IoT-Devices and Data Powering Contextual Decisions
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IoT-Devices and Data Powering Contextual Decisions

Mark Gildersleeve, VP and head of Watson IoT and Decisions Platform, IBM
Mark Gildersleeve, VP and head of Watson IoT and Decisions Platform, IBM

Mark Gildersleeve, VP and head of Watson IoT and Decisions Platform, IBM

As happens with almost every major new technology platform, change happens slowly at first and then accelerates suddenly. The Internet of Things (IoT) has finally escaped the early hype and today delivers meaningful value across most sectors of the economy.

Three main factors contribute to higher value:

• Billions of connected sensors, gathering a steady stream of raw information from consumer products, industrial electronics, transportation networks, and supply chains. Today, we can collect a range of types of data at a physical granularity and a sample rate that was not possible even a few years ago. This data can range from vibration and temperature readings in a factory to soil hydrology and atmospheric pressure on a farm.

• Interoperability of devices and systems. Broader coverage and greatly reduced costs of telecommunications enable greater connectivity across a range of settings and device types. Furthermore, innovations in cloud and edge computing allow the data from these devices to be more easily integrated and shared.

• Advances in analytics and machine learning. Platform investments in data management, analytics, and artificial intelligence allow for the creation of custom systems to analyze these massive amounts of distributed data. Importantly, this analysis can drive action, all the way from actuators at the edge of the network to optimized routing of trucking fleets.

Local data has driven control of devices and systems for decades. From the earliest days of supervisory control and data acquisition (SCADA) and machine-to-machine (M2M) technologies, the industry has worked to use data to head off unplanned machine down-time by responding to changing conditions more quickly and by looking for patterns to plan maintenance schedules. We can extract more value when we aggregate and normalize data into a form that allows for broader consideration. In one simple example, we can improve the quality of data coming from a temperature sensor in part by comparing its results with neighboring sensors.

  With greater deployment of sensors and actuators, the integration of collected data sets, and continued advance in analytics and machine learning, IoT will deliver even greater value  

In the world of IoT, outcomes are improved by making better decisions with better data. By ingesting and correlating sensed data along with parameters such as environmental conditions we can use many kinds of analytical tools to better understand what is happening, and through deeper analysis dig into how we might effect change. The true inflection point comes when the synthesis and analysis of data enables higher-order contextual decisions that drive concrete actions. This process from data collection, to data analysis, to decision, and finally action can drive real time systems that control equipment, can drive scheduling of specific actions (such as preventive maintenance). I can also drive planning and forecasting systems that use our improved understanding of the world to better forecast the consequences of changing conditions.

One area where IoT data is turned into action is with airplanes, one of the most complex IoT devices out there. Let’s explore air turbulence to bring this to life. There are sensors on a plane that measure when the airframe experiences turbulence. A message is sent to the operations center in real time informing dispatchers that a turbulence event occurred. Analytics are applied on that data to predict where turbulence will be occurring in the future. Then, an alert is sent to the planes that fly through that “turbulent air space” so that flight routes can be altered to avoid the hazard. The outcome is fewer injuries to flight attendants and passengers, lower maintenance costs, and a return on investment measured in months, not years.

Let’s look at a two other industries to illustrate the possibilities. The retail industry is undergoing truly massive change as commerce shifts online, consumers become more connected and mobile, and advertisers have more insight and ability to target promotions. Retailers have access to a range of data sets including retail receipts; footfall traffic in brick and mortar stores as measured by a range of sensors such as Wi-Fi, Bluetooth, and cameras; online browsing patterns, and competitive analysis. They can use this information to power new analytics to improve their supply chain by predicting demand more accurately and to provide more targeted promotions to consumers across all shopping modes.

Transportation around the globe is now powered by the collection and analysis of distributed data sets to increase efficiency and safety, reduce emissions, and enable infrastructure planning. Public transportation relies on accurate system and passenger flow optimization to ensure crowds move fluidly and safely, while measurements of on-time performance, road and rail conditions, and traffic patterns can enhance multi-modal routing for freight.

In summary, IoT has started to enable exciting change across a range of industries. Dramatic reductions in the cost of sensors, networking, and computing has allowed for the collection of more data than imaginable only years ago. But real value is only delivered when the aggregation and analysis of those data driven action, whatever form that action might take: a setting automatically triggered at a device on the network edge, a precisely targeted and hopefully welcome purchase offer to a customer, or the redesign of a major industrial machine. With greater deployment of sensors and actuators, the integration of collected data sets, and continued advance in analytics and machine learning, IoT will deliver even greater value in the months to come.

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