We’re hearing a great deal these days about Big Data and related terms, one of which is Big Data analytics. There are many definitions of this term and here’s one as good as any:
We’re hearing a great deal these days about Big Data and related terms, one of which is Big Data analytics. There are many definitions of this term and here’s one as good as any:
Big Data analytics is the process of examining large amounts of data of a variety of types (Big Data) to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages over rival organizations and result in business benefits, such as more effective marketing and increased revenue.
You’ll get no argument from me on the importance of defining key terms, be it Big Data, analytics, platforms, etc. Many blown IT projects or corporate initiatives can trace their failures to people not being on the same page from day one.
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And this is why I’m a bit skeptical of the term Big Data analytics. Is the focus on Big Data? Analytics? Both?
Where’s the Focus?
I’d actually argue that it should be neither. That is, “BDA” is just a means towards the normal business end. To me, the entire point of capturing, storing, and analyzing any data (Big or Small) is to move the needle. Period. Or, if you like, consider the simple diagram below:
How many of us take the chain to the end? Or do things stop prematurely? I worry that the focus on either analytics or Big Data is misplaced. They are all merely means to the traditional business ends: increasing sales, decreasing expenses, etc.
I’ve written thousands of reports in my consulting career and, lamentably, far too many of my clients would want the report for the sake of wanting the report. I can recall several occasions in which I’ve stumped my clients by asking a simple question like, “What do you do with this information?”
Simon Says: Don’t Forget the Endgame
I have no doubt that the analytics available from unstructured data can augment our understanding of customers, users, employees, and just about everyone else. At the same time, though, data for the sake of data is meaningless. Consider two organizations, A and B. The former effectively utilizes Small Data and routinely makes decisions based on analysis, tested hypothesis, and fact. The latter doesn’t touch the vast troves of data at its disposal–both big and small.
All else equal, I’ll bet on Organization A any day of the week and twice on Sunday.
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What say you?
