Larry Buzecky, AEM Vice President of Business Intelligence and Strategy

Data UnicornWhen you say you have an interest in big data, I have absolutely no idea what you are talking about. What do you mean by big data? Do you have a particular definition in mind?

Perhaps you mean IBM’s definition: Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency. And it has one or more of the following characteristics – high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media - much of it generated in real time and in a very large scale.

Okay, but what defines high for volume? High for velocity? High for variety? By the way, that’s a lot of height. And for that matter, what defines velocity or variety? Are there any agreed-to standards for those terms?

In any case, that’s one definition courtesy of a big player. For your professional enrichment, consider the 30-plus definitions for big data that were being tracked here until the website admin grew weary of the task and stopped updating it.

I kind of like this definition from back in the day: "The definition of big data? "Who cares? It's what you're doing with it. (Cited from 3/2013 FCW article, quoting Bill Franks.)

Exactly.

Regardless of an accepted, standard definition for big data, what are you doing with the giga- or tera- or peta- or exa- or zetta- or yotta- or brontobytes of data you have been collecting from your customer base, from your CRM, or your ERP? What about the data you've gleaned through your competitive analytics, or marketing analytics, or your R&D group? How about the data you've received from your business intelligence analysts or statisticians or consultants?

These days stockpiling data couldn’t be easier, because there are so many relatively cheap tools available now to help you grow and store your stockpiles.

Aggregating data, however, into centralized, structured repositories where filters (Do you buy your filters? Do you create your own?) can be applied to help define rational, winning new business strategies. This is more complex.

Executing on those strategies, measuring the results for an even more refined strategy, discarding the strategy altogether because the data says you should, or because your competition has suddenly made your game plan irrelevant (Remember -- they’re using big data, too) – now that’s really tough.

Your objective or objectives solve the mystery of big data. This is really my crafty, Jedi-like way of saying the following:

You already have the answer to your big data definition mystery, which may (or may not) be my answer to my big data definition mystery.

And if all this is very confusing for you, try hunting down the elusive and mysterious unicorn data scientist.

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