Big Data Quotes of the Week

“It’s great to see [data science] maturing, and this new focus will lead to data applications which are not just more powerful, but more reliable and more impactful as well. Data Science has come of Statistical age.”–David Smith, Revolution Analytics  

“Today, Facebook makes more than 90 percent of its revenue from online ads and Zynga games. It’s conceivable that, as online ad revenue might never quadruple on a per-user basis, Facebook builds a big-data platform that skims aggregated information off its user base and sells the results as a financial service. That’s a vision of Facebook, big-data service provider.”–Derek Thompson, senior editor, The Atlantic

“Of course, there are many other fixes required to make the health care market look like the markets for other goods and services in our economy — like transparent pricing and better information about quality of services. But enabling researchers to have access to data about patients — much as Google, Facebook, Amazon, and many other retailers have data about our buying habits — is a critically important, but so far overlooked, early step. Lives and money are at stake.”–Robert E. Litan, vice president. research and policy, the Ewing Marion Kauffman Foundation

“Intuit collects a lot of data on more than 5 million Mint users. Intuit calls it ‘data for delight.’ The service becomes about more than just tracking your spending, but becomes a tool for comparison. Users can compare their financial situations to others who are similarly situated by demographic, geography or other factors. The service can tell users how much money they’d save by switching to a new credit card or refinancing their mortgages. ‘The [alerts users] really dig is when we give them personalized information,’ [Intuit Senior VP of Big Data, Social Design and Marketing Nora] Denzel said, comparing Mint to her ink-ordering printer that saves her trips to the office supply store. ‘At least someone in this house is helping me.'”–Derrick Harris, GigaOM

“I don’t have any objections to companies basing the developers and maintainers of these solutions in the IT organization. However, standard solutions are not the only analytical tools that matter. What if (as is likely in most organizations) some new or repurposed algorithm could make a big difference in your business if someone could figure out how to implement it? What if a big-data analytics project could make your product or service much more attractive?… So go ahead and put standardized information solutions–whether they involve reporting or predictive analytics–in IT. But for the really creative uses of analytics, you need to find a new home. I am leaning toward the R&D organization.”–Tom Davenport, Babson College

“Real-time analytics is actually a very complex area, and the ability to get a ready-made, easy-to-implement solution cannot be overestimated. At the same time, simplicity is not enough for business that require accuracy, speed and reliability as basic requirements.”       —Adi Paz, EVP, GigaSpaces

“Adaptive Data Fusion… is a science that has been used traditionally in military and wireless sensor applications–taking big, complex streams of data from multiple sources and transforming them into actionable inferences that are superior to using the data streams individually. We’ve applied this to Zebit, the first platform that performs underwriting on a per-transaction basis–because each customer transaction is inherently different, involving a unique purchase, unique variables and a distinct point in time–the holy grail of risk management.”–Michael Thiemann, CEO, Global Analytics Holdings