Big Data: Too big to fail?
With ongoing hype surrounding (1) the exercise of Big Data mentality, and (2) implementation of Big Data procedures, and (3) application of Big Data software solutions, I really thought I was onto something original and clever here with the too-big-to-fail motif: “Big Data is TOO BIG to Fail!” ha ha ha
I figured it would be easy to write about how businesses are frequently encouraged to just “do” Big Data and–Bam!–they will become overnight successes, outpacing their competitors, $aving million$ of dollar$, and growing their customer bases like crazy. Big Data Magic! Everyone who touches Big Data turns to gold! Capitalize on it now or risk failure!!
However, the reality is that Big Data—just like any business initiative–most assuredly CAN fail if the mentality is not goal-oriented. Or if the procedures are not relevantly planned. Or if the software solutions are insufficiently targeted. Any or all of these factors could easily lead to misguided data interpretation and faulty conclusions. Big Data does not have to end in failure, of course. But, like the old saying goes: “Failure to plan is planning to FAIL.” And what struggling business wouldn’t be ready to blame new, faddish, Big Data efforts for such failure?
Anyway, it turns out the too-big-to-fail motif has already been broached by better minds than mine. Here are a few entries:
- From Click Z, May 2012: Big Data Is Never Too Big When You Can Act On It
[Insight is great as far as it goes, but Big Data projects will fail without conversion of insights into actions.]
- From Wall Street Journal, March 2013: Big Data, Big Blunders
[Five Big Data mistakes that companies commonly make, and expert tips on how to avoid them.]
- From earlier this week at Tech World News, April 2013: 5 strategic tips for avoiding a big data bust
[More tips to increase the chances your Big Data projects do not end in failure.]