Data Scientists are sexy, but Big Data is not. It’s really just a big pile of data.
And you can get lost in it, spend lots of money trying to manage it, find insights in it that aren’t actionable … or sweep it under the rug until your CEO reminds you that it (and your employment status) is on the agenda next week.
You’re supposed to tell the board about how Big Data will provide a strategic advantage for your company in 2014.
And talking about the Big Data pilot that you ran last Spring isn’t going to cut it this time; it didn’t even pay for itself.
Instead you’re going to have to aim as high as the hype and join the term Big Data with at least half of these keywords: make money, save money, attain/retain customers, make new discoveries, innovate new products and services ….
It’s going to take Big Data Analytics to realize those returns, but they aren’t easy either. You’ve never even heard of half of the vendors and tools available.
But whatever you promise, you’re going to have to deliver, the CMO will draw that line for certain. He’s sure he can do your job better than you. And while he’s not gunning for your title, he no longer wants to fund your department; he’ll go handle his Big Data Analytics needs on his own.
Is Dealing With Big Data Analytics All That Difficult?
And while this sounds like a somewhat preposterous scenario, the reality is, that it’s exactly what’s playing out not only on Main St and in suburban office parks, but at the corporate headquarters of Fortune 100 companies as well.
Big Data evangelists and chronicles of success stories from the likes of Netflix and Facebook have created a good number of Big Data believers and now every C-level exec wants his/her own Big Data story to tell.
And though CIOs don’t question that Big Data offers many big opportunities, the idea of doing what it takes to gain value from it is, to many of them, daunting. Some CIO’s feel almost crippled.
The Problem Isn’t Just Big Data, But Leveraging Big Data Analytics
Earlier this year Hadoop turned seven years old and there are many, many vendors who provide their own distributions as well as products, services and support around it. Anyone who has — and is willing to — spend the cash required to bring Hadoop on premises, or use it on the cloud, can get the resources they need to make it happen.
What’s more difficult is gaining actionable insights from Big Data in reasonable amounts of time. And not just that, but doing so with tools that are intuitive or familiar and at the same time, leading edge, enterprise-grade and state of the art.
That’s a Big Data analytics problem that, at the Enterprise level, is not yet (for the most part) being solved by a large number of vendors.
Teradata Unveils Big Data Analytics That are Fast, Enterprise Grade, Powerful
That’s why it’s a big deal that Teradata today announced the release of its new database which not only rivals what next-generation Big Data analytics vendors provide, but what they claim moves beyond them because it is faster.
They’re achieving this partly by bringing the analytics to the data (which up until recently was done the other way around) using R Analytics that are fully parallel and scalable. They’re also offering what they say is the largest, most complete analytic library of over 1,000 in-database analytics, enhanced access to native Extensible Markup Language (XML) data, streamlined temporal analytics, and capabilities that make geospatial data faster and easier to use.
In other words, they’re providing technology that helps Enterprises gain insights from vast quantities and varieties of variable data in record time.
An admittedly oversimplified example is that marketers are more likely to be able to tell who you are, what you like, where you are and what you’re on schedule to buy at this specific point in time, so that they can then shoot you a coupon and bring you into their stores.
While much of this was, theoretically, possible to do in the past, in most actual applications you would have left the store and gotten a few nights sleep before the “buy now” offer found you.
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