Operational Intelligence for
the Internet of Things

The Internet of Things is growing exponentially—
and so is the need for operational intelligence.

Operational Intelligence for a Waterfall of Sensor Data

The Internet of Things (IoT) refers to embedding sensors, computing, and connectivity in many of our everyday devices, such as thermostats, lighting, cameras, and appliances. This opens the door to an unprecedented level of automation and personalization. However, without a scalable way to track and analyze all of the data being produced, these benefits cannot be fully realized.

“In 2023, IoT Analytics expects the global number of connected IoT devices to grow another 16%, to 16.7 billion active endpoints.” – IoT Analytics

In-memory computing meets the challenge by simultaneously tracking millions of intelligent devices and analyzing telemetry in real time. This provides the operational intelligence required to react to changing patterns, optimize behavior, and head off impending problems. Operational intelligence unlocks the power of the IoT.

It’s Not Just Devices

The IoT includes more than intelligent devices in the home. Machines on a manufacturing floor, a fleet of rental cars or trucks, and even a windmill farm benefit from operational intelligence.

In-Memory Computing: A Perfect Fit for the IoT

With its ability to quickly update data, analyze it in parallel, and run in live, mission-critical environments, ScaleOut’s in-memory computing provides an ideal computing platform for the IoT.

How In-Memory Computing Enables the IoT

Analytics strategies that focus on analyzing static data, such as last week’s machine logs, can help identify long term trends, but they cannot react fast enough to capture business opportunities in the moment. The IoT needs real-time analytics that can track live, continuously-updated data with extremely low latency.

The secret sauce that makes this possible is in-memory computing. This technology has been used for more than a decade to store and update large, fast-changing data sets, such as shopping carts and financial data. More recently, ScaleOut Software introduced scalable, in-memory computing capabilities to give this computing platform the horsepower needed to rapidly analyze live data and provide immediate feedback. For example ScaleOut StateServer Pro can analyze a terabyte of continuously changing data in a few seconds or less.

As illustrated in the following diagram, ScaleOut StateServer Pro’s in-memory data grid tracks real-time state changes generated by an array of intelligent devices and maintains an in-memory representation of the state of these devices, enriched by historical information held on disk. It then uses data-parallel analytics to look for patterns and generate real-time feedback as required, providing operational intelligence.


Using In-Memory Computing to Analyze Data from the IoT

The Possibilities are Endless

The use of operational intelligence with the Internet of Things has countless possibilities. Imagine a commercial food processor that streams sensor updates from its machines into a real-time analytics cluster to predict and avoid failure scenarios. A rental car company can use operational intelligence to track vehicles, look for lost or erratic drivers, and anticipate deviations from safe practices. A brick-and-mortar retailer can improve its competitive edge by using operational intelligence to precisely track RFID tags attached to inventory and thereby lower costs while enhancing the customer’s shopping experience.

In short, operational intelligence gives companies the means to leverage the fast-changing data created by the IoT, and it empowers them to optimize business processes and grow profits.

So what are some examples of operational intelligence being used in the IoT? Imagine a commercial food processor that streams sensor updates off of its machines into real-time analytics cluster to instantly predict failure scenarios. Or imagine a large cable provider analyzing user data coming off of set top boxes in real-time and delivering content recommendations immediately based on that analysis. Finally, imagine a brick-n-mortar retailer — competing with e-commerce — using operational intelligence to combine a consumer’s location with her shopping history to deliver a personalized shopping experience. A sales associate, armed with a tablet, could discover the consumer’s preferences, sizes, etc. before they’ve interacted. In short, operational intelligence gives companies the means to leverage the fast-changing data created by the IoT, and it empowers them to enhance the customer experience, optimize business processes, and grow profits.

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