Today, there are 15 billion connected devices worldwide, and with those connected devices come massive amounts of data. Businesses are leveraging this wealth of data to gain insights into prospects and customers to steer engagement strategies, identify pain points in marketing and sales performance, and unleash intelligence about where organizations can best allocate resources. In essence, data is the key to accelerating business performance and gaining competitive advantage.
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The problem with data, however, is that there is now so much of it — with more and more becoming accessible every day — that businesses are struggling to make sense of it all and use it to its full potential. And with the internet of things market expected to grow leaps and bounds in the next few years (the amount of internet-connected devices is projected to reach 50 to 200 billion by 2020), businesses need to quickly figure out how to manage and optimize their ever-increasing amounts of data. And while data storage is increasingly cheap, the rate of growth is such that the cost of storing, processing, auditing, securing and exploiting the data is growing much faster than any savings accruing due to falling storage costs.
To make the best use of their data, businesses need to understand the most effective way to leverage the data while keeping costs and complexities under control. When it comes to monetizing data, less is often more. Big data in and of itself will not drive them forward; rather the key is what we call “deep data.” Deep data is an approach through which businesses identify and aggregate the most meaningful data streams and model them in a way that delivers insights into their most pressing business challenges. It’s based on the notion that rather than hoarding irrelevant or less useful data, businesses should focus on the data streams that are rich with valuable information.
To determine which data streams will provide the most value, companies must start by focusing on the business challenge they are trying to solve by leveraging data. From there, they can apply advanced analytics to small but information-rich data versus sifting through piles of information, which can often feel like looking for a needle in a haystack.
In the energy space, we have found the deep data framework to be hugely successful. Looking at data from the utility meter, arguably the oldest and most prevalent IoT device around, we can identify ways in which utility customers have historically used energy. That intelligence, coupled with data pertaining to weather and geographic factors, enables us to draw conclusions about how those customers may use energy in the future. Armed with these insights, utilities are empowered to prepare for shifting energy usage patterns, educate consumers about their energy spend and engage customers with offerings that meet their individual energy needs.
As more and more “things” are connected to the internet, businesses will find that the deep data approach will not only alleviate stress related to the explosive growth of data, but deliver welcome insights that can help decision makers choose the best path forward. That’s something we can all feel good about.
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