Transforming Big Data into “Smart Data”: Don’t Ignore the Archives

Transforming Big Data into “Smart Data”: Don’t Ignore the Archives

Big Data is the biggest buzz word in IT these days, rapidly becoming the must-have tech for every organization and enterprise worth its salt. But with everyone rushing to get their Big Data strategies off the ground, a lot of CIOs have dived in headlong without even working out how to get the best from these tools.

Speaking to Fast Company recently, Joe Rospars, CEO of Blue State, summed things up:

“There’s a tendency with big data to say, ‘Mmm, I just want more data,'” he said. “But basically everyone already has too much data, and the question is how to use it and integrate it within your organization.”

It’s really not that surprising that some CIOs may be struggling to find answers to this question, given that “the role of Big Data is deliberately vague”, says IBM’s James Kobielus at Infoworld.

What Kobielus was trying to say is that when it comes to Big Data, there is no single ‘best-practice’ strategy that organizations can follow when it comes to gleaning insights from their data. Big Data isn’t specifically to be used for marketing, any more than it is to be used by sales departments, C-level execs or IT leaders. Quite simply, Big Data is there for everyone and anyone – the challenge is for each of these departments to work out how to exploit it for the best.

Kobielus advises that organizations need to concentrate on finding the “gems” within their Big Data that can serve a variety of critical roles, including boosting fraud prevention for IT security, improving operational efficiencies and enhancing marketing campaigns by measuring relationships with consumers.

This relationship aspect is especially important, claims Rospars:

“Big data is about having an understanding of what your relationship is with the people who are most important to you and an awareness of the potential in that relationship.”

Another mistake that organizations new to the Big Data game make is that they spend too much time focusing on ‘fresh’ data. Kobielus says that this really isn’t necessary, and that companies could reap a lot more benefits from dipping into their archives. Rospars supports this argument too, saying that we need to be “smart about the data we had all along”.

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From the perspective of marketing, most organizations already have a stack of rich information stored up in their data archives; for example, we have consumer searches, purchasing history, advertising ROI statistics, and more besides. By focusing their Big Data tools on this archived data, organizations should be able to gain unprecedented insights into the relationships they have with their customers.

However, Rospar reminds us that Big Data isn’t the be-all and end-all of everything. Company strategists need to remember that Big Data analytics is just a small part of the larger enterprise goals, and these must be carefully defined before anything else. Only when decision makers have a concrete idea of what they want to accomplish will they be able to utilize Big Data to achieve these goals.

Mike Wheatley

Mike Wheatley is a senior staff writer at SiliconANGLE. He loves to write about Big Data and the Internet of Things, and explore how these technologies are evolving and helping businesses to become more agile.

Before joining SiliconANGLE, Mike was an editor at Argophilia Travel News, an occassional contributer to The Epoch Times, and has also dabbled in SEO and social media marketing. He usually bases himself in Bangkok, Thailand, though he can often be found roaming through the jungles or chilling on a beach.

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  1. BernardMarr Good article, on point. Touched on this in feature in

  2. imbigdata Smart nothing but actionable !! Transformation leads to growth beyond the expectations

  3. Big Data is certainly the topic of the day, it seems to excite everyone we talk to. It’s not just about how to analyse the data though, there’s also a big question as to *what* data it is that’s being captured. Data can also be incredibly misleading, especially once it’s been sufficiently abstracted (or hidden behind a buzz word), so validation of the data gathering process and verification of the data itself is also critical. An easy example is how “hot-dogging” your way into buildings can mess up entry, exit, occupancy & duration data.

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