In Analytics, Internet of Things


The Internet of Things (IoT) is rapidly changing the way business operations are monitored and managed. Connected devices detect and communicate the status of essentially any aspect of manufacturing, warehousing and distribution. Many of these same devices are also able to receive commands such as to open or close a switch or valve. As this digital transformation pervades throughout operations the speed at which adjustments and corrections can be made to improve processes, throughput and cost efficiency is becoming faster.

The increased speed of process throughput and improvement now exceeds the capabilities of traditional Business Intelligence (BI) systems offering “descriptive analytics” that are inherently retrospective. The traditional BI modus operandi was to review the output from analyses and then take corrective measures. The cycle time typically spanned days to more than a month. Nowadays with IoT, the cycle time is reduced to mere hours, minutes or even seconds.

This sea change poses challenges for BI solutions that were not designed for fast cycle times, much less immediate real-time processing of streaming data. Just about every operation today is awash in data and crunched for time.

The data problem will continue to pose ever greater challenges because:

  • The Internet of Things is expanding, which means that smart sensors will soon be almost everywhere, creating additional streams of continuous data.
  • New technology will measure data at ever finer intervals, such as synchrophasors used in the transmission and distribution of electricity that measure voltage up to 30 times a second
  • Lean operational processes, such as Kanban and flow, improve operations and just-in-time production and inventory, and generate large volumes of data in the process.
  • Digital customer service increases the number of touch points between customers and vendors, generating still more data.

For all this data to make an immediate impact on your operations, you need to be able to capture it, normalize it, and in many cases analyze it immediately.

This is where traditional (BI) solutions fall down. BI was not and is not designed for real-time analytics of large volumes of high-velocity data. It enables users to ask questions by querying their data, but leaves it to the user to convert the data-out responses to usable and actionable answers and then decide how to apply them. More specifically, BI systems were originally designed for producing data and reports, organized and visualized in presentable formats (e.g., tables, graphs). This was and still is a very useful and valuable, but it’s not the same as enabling ongoing and in many cases real-time operational process management.

To take a data-driven approach to improving operational efficiency, what you need is a more comprehensive analytics approach that integrates and analyzes multiple sources of data both in batches and in real-time to deliver insights that you can act on immediately to drive and/or fully automate business operations.

The need for a more comprehensive solution that transcends the now limiting capabilities of BI systems has been met by a new category of enterprise software solutions referred to as “situational intelligence” (SI). Situational intelligence is a superset of BI capabilities that adds analysis of operational systems with purpose-built advanced analytics that can consume any type of data: internal, external, structured, unstructured, big, streaming and more.

With access to all this data and an understanding of its contribution to the big picture, situational intelligence illuminates the what, where, when, why and how of every asset and situation to provide context needed to make fast and confident business decisions that lead to optimal actions and outcomes.

I strongly recommend that organizations not only adopt and operationalize advanced analytics, but do so within the context of SI solutions to thrive and survive as the IoT transformation continues to unfold.

That’s a bold statement, I know. In coming posts I’ll discuss specific use cases to show how situational intelligence optimizes operations, helps handle uncertainties that arise, and detects and corrects anomalies as they occur.

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