a factor as organizations look to make more and more operational decisions or automate processes in real-time. Yes, others have suggested other V's like veracity, but these are not measures of bigness, so are not really definitional characteristics of Big Data. Nonetheless, they are important considerations for most data. In fact, some colleagues and I came up with 12 Vs of data that can be used to ensure various aspects of data are managed and leveraged appropriately.

GP: You recently came up with another breakthrough idea of Infonomics, which is the discipline of monetizing, managing, and measuring information, and wrote a book on Infonomics. Can you explain it a bit?

DL: Infonomics is the concept that information is, or should be, an actual enterprise asset. Even though antiquated accounting standards still do not recognize it as one, it clearly meets the criteria. That is, information can be owned and controlled, is exchangeable for cash, and generates probable economic value. Infonomics posits that irrespective what the accountants say, it is increasingly incumbent upon organizations to treat information like an actual balance sheet asset.

Monetizing information as an asset is about deploying it in a variety of ways to generate economic benefits. This can range from licensing it to others, to using it to improve top or bottom line results via process improvements. Generating measurable economic benefits from or attributable to available information assets.

Managing information as an asset involves applying traditional asset management principles and practices to information. This can involve adapting physical, financial, human capital, or other asset management methods. And measuring information as an asset is about gauging an information asset's quality characteristics, business relevance, impact on key performance indicators, along with applying traditional accounting valuation methods such as the cost-, market- and income approaches.

A key theme of Infonomics is that

"you can't manage what you don't measure, and you can't monetize what you don't manage."

Unfortunately, too many organizations fail to measure and manage information with the same discipline as other recognize assets, so they fail to generate sufficient value from them.

GP: Can you name some companies that are very successful in monetizing information, and some that are the laggards?

DL: Some companies like Kroger, Rite Aid and Dollar General are known to license data outright, along with most telcos. Many companies barter with information for B2B discounts or favorable terms and conditions. And companies like Vivint and MISO Energy among others have used infonomics valuation models to identify and generate millions of dollars in new economic value for their businesses. My book includes dozens of examples, and at Gartner we have a compilation approaching some 500 real-world stories. Laggards are typically those organizations in industries where transaction volumes are low, like manufacturing and government.

GP: You write that "Analytics is The Engine of Information Monetization." What are the most promising analytics methods here?

DL: We find that greater economic benefits from information are generated when it is used for diagnostic, predictive or prescriptive purposes, rather than simply producing reports, spreadsheets, or bar charts. And we're seeing more and more of these use cases include machine learning, and text and multimedia analysis. No longer can many organizations afford to hand-craft fully-optimized static analytic models. Business moves too fast and business environments change too fast, so adaptive learning models are increasingly critical.  

Read the rest of the interview on KDnuggets:

Exclusive Interview: Doug Laney on Big Data and Infonomics-

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