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Vendredi 9 Janvier 2015

FY360° | Big data, fuel for the 21st century


By Paul Burton, Senior Vice President, Analytics & Research | December 29, 2014.



Data. It's often called "The Oil of the 21st Century," and today we find ourselves living in a sea of it. But what has really changed to make data the prized business commodity it's become? For an answer, consider oil's path through history...

Oil has always existed. Yet it wasn't until the mid-19th century that it started to become so immensely useful. That's when we attained the industrial capability to efficiently extract vast quantities of it from the ground. That's when we became capable of transporting it over long distances, by rail cars and then pipelines, to get it to a processing facility. Once there, we leveraged our new found ability to refine it in many ways, then transport it further upstream, and in many cases further refine it before end users could consume it.

Going back just seven generations, neither the technologies necessary to produce and refine oil existed nor did the infrastructure to transport it over distance to make it broadly available for consumers. It was only as the industrial processes were devised to cost-effectively exploit this commodity at scale that oil came to realize its full potential, came to be the Life Blood of the Global Economy it is today.

Now you have to ask yourself: How is Big Data so different from oil? The data that our Nike wrist bands capture or our iPhones capture; the data being perpetually spit out by sensors on industrial devices. This commodity always existed. What’s new now is our ability to collect it and analyze it. What’s changed is the technology and tools we now have that allow us to exploit it.

So like oil, Big Data is nothing new. What is new is our ability to gather and exploit it, because of an increase in available technology and tools. Think of all the good we have received from oil: Transportation, consumer goods, heating for our homes, lighting when it's dark. All these things came about because of oil. The level of economic development we enjoy today, however high or low, is largely driven by oil.

In much the same way, we can now expect data to give our living standards an incremental (and in some instances exponential) boost; expect it to make our lives fundamentally better. But that happens only if we have the technology and tools to harness and exploit the full potential of the data available to us, much of which arguably still lies untapped; and, only if we know for what purpose our efforts are being directed.

So what is needed to exploit data's potential?

I am sure by now that everyone has heard “Data Scientist” is the sexiest job of the 21st century. Seriously, though, it’s a position in incredibly high demand, and much needed candidates are scarce. So, this automatically means that, in terms of compensation and prestige, the Data Scientist commands a premium in today's employment market, right? Not necessarily so.

I believe data scientists are necessary, but not enough by themselves to have the impact needed. When oil was discovered it took many different types of skilled people to fully exploit it. Geologists, chemical engineers, petroleum engineers, mechanical engineers, logisticians, economists, accountants, even lawyers. It was not a single person or skill that was determinative to unlocking the commodity's full potential. And so it is with Big Data. In order to exploit Big Data, we need diverse sets of people working together toward a common goal.

In many respects, it is harder to tap Big Data's full potential then it was to tap oil's. Yes, you had to search for and discover oil; but once it was found, extracting it and moving it through the value chain to higher valued uses was straightforward, even if complex and difficult. With oil, once it was discovered, everything else was knowable. Not so with Big Data. With it, you are inundated with information and have no idea what to do with it. So the problem is flipped on its head. This is why diversity and creativity in a “Big Data” team is so important.

- People with domain expertise are needed to carefully define the problem.
- People from adjacent fields like psychology, economics, or history are needed to help see the problem differently, finding its unexplored contours and nuances, and further helping to define it.
- The Data Scientist is needed to lend method and science to using data to rigorously solve the problem as it is defined
- And the team at large is needed to understand the robustness of the proposed solution — i.e., what does the result produced mean and why?

The role of the data scientist simply does not exist absent a problem defined by business and domain experts. It is they who give direction to the work. So for those interested in money, prestige, and a lofty position in business, data science will take you only part way there. My advice to those seeking that career path is simple: Don’t limit yourself to breadth and depth in a single discipline. Realistically, you need to be deep in a couple disciplines.

Industrializing Insight

For oil to be commercially exploited, the processes of exploration, capture and refining (to name but a few) had to be industrialized. These processes had to be efficient and operate at scale. Is there a parallel for Big Data?

Insights have material impact only when industrialized and effectively embedded into business processes.

Again, we must remember that Big Data presents a different challenge than oil. With oil, the trick was to find it, and then exploitation happened as a matter of course. With Big Data, finding it is not the problem, understanding how it can be used to solve a problem is, since problem definition can be elusive.

Indeed, the reason firms struggle with harnessing Big Data to drive material business impact is not just because data scientists are hard to find or because technology is a “moving target.” It is also because enterprises are not used to thinking of the analytical impact at scale in terms of (1) the data-to-insight process and (2) the insight-to-action process. Proper problem definition is needed for both, or neither will be possible.

For any economic benefit to stick, the process of solving problems with big data needs to be industrialized and performed at scale. That's the game changer, and it cannot be accomplished by a data scientist in isolation.

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