Big Data Quotes of the Week, September 21, 2012

David Bowie

“When the term data scientist was first discussed 18 months ago, Frankenstein’s laboratory came to mind. As we continue to research the job descriptions of data scientists, we now realise it’s a legitimate role that is useful in a lot of organisations to help businesses get the most out of their data and to help bridge the gap between IT and business needs”–David Bowie, SAS

“At least as important [as big data technologies] are the people with the skill set (and the mind-set) to put them to good use…what data scientists do is make discoveries while swimming in data… the dominant trait [of data scientists] is intense curiosity—a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested. This often entails the associative thinking that characterizes the most creative scientists in any field….perhaps it’s becoming clear that the word ‘scientist’ fits this emerging role”—Tom Davenport and D.J. Patil

“Simply put, because of big data, managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision making and performance… Data-driven decisions tend to be better decisions. Leaders will either embrace this fact or be replaced by others who do. In sector after sector, companies that figure out how to combine domain expertise with data science will pull away from their rivals”–Andrew McAfee and Erik Brynjolfsson

“Executives need to understand that big data is not about subordinating managerial decisions to automated algorithms but deciding what kinds of data should enhance or transform user experiences. Big Data should be neither servant nor master; properly managed, it becomes a new medium for shaping how people and their technologies interact”—Michael Schrage

“Big data could transform the way companies do business, delivering the kind of performance gains last seen in the 1990s, when organizations redesigned their core processes. As data-driven strategies take hold, they will become an increasingly important point of competitive differentiation”–Dominic Barton and David Court  

“As Big Data science increases our ability to model or simulate complex systems, these models, ironically, become as complex as the real world. But they are not the real world… Overcoming these difficulties requires trained skepticism, sophistication and, remarkably, some level of intuition about the systems we study. Moving deeper into Big Data simulations will be an exercise in maintaining that skepticism, developing new intuitions and developing new tools to separate the chaff from the real, useful insights”– Adam Frank

“We actually want to have access to all of the information our bank has, and all the information we can get externally, as well. It’s a big thing to bring all that data together and be able to do something with it… the biggest benefit is that it takes you an enormous leap forward in analytic capabilities”—Greg Nichelsen

“We want to increasingly make use of ‘data that drives our questions’ as well as ‘questions that drive our data’”–Myron Guttmann, National Science Foundation

“Machines don’t make the essential and important connections among data and they don’t create information. Humans do”–Jim Stikeleather, Dell

“It is interesting to note that a substantial subset of the computer science community has redefined their research agenda to fit under the marketing banner of ‘Big Data.’ As such, it is clearly the ‘buzzword du jour’”—Michael Stonebraker

“Here’s a dirty little secret about the news business: If you walk into any newsroom today and flag down a passing journalist, the odds that they will know the difference between a median and a mode; or know how to multiply two fractions; or calculate percentage change, are probably worse than 70/30. It’s something journalists wear like a badge of honor. There’s even a canned response many reporters will give you, which they no doubt first heard in journalism school: something along the lines of ‘I became a journalist because I suck at math.’ We’re not all like this, fortunately. In many newsrooms, there’s a small but growing number of journalists that has embraced math, computer programming, data visualization and the other tools of data science in order to uncover trends; investigate waste, fraud and abuse; and reveal complex trends to our audience. We’re known as data journalists”–Chase Davis, Center for Investigative Reporting