Big Data: Emerging Discoveries in Complex Ambiguity

“These days, tremendous volumes of information are being generated and collected through new technologies, be they large telescopes arrays, DNA sequencers or Facebook”, says Malte Ebach in his article Scientists are asking the wrong questions of big data.

“Big Data has sparked the development of sophisticated new ways to capture, synthesize and act on it. [These] tools are helping us to move beyond big data, in fact, to aggregate and process data at a faster pace, in real time, across a broader set of touch points”, says David Steinberg in his article Data-Driven Marketing: Bigger, Faster, Better.

So, what is Big Data? Let’s first define traditional data. Traditional data deliberately collects information to answer a specific question. Think microscopes or surveys used in traditional research experiments. Big Data unintentionally collects information without reason or purpose. Think of clicks on Facebook, searches on Google or the Internet of Things (IoT) compiling data about our behaviors.

Big Data leads to a vast and somewhat unearthed terrain filled with waiting discoveries.  [Tweet This]

Big Data Questions

Ebach gives two examples of the discoveries waiting. He asserts that “our data sources have changed but our questions haven’t.”

First is that of biologists. Instead of letting the limitations or patterns in big data shape questions, biologists find themselves searching for more data to answer already existing questions.  Meanwhile, untapped data is being thrown to the way side. We could have used this data to develop questions we might not know we have.

The second example is Google Flu Trends and the Centers for Disease Control & Prevention (CDC).  Google, in trying to show they would be able to predict flu patterns quicker and more accurate than the CDC, asked of their Big Data, “When will the next flu epidemic hit North America?”  The data collection was non-traditional, but Google was asking a traditional question. In the end, the CDC produced more accurate predictions. What might have been discovered about flu trends had Google allowed Big Data to shape their question instead by asking, “What do the frequency and number of Google search terms tell us?”

It’s what we ask of our data that counts. –Malte Ebach   [Tweet This]

Big Data Organization

As quoted above, Steinberg likewise addresses the demand to unearth the potential discoveries in the vast terrain of Big Data. He asserts that the “return to organizational aspects of data management, with the inclusion of metadata and master data (‘data about your data’), will ensure that as we accelerate our data-driven marketing trains, we won’t careen off the tracks.” He believes that innovative organization is the way to most efficiently use Big Data.

In Steinberg’s words, the challenge is to learn how “to efficiently and effectively tap into this newfound richness and harness the power in these new data sets”.

Your challenge: What questions are you asking of, and what patterns are you seeing in, your databases?
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In the world of Big Data, whether you’re a marketer or product developer, combining new tactics for organizing data in real-time with emergent questions will allow you to successfully journey a  path of discovery and arrive ready for the future.

An Article Review by emily white

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