“Let us cultivate the mathematical sciences with ardor, without wanting to extend them beyond their domain; and let us not imagine that one can attack history with formulas, nor give sanction to morality through theories of algebra or the integral calculus”–Augustin-Louis Cauchy, 1821, quoted by Matthew Jones, Columbia University
“…the common language of business is not going to be Chinese or Spanish. It’s going to be math”–Michael Rhodin, IBM
“The future is going to be owned by people who are comfortable in the quant world but have deep business knowledge”–Christine Poon, Max M. Fisher College of Business, Ohio State
“[One false promise that some proponents of Big Data hold out is that somehow vast oceans of digital data can be sifted for nuggets of pure enterprise gold.] It is not going to happen magically. The software only finds correlations, not causations. In order to find causal relationships you have to do work. If you take any sufficiently large data sets, you are going to find correlations. You need a human in the loop to work out which are important”–Stephen Sorkin, Splunk
“Since [I graduated in 2001] we’ve started generating lots more data, mostly from the web. It brings up the natural idea of a data economic indicator, return on byte. How much value can I extract from a byte of data? How much does it cost to store? If we take the ratio, we want it to be bigger than one or else we discard. Of course this isn’t the whole story. There’s also a big data economic law, which states that no individual record is particularly valuable, but having every record is incredibly valuable”—Josh Wills, Cloudera
“The fallacy of big data is that more data doesn’t mean you will get “proportionately” more information. In fact, the more data you have, the less information you gain as a proportion of the data. That means the information you can extract from any big data asymptotically diminishes as your data volume increases”–Dr. Michael Wu, Lithium
“Big data is more than an opportunity to do better. It should reset our expectations to see that great things are possible. As we develop the technical capacity to collect, organize, analyze and visualize we must make comparable investments in our institutions, civic dialogue and policy processes to adapt to this new data rich world to get the biggest bang we can from big data”–Joseph F. Coughlin, MIT
“It’s too simplistic to see ‘big data’ as a knight in shining armor, but the intelligent use of rich data, regardless of size, has the potential to help dramatically with problems from basic research to commercial operations”—Guy Cavet, Kaggle
If 2012 was the year of big data hype, interest and pilot projects, 2013 will bring production deployments, early returns on investment and a bit of disruption. By 2014, big data projects and systems are likely to be commonplace”—Larry Dignan, ZDNet
The attraction of Big Data is that correlating that much information has already proven to yield surprising and useful results. The potential downside is that it may pose a threat to user privacy, which could lead to legal troubles and a damaged reputation. And as with any fad, it could drain more resources from your client than what they gain in return, only to become obsolete when The Next Big Thing arrives”–Chip Camden
As research continues, we expect that the reliability of Big Data techniques will improve. Whether they will ever match traditional survey-based methods is debatable, but it certainly won’t happen in the foreseeable future”–Claire Emes and Gabriela Mancero, Ipsos MORI
“We’ve got lots of dirt — data. And there’s definitely gold in our data. But to refine it into information, you need people that have been doing this for years”–Rob Lux, Freddie Mac
“Every 14 minutes, somewhere in the world, an ad exec strides on stage with the same breathless declaration: ‘Data is the new oil!’”—Jer Thorp, Data Artist in Residence, New York Times
As someone who has worked with “Big Data”, without ever calling it by that name (for 15 years), I have to say that the new “Big Data” phenomena is being propogated by people who are either fearful of it (market researchers with qual backgroungs) or media-savvy research who want to cash in.
I’ve see so many of the former “fear” folks recently, and the series of quotes above underscore these sentitments. Fact: There is a growing amount of information stored. Fact: There are often gaps in the information gathered, making it it challenging to develop good model. Fact: Business intelligence is a tricky recipe – including some tech, some programming, lots of business knowledge, suble soft skills, good communication skills, and savvy.
I’ve seen plenty of articles in the past week that talk of “Big Data” as if it was a fad. It isn’t. The name may be a fad, but the business need isn’t. There are mountains of information that no one know what to do with. There are lots of opportunities to improve information gathering that are lost causes because there is no leadership. Big Data is all about an opportunity to address a major skill gap, and begin to actually take advantage of the software and technology out there. The people systems are way behind the tech at this point.
Point is… I’m waiting for the day when people stop dismissing “Big Data” and start accepting the challenge.
I agree with the last comment, I enjoyed the quotes above, for some of us Cauchy is still a statistical distribution, a very useful one.