Over the weekend, I installed the Nest Hello, the latest HD video doorbell that lets you know who’s at your front door. I purchased the device to improve my home security, but very quickly realized I was buying into the Google IoT ecosystem. There are many smart doorbells on the market, but the power of the Nest Hello was its ability to interact across my smart home ecosystem – Google Home, smart TV, thermostat, fire alarm, and my digital Google persona. Essentially, Google (GOOG) paints a 360-degree view of my home interactions to help improve my quality of life. Google’s ability to do this is based on its ability to transform data into meaningful insight and use that insight to improve my physical and digital experiences.
Home assistant services are probably the most salient example of IoT in action, but the uptake of edge devices is transforming every industry. In the realm of sports, Wilson Sporting Goods’ engineers and scientists from SportIQ have developed smart basketballs and footballs that stream performance data to an individual’s mobile device. The realization that sensor-enabled sports products can improve training has now evolved into developing the next objective – game day-worthy products for NCAA Championships and Super Bowls.
The IoT movement is not only affecting consumers, but also redefining industrial arenas, like manufacturing, utilities, and logistics. Companies such as EnerNOC are refactoring the energy grid with the virtual power plant. Customers can use their energy as a revenue source, by reducing non-critical energy use during moments of severe grid strain. For this to be effective, the virtual grid must be able to transform both historical sensor data and streaming sensor data into impending costs and potential savings opportunities. The virtual grid can be used for real-time remediation by turning off IoT-enabled devices to save energy.
In essence, the value of IoT is in building more intimate digital relationships by correlating data across users, devices, and things, and translating it into instant insight. Most businesses, however, are still using data-related technologies that use the old serial computing paradigm running on CPUs to store, manage, and analyze IoT data. The problem is, these technologies are just too slow to extract value from data in real-time, to make operational decisions on the go, or quickly and accurately assess risk.
Traditional CPU-powered databases aren’t designed to handle unpredictable data sources or complex analysis. Data can be big or small, static or streaming, structured or unstructured, human or machine, long-lived or perishable. CPU-powered databases take a long time and require users to decide what elements of the data they think will be important to analyze in advance, and they struggle to produce real-time visualizations. They’re holding businesses back.
Gartner recently estimated that through 2018, “80% of IoT implementations will squander transformational opportunities”and fail to monetize IoT data. In this era of extreme data management, IoT data engines must address massive sets of complex data at unparalleled speed, with streaming data analysis, visual foresight, and streamlined machine learning, all orchestrated around an innovation-focused ecosystem. Without these things, it becomes impossible to support, utilize or monetize the IoT.
It is incumbent upon us as business leaders and technologists to evolve our thinking around IoT data to the point where data relationships shape our business strategies, drive our investments, and enable hyper growth. Now, while we see the growing importance of IoT, the data streaming from edge devices must be woven into the very fabric of our business strategy and data operations.
The IoT requires a Graphics Processing Units (GPU) database for analyzing data simultaneously, in real-time: an insight engine. Using GPUs, a technology pioneered by NVIDIA (NVDA), companies can process data with extreme parallelism. Essentially, a CPU is designed to process a trickling stream of data and a GPU is designed to process a fast-flowing waterfall of data (think garden hose versus Niagara Falls). The IoT is already generating 100x more data, 100x faster than Niagara Falls, and requires a different foundation for success.
Need a primer on a GPU database? Read a quick overview here.
A GPU database can also take geospatial and streaming data and turn it into visualizations that reveal interesting patterns and hidden opportunities, whether that’s where it’s safe and profitable for an oil company to drill, or where a tire company needs to track and respond to customer sentiment during a snowstorm. It can also apply algorithms to augment human learning with machine learning, quickly identifying complex patterns that humans can then analyze in depth.
In short, through accelerated parallel computing, a GPU-powered insight engine enables businesses to consume extreme volumes of IoT data, visualize their IoT landscape, do ad-hoc discovery across the data, analyze it with machine learning, and instantly feed insights back to business applications for immediate action.
With the right tools, you can analyze IoT data when it’s most valuable to you. And that’s right now. No matter what industry you’re in, you can immediately affect digital relationships and gain a massive competitive advantage that will set you up for success in the Extreme Data Economy.
Editor’s Note: This article was originally published in Forbes on 3/26/2018