{"id":4580,"date":"2014-04-30T07:00:33","date_gmt":"2014-04-30T15:00:33","guid":{"rendered":"http:\/\/formtek.com\/blog\/?p=4580"},"modified":"2014-04-10T07:08:07","modified_gmt":"2014-04-10T15:08:07","slug":"big-data-beat-up-backlash-attacks-on-the-technology","status":"publish","type":"post","link":"https:\/\/formtek.com\/blog\/big-data-beat-up-backlash-attacks-on-the-technology\/","title":{"rendered":"Big Data Beat Up: Backlash Attacks on the Technology"},"content":{"rendered":"

After considerable positive hype, the skirmishes and attacks against Big Data technology have begun.<\/p>\n

On the one hand there are those that either say we don’t need Big Data or that Big Data isn’t really anything different than standard data management and analysis. \u00a0For example,\u00a0Jim Goodnight<\/a>,\u00a0SAS CEO,\u00a0said that<\/a>\u00a0“the term big data is being used today because computer analysts and journalists got tired of writing about cloud computing. \u00a0Before cloud computing it was data warehousing or ‘software as a service’. There’s a new buzzword every two years and the computer analysts come out with these things so that they will have something to consult about.”<\/p>\n

On the other hand, there has also been backlash against what people see as a misguided use of technology because they’ve tried to use Big Data without fully understanding the tools.<\/p>\n

David Lazer<\/a>, professor at NorthEastern University,\u00a0points out issues when technology<\/a>\u00a0is applied to solve problems without having sufficient understanding for how to use or apply the technology. \u00a0“‘Big data hubris’ is the often implicit assumption that big data is a substitute for, rather than a\u00a0supplement to, traditional data collection and analysis. We have asserted that there are enormous\u00a0scientific possibilities in big data. However, quantity of data does not mean that one can\u00a0ignore foundational issues of measurement, construct validity and reliability, and dependencies\u00a0among data. The core challenge is that most big data that have received popular attention\u00a0are not the output of instruments designed to produce valid and reliable data amenable for\u00a0scientific analysis.”<\/p>\n

Mike Loukides<\/a> wrote for O’Reilly Radar that\u00a0<\/a>\u00a0“Ignore the hype. \u00a0Learn to be a data skeptic. That doesn\u2019t mean becoming skeptical about the value of data; it means asking the hard questions that anyone claiming to be a data scientist should ask. Think carefully about the questions you\u2019re asking, the data you have to work with, and the results that you\u2019re getting. And learn that data is about enabling intelligent discussions, not about turning a crank and having the right answer pop out.”<\/p>\n

A recent article in the New York Times <\/a>identifies problems with Big Data which include:<\/p>\n