How Analytics Turns IoT Data into Dollars
Using analytics to process IoT data can produce powerful results.
The buzz around the term “Internet of Things” (IoT) amplifies with each passing day. It’s taking some time, however, for everyone to fully comprehend just how valuable this phenomenon has become for our world and our economy. Part of this has to do with the learning curve in understanding the sophisticated technologies and analytics involved. But part of it is the sheer, staggering scope of value that’s possible worldwide. A comprehensive study in June 2015 by the McKinsey Global Institute, in fact, concluded that IoT is one of those rare technology trends where the “hype may actually understate the full potential.”
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The Internet of Things is our constantly growing universe of sensors and devices that create a flood of granular data about our world. The “things” include everything from environmental sensors monitoring weather, traffic or energy usage; to “smart” household appliances and telemetry from production-line machines and car engines. These sensors are constantly getting smarter, cheaper and smaller (many sensors today are smaller than a dime, and we’ll eventually see smart dust: thousands of small processors that look like dust and are sprinkled on surfaces, swallowed or poured.)
Smart Analytics Drive IoT Value
As the volume and variety of sensors and other telemetry sources grows, the connections between them and the analytic needs also grow to create an IoT value curve that’s rising exponentially as time goes on. IDC predicts the installed base of IoT-connected things will reach more than 29.5 billion in 2020, with economic value-add across sectors by then topping $1.9 trillion. For all the focus on sensors and connections, however, the key driver of value is the analytics we can apply to reap insights and competitive advantage.
As we build better algorithms for the burgeoning IoT digital infrastructure, we are learning to use connection-based “smart analytics” to get very proactive in predicting future performance and conditions and even prescribing future actions. What if we could predict such a failure before it ever happens? With advanced smart analytics today, we can. It’s called predictive maintenance and it utilizes a probability-based “Weibull distribution” and other advanced processes to gauge “time to failure” rates so we can predict a machine or device breakdown before it happens.
One major provider of medical diagnostic and treatment machines has leveraged predictive maintenance to create “wearout models” for component parts in its products. This enabled early detection and identification of problems, as well as proactive root cause analysis to prevent downtime and unplanned outages. A large European train manufacturer, meanwhile, is leveraging similar techniques to prevent train engine failure. It’s a key capability that has enabled the firm to expand into the leasing market – a line of business that’s profitable only if your trains remain operational.
Building IoT Architectures
There is really no limit to how far we can take this alchemy of sensors, connections and algorithms to create more and more complex systems and solutions to the problems facing businesses. But success remains impossible without the right analytics architectures in place. Most companies today still struggle to capitalize and make use of all this IoT data.
Indeed, McKinsey’s June 2015 IoT report found that less than one percent of IoT data is currently used; and those uses tend to be straightforward things like alarm activation or real-time controls rather than advanced analytics that can help optimize business processes or make predictions.
Even the most tech-savvy businesses are now realizing that extracting value from the data is a difficult and skills-intensive process. Top priorities include intelligent “listening” to massive streams of IoT data to uncover distinctive patterns that may be signposts to valuable insights. We must ingest and propagate that data in an analytical ecosystem of advanced machine learning algorithms, operating at scale to reap sophisticated, actionable insights.
Agility is key: Architectures need to follow multiple streams of sensor and IoT data in real-time and deploy an agile-central ingestion platform to economically and reliably listen to all relevant data. Architectures also should be configured to deploy advanced analytics – including machine learning, path, pattern, time series, statistics, graph and text analytics – against massive volumes of data. The entire environment should be thoroughly self-service to enable rapid innovation of any new data set and avoid bogging down IT personnel with costly, requirements-driven custom projects.
These are the kind of capabilities companies must pursue to economically spot and act upon new business opportunities made possible by the Internet of Things. It takes a good deal of investment and strategic planning, but the payoff in terms of analytic insights, competitive advantage and future revenue is well worth it.
This article was originally posted at Teradata.com. It has been reprinted here with permission. Teradata retains all copyrights.