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Leveraging Big Data Requires the (Expert) Human Touch

Behnam Rezaei, CTO and Co-Founder, NetSeer

Behnam Rezaei, CTO and Co Founder, NetSeer, Behnam is at the helm of NetSeer's technological innovations. He oversees the design, architecture and development of the company's ad platfor...  More >>

With big data offering access to vast amounts of information about consumer behavior, preferences and other metrics, companies have the ability to execute more precisely targeted advertising and marketing initiatives than ever before, predicting with greater accuracy the outcome of those efforts. What’s more, with highly distributed architectures, more memory and increased processing power, it has become much easier to process, visualize and analyze information drawn from big data. It’s therefore not surprising that many companies are employing sophisticated big-data analytics tools to help them make sense of and translate all that information into action plans.

Leveraging big data for a specific end, of course, depends on asking it the right questions. To do this, you have to understand what data points to collect. Some companies assume that analytics tools, along with experts in those analytics tools, can assist with this. On the surface, this makes sense, as today’s more advanced big-data analytics software can draw out a lot of interesting and useful insights from large pools of information. They’ve gone far beyond SQL to leverage machine learning, graph analysis, predictive-modeling algorithms and other techniques to undercover patterns and correlations in the data that may not be readily obvious, but could turn out to be highly beneficial to a specific aim.

To uncover, or even recognize, these correlations and patterns, however, still requires the input of certain data points. The natures of these points depend heavily on your industry, your goal or initiative and what you’re trying to accomplish with it. Big-data analytics software, and in many cases, the person who knows that software inside and out, can’t discover them for you. Only a big-data expert with deep experience and background in your specific field can help you unearth the right data points to collect, and therefore ask the right questions of your big data.

This is important because when it comes to big data, people tend to forget one critical fact: It’s simply a tool for accomplishing something—whether that is to sell more units of a specific product or to gauge consumer response to a new advertising initiative. And just like any other tool, someone with the proper skills and knowledge is required to make it work.

Think about this in the context of a tool from a different industry: Magnetic Resonance Imaging (MRI). This sophisticated technique utilizes magnetic fields and radio waves to depict images of the human body. MRIs reveal all sorts of elements of the human body, from spinal fluid and grey matter in the brain to specific problems such as tumors or neuron damage. Simply putting someone through an MRI machine, however, won’t tell you if he or she has brain cancer, or the beginnings of a neurological disorder. Nor can the technician who knows how to push the buttons on the machine make these diagnoses.

Only a medical doctor specializing in the patient undergoing the MRI’s specific area of concern can look at the images depicted by the MRI and make a definitive diagnosis. That’s because the doctor knows what questions to ask of the MRI—again, what data points to collect. A neurologist looking to find out what’s going wrong with a patient complaining of severe headaches will know what areas of the brain to examine to determine if there is a tumor, or whether some cells look abnormal. He or she can glean from years of education and training the signs and features in the MRI images that point to a certain diagnosis. The MRI is merely the tool for extracting this information. Without the neurologist’s expertise, it’s not incredibly useful.

The same goes for extracting the right information from big data. Once you’ve formulated a goal for which you need this information, then it’s time to bring in the person who is an expert not only in managing big data, but also understanding it in the context of your specific field and application. If your company is seeking to detect and block click fraud on its online ads, for example, you need someone who not only understands your particular big-data analytics system thoroughly, but also has a comprehensive grasp of how to detect such fraud in the first place.

This is because click fraud is a complex, evolving phenomenon involving specific activities deriving from certain IP addresses and other markers. Experts on it look at a large number of data points—everything from the number of duplicate clicks on an ad that originate from the same IP address and the amount of time that elapses between those clicks to behavioral anomalies that can determine whether a human or a machine is generating the clicks—to root it out. If your company were looking to abolish these illegitimate clicks, do you think your big-data expert would know to look for these factors? If not, you must hire someone who can, or else your big data is useless for this task.

There is no doubt that big data holds the key to unlocking many insights into consumer behavior, among other types of information. It’s important to remember, however, that it’s just a tool. Without the expert human element, it won’t help you address any business challenge. You still need to ask it the right questions, and to do that you need a person who understands thoroughly the data it takes to ask those right questions in the first place.

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Machine Learning

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