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Harnessing IoT at the Edge to Deliver the Autonomous Digital Enterprise of the Future

Illustration: © IoT For All

Over the next five years, businesses will continue generating massive amounts of data from Internet of Things (IoT) connected devices. While much of the IoT conversation has focused on said devices, we’re seeing a shift as more enterprises become data-driven. Business owners now realize the greatest opportunity to accelerate their business comes from the data being generated, not the devices themselves.

As organizations turn to IoT to accelerate business initiatives, IT leaders must ensure devices, architecture, automation, and human intelligence are working in harmony to create superior employee and customer experiences. In this way, IT leaders can drive operational efficiency, reduce time spent on mundane, administrative tasks, and fortify network security to deliver enhanced end-user experiences. This is the framework of the autonomous digital enterprise we’ll continue to see come to life in stores, schools, and cities in the years ahead.

We predict that by 2025 – just as big data is reaching unprecedented levels – technology will underpin every business function. IoT will be the foundation of this reality, and the companies that learn to harness it effectively will not only survive but thrive.

Defining IoT Edge Computing

The core of an IoT solution is typically a central IT system for storing, processing, and analyzing IoT data. Much of this IoT data is often located in the cloud, away from the core. This can lead devices to spend more time in the cloud, resulting in slower reaction times, less reliable operations, and numerous employee and customer frustrations. Edge processing can address these challenges.

Edge computing services and IoT are inseparable. Communications are facilitated by the edge computing services and digital transactions are facilitated by IoT. As a result, edge services act as a valuable autonomous digital source that can greatly enhance an organizations ability to process, store and analyze IoT-device data. This can help organizations more effectively manage and control those devices, protect them against vulnerabilities, and glean valuable insights from the data they generate.

IoT Data Processing at the Edge Remains Crucial

The term ‘edge computing’ is arguably as misconstrued as ‘IoT’ across the marketplace as it means different things to different people, organizations, and industries.

Edge computing typically applies to some element of processing, sensing, and actuating commands to monitor, control, and optimize certain functions at the point of origin with computation at the sensor or device-level and up-through other on-site infrastructure (server closets, gateways, etc.).

It could be a smart meter on an electric grid, a sensor on a remote oil rig, a CNC machine in an automotive factory or even the PLC controlling it and other machines in an assembly line.

The Autonomous Digital Enterprise and IoT Edge Computing

The ultimate autonomous digital enterprise will operate on a seamless IoT Edge-AI based computing solution that empowers IT administrators to:

  • Configure which data should be stored locally and set a data aging policy.
  • Define conditions with adjustable time windows to identify patterns in the incoming IoT data as a basis for automated events. For example, certain conditions can initiate transactions and notify appropriate parties.
  • Execute business transactions at the edge to provide continuity for critical business functions even when the edge is disconnected from the core.
  • Use predictive models that are constantly “being trained” for analyzing the IoT data. The predictive algorithm would be trained in the core and then applied to the edge.
  • Apply deep learning algorithms at the edge specifically for image and video analysis.
  • Visually inspect the data collected at the edge. For example, after an alert has been sent to the core, an analyst can dig into the details which led to the alert.

The capabilities listed above help organizations to seamlessly converge, analyze, and prioritize operational and IT data. As such, they can achieve two critical functions: (1) better prediction and remediation of potential problems before they lead to disruptions for customers and (2) ability to generate more valuable and actionable insights from data.

These two components are key to delivering a superior employee experience that retains and attracts talent, creating a transcendent customer experience that forges long-term loyalty, and ultimately, driving the business forward.

Conclusion

Looking ahead, we’re poised to see more devices deployed across the enterprise to support business needs, such as occupancy sensors, asset-tracking devices, industrial equipment monitoring, and other solutions aimed at ensuring productivity and efficient use of resources.

IoT edge computing is playing an increasingly important role in the enterprise as IoT technology becomes more entrenched in our daily lives. Some businesses may experience even more acute networking needs and device adoption if they choose to embrace long-term remote work or flexible work-from-home policies. As the type of devices and volume of data generated by them grows, edge computing will become more essential to maintain operational speed and efficiency across the business.

Today’s up-and-coming autonomous digital enterprises recognize that the devices are not the end game. With every organization becoming a data-driven technology company by 2025, the true market leaders will be the ones that strategically harness IoT to effectively collect, analyze, and apply vast amounts of data faster and more intelligently than their competitors.

Written by Sam Lakkundi, VP, Innovation and Head of BMC Innovation Labs, BMC Software

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