IoT And Big Data Really Is Like Oil, We Just Need To Let It Flow

McKinsey’s recent report on the potential value of the Internet of Things (IoT) projects the future economic impact to be $11 trillion per year. McKinsey is right in concluding that “the hype may actually understate the full potential.” IoT and Big Data are much more than buzzwords and the implied hype. Their impact on business and society in the coming 20 years will exceed even that of the Internet over the past 20. But that will only happen if we elevate our view of where in the value lies, from within to between the systems we are building.

Businesses and governments have collected massive volumes of data on everything. We can gauge moisture levels in soil across a thousand-acre farm down to the square foot. We test and record every possible aspect of a consumer’s interaction with our brands and products. Yet the majority of this data lies dormant, with only a small fraction of its potential value effectively harnessed. We store it, we secure it -- but, largely, the insights it should provide remain in disparate mountains of ones and zeroes.

Most businesses today barely scratch the surface in creating meaningful value from the data they already access. Many saw the complex task of simply collecting and storing the data as the primary objective. The real value in a world where all of our devices are connected and talking to one another will come from breaking down the silos in which we currently house the data we generate. Bringing together seemingly unrelated datasets and tying them into real-time, living, of-this-minute data on behaviors and activities is where the breakthrough insights will come. This is the next opportunity we must tackle to realize all the promises that have been made to us throughout the IoT and Big Data hype.

A number of people, including McKinsey and Alibaba CEO Daniel Zhang, have metaphorically compared data to oil. It’s an apt metaphor, because just as oil is of little use when first pulled from the ground, data in and of itself has little intrinsic value. It’s only when you do something with the raw material -- when you refine it for use in an engine or analyze and model it to find the patterns it contains -- that its value becomes tangible and accessible. We have an incredible volume of raw data sitting in storage, but organizing and transforming that data so it can be responsibly combined with other data, and then processing, analyzing and modeling it to uncover patterns and insights, and make predictions, takes a substantial investment in computing power and data science. Consequently, much of the data we produce and record is left largely in its raw state, with the insights it could hold remaining hidden. It’s akin to oil, pulled from the ground but left in a storage tank rather than sent to the refinery.

This is particularly true in an area where most companies have an incredible volume of existing data -- marketing. As one example, for two decades we have built up treasure troves of search data, and we’ve used it to better inform how we design Web sites for SEO and how we more effectively reach the right prospective customer when they type their interests into a search box. But, the deeper potential value in that data remains untapped. Within this search data lie the seeds needed to find new customers before they even think of typing a product’s name into a search engine. To make that happen, we need to do two things that are not currently second nature for us. First, we need to connect search data to other data on consumer behavior and interests, then we need to apply the right analysis and modeling to this unified data to reveal to insights inaccessible to intuition alone. The situation is repeated in even our most valuable data sets such as CRM.

Think of it in a more personal way. Many of us have at least experimented with a fitness tracker, a Fitbit or an Up, or one of the dozens of others available. These sensors, combined with their accompanying software, are great at producing graphs and charts that give us insight into our activity levels, but the insights are inherently limited. Does walking an extra mile have a positive impact on the concerns your doctor raised at your last physical? You could guess, but you wouldn’t really know because the health data being collected on your wrist isn’t connected to the important data on your health sitting at your doctor’s office. Bring this data together and things can get much more sophisticated and impactful, “Hey, you need to do more than walk an extra mile to address your blood pressure. Here’s some suggested exercises and now let’s talk about that lunch you’re about to have.”

We have created incredible data stores reflecting human knowledge and activity -- immense batteries waiting for the means to apply their potential energy. We can now unleash that energy by building connections between our discrete data collections, applying the power of intelligent machines to draw out insights we don’t intuitively know are there and then, utilizing the globe spanning web that is the Internet, scaling them across everyday human experiences. This is where the value lies. And this is where the promise of IoT and Big Data begins to be fulfilled. 

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