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The industrial internet: Why data analysis is a critical success factor

In the latest part of his series about the potential of the industrial internet Ian van Reenen, vice president of engineering, endpoint products at Autotask, shares some more advice

It’s no secret that data mined from the Internet of Things (IoT) for Industrial Internet applications is having a growing impact on multiple business sectors worldwide. However, as IoT data use for these purposes becomes more common, it requires more sophisticated analysis to determine which data has real value, and how much value that data has. This is directly related to the installation of sensors and other instruments that empower data analysis to improve machine performance and efficiency.

Why You Need Real-Time Data from Remote Endpoints

How well industrial operations function depends, in part, on the value of data collected on a constant basis across the enterprise, from far-flung as well as close-in facilities. That applies to trains, transport fleet management, wind farms – indeed, to any business where the performance and maintenance of costly equipment must be monitored and managed. It’s the best way to spot and solve problems, then calibrate the right level of response.

Smart sensors are the conduit for the capture and discernment of remote data, given that they connect disparate devices and systems so that multiple machines can talk to each other. Manufacturers have the global ability to see what’s happening in all of their plants at the granular level, aggregate that data, do benchmarking and put the data in context so that they can make informed decisions about their operations. It’s not the data that makes the difference—it’s having the data in an actionable format that is important.

Non-manufacturing industries also benefit from sensor-based devices. Take healthcare, for instance, where miniature, high-resolution, at-a-distance detectors worn by patients can help them manage chronic conditions like diabetes and heart disease or provide early warnings for developing medical problems. The resulting predictive analytics could tell doctors about potential medical crises before they happen, allowing early intervention through personalized, real-time care recommendations for patients.

Cost-avoidance figures heavily into the value of data gleaned from smart sensors,too. Manufacturers can move from scheduled maintenance to the predictive maintenance that uses high-quality data that signals when to service equipment before it fails. Intelligent instrumentation shrinks the replacement asset value (RAV), the universal benchmark measure of operating asset performance success, by cutting down on unexpected downtime and premature parts replacement and extending regular maintenance intervals. The difference between a 20% and a 2% RAV is the difference between having to build a new plant because of obsolescence every five years and having to build one every 50 years.

On-target data analysis also tells you what your customers are buying or using, and their demand for those products. For example, whether business customers have the right amount and type of printer supplies or whether hospital customers have the right medications and service delivery networks in place to meet patient and physician needs.

Much of this analysis is about spotting and responding to trends, spawning what’s now known as data mining. Using a hospital example, data mining could quickly aggregate data on what medicines have been administered and let providers interpret the data to gauge the effectiveness of—and need for—those medications.

Where Machines Fit In

In a sense, the developing data mining industry is evolving like so many others—from human, or manual inputs and decision-making to automated production. Hence, there is the increasing influence of artificial intelligence (AI) in the form of automated (machine) data analysis and follow-up recommendations. Massive, cloud-based data farms and online data stores—think Amazon’s or Netflix’s web service or the Google app engine (deployed in products such as Google’s self-driving car)—are operating with optimal computing power so they can sift through seemingly endless data streams and suggest customer/consumer responses to it.

This machine learning, using algorithms that iteratively learn from data, lets computers pick up on patterns to find hidden knowledge without being specifically programmed to look for it. This process isn’t new—what’s new is applying these complex mathematical calculations quickly and automatically to bigger and bigger data sets, returning accurate results with surprising speed.

A textbook example of this is the transportation industry. Online purchasing of courier services has heavily embraced mobile device usage (e.g., tablets to capture signatures) to track parcels, personnel and vehicles at every point of the supply chain. This forms the heart of what IoT is becoming, with sensors scanning barcoded products and transferring that information to the cloud.

Other industries are deploying automated data analysis for their own purposes, to prevent fraud and identify investment opportunities (banks), analyze buying history to personalize a shopper’s experience (marketing and sales) or find new energy sources and streamline its distribution (oil and gas).

The evolution from personal to digital interactions to track, capture and contextualize the data journey and subsequently inform better decision-making has become inevitable, and its potential reach is limitless.

For more details about Autotask click here

This was last published in December 2016

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