These are interesting analyses on current events in their own right, but some of you may need the information for your own purposes. Or, you may want to contribute data or data visualizations to one or more of these silks to aid in the overall understanding of the topic(s).
Argyle Data announced that it has joined the Hortonworks'partner program to provide Hadoop native fraud analytics to Argyle clients. It's a great move to bake fraud detection so deep in Hadoop rather than pasting it on later in the process.
The days of expecting all students in a classroom to memorize and regurgitate the same information on the same tests are numbered. In the not so distant future, real-world project results will be the prime scoring mechanism instead.
Nadarajan "Raj" Chetty, Bloomberg Professor of Economics at Harvard University uses big data to reveal what is driving upward mobility and contributing significantly to the realization of the American dream in various areas of this country and the world.
Delta just announced its new Intelligent Distribution Analytics Platform designed to warn electric, gas, and water utilities of critical issues in real-time.
Real estate brokers are no longer solely focused on what data buyers want to have, but on data that can place a broker directly in front of a seller at exactly the right moment too.
Vasant Dhar, professor at NYU Stern School of Business, is recommending that investors seriously consider big data based sentiment as an input in investment decision making.
Data hoarders are no longer trendy, but rather seen as in need of a good data warehouse cleaning. So now the new trend is to move to "smart data"--which is to say relevant data.
You may recall a post from last year on how big data was being used by Formula One race teams wherein it was reported that "approximately 240 sensors generate 25 megabytes of data per lap with which engine and chassis performance is analyzed." But, oh my, the speed with which that data is growing rivals the Formula One cars themselves.
Stitch Labs has done the analysis on more than 300,000 orders from the Wednesday before Thanksgiving to the Tuesday after Cyber Monday in 2013 and found a few shockers in the results.