Big data experts say accounting rules need to catch up to the fact that information has value that should be reflected on a company's books.
13 Big Data Vendors To Watch In 2013
(click image for larger view and for slideshow)
Business leaders who want to start valuing their data with the same rigor and discipline they apply to physical assets are being stymied by out-of-date accounting practices, a panel of experts told a meeting of the prestigious Executives' Club of Chicago earlier this week.
Information has a discernible value -- one that should be accounted for on a company's books, according to Doug Laney, VP, analytics & information management at Gartner. Laney is best known for his influential research note in 2001 that framed big data in terms of the three V's: Volume, Velocity and Variety.
Unfortunately, Laney declared, accounting practices have not caught up to this need. He noted that after the 9/11 terror attacks, insurance companies denied claims related to data loss because the companies hadn't put these assets on their balance sheets. The current financial accounting standard still "explicitly excludes" data, Laney said.
The situation may be somewhat better internationally. Introducing the panel, Bob Kress, Accenture's executive director, global IT audit, observed that "data" was added as an asset class during the World Economic Forum Summit in Dubai in November.
Laney said Gartner clients lately have been asking not only how they can become more information-driven but how they can generate revenues from their own data assets. Some of the "dark data" -- information a company captures but doesn't use -- can be "spun into gold," he said.
Panelist Michael Connor, VP, enterprise data architecture at insurance giant QBE North America, said data was valued, in general. But echoing Laney, he said different types of data and their relative worth were not being captured from an accounting standpoint.
He suggested today's businesses need a more nuanced way to account for the value of different data assets. "The revelation for manufacturing accountants in the late '80's and early '90's centered on properly assigning re-work dollars proportionately to the appropriate line of business," Connor explained in an email. "For the auto industry, as an example, it implies that certain cars (e.g., lines of business) looked profitable because all cars (LOBs) shared in re-work dollars equally. However, the reality was that the certain line of cars deserved 90% of the costs."
Whether you want it or not, the amount and variety of data are expanding exponentially, said panelist John Lewis, president & CEO, consumer group, NA, at Nielsen. Lewis urged the audience of about 150 to "embrace that trend" and transition their organizations to "understand information is a competency" that needs the right people, processes and platforms. Later, he said that executives who say the big data phenomenon is being overhyped "are behind and will fall further behind."
Asked where organizations err in their data approach, Lewis said a common mistake was "falling in love with your own data" and failing to incorporate external sources, such as industry or consumer data. "You'd be surprised how common that is," he said.
For Gartner's Laney, it's a mistake to use exclusively data events. Gartner has defined four levels of business intelligence sophistication: descriptive, diagnostic, predictive and prescriptive. In the final, prescriptive stage, Laney said, "[companies can] take insights and predictions to make something happen."
Predictive analysis is getting faster, more accurate and more accessible. Combined with big data, it's driving a new age of experiments. Also in the new, all-digital Advanced Analytics issue of InformationWeek: Are project management offices a waste of money? (Free registration required.)
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.
InformationWeek Must Reads Oct. 21, 2014InformationWeek's new Must Reads is a compendium of our best recent coverage of digital strategy. Learn why you should learn to embrace DevOps, how to avoid roadblocks for digital projects, what the five steps to API management are, and more.