Industrial Internet of Things brings opportunity, but ripples in the road ahead

Data integration and analytics are the biggest challenges facing the Industrial Internet of Things.
Published: Thu 15 Sep 2016

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The Industrial Internet of Things – i.e. the Internet of Things at industrial scale and encompassing critical infrastructures such as the grid – is set to be the next great technological advance of our era, revolutionizing the way businesses operate.

While the utility sector has been, and continues to be, at the forefront of these developments with smart metering and the smart grid, and for example the emergence of grid edge intelligence, other industries are starting to follow suit.

A new survey of more than 100 senior IT executives spanning a range of industries from data integration specialist Bit Stew Systems, finds that almost one third have already initiated some level of IIoT implementation and one quarter are planning it, while a further third are considering it. That leaves little more than 10% who are not prepared at all.

The reasons are primarily to improve operational efficiency and reduce operational costs (50% and 39% respectively) – indeed almost half cite missed opportunities to make strategic business decisions as the greatest risk of not having an IIoT strategy. Other reasons include creating new revenue streams, instigating innovation, improving uptime and optimizing asset utilization.

Data challenges for businesses

But the survey also finds that adoption is being slowed because most organizations are struggling with the volumes of complex data that come with the IIoT. For example, 39% are challenged with integrating data from disparate sources and systems, while one third cite inability to get actionable details from the data and a similar proportion profess to being overwhelmed by the complexity of the data.

Overall, less than one third have a high degree of confidence in their current ability to accommodate the scale of IIoT data integration. And disturbingly the confidence levels drop as projects are implemented – at about half the level of more immature project executives – and with progress into a project.

The top reason cited for the data challenges is lack of access to the right skill sets, but also the costs of software and the data storage and bandwith requirements to handle the data are key issues.

Data operations opportunities

In an interview to review the findings, Sandy Mangat, product marketing manager at Bit Stew, told Engerati that they confirm the anecdotal evidence from customers and the potential for the IIoT if the data challenges are overcome.

“The IIoT is very much a buzz phrase and the survey validated that there is real opportunity and potential with significant benefits for businesses,” she says. “But it is still quite immature with about 30% as the early adopters and it will be critical to overcome the data integration challenges.”

Mangat explains the challenges as part of the natural maturity cycle – at the lowest end in struggling to get data, then struggling to integrate it, then enabling the right kind of analytics and finally providing the operational intelligence to those who need it.

“Accessing the right skill sets should alleviate some of the burden but the data platform and software is also key,” she says, pointing to semantic data organization and machine intelligence as the only recommended solution.

“Traditional methods are unable to handle the volumes and complexity of the data and the latency requirements, whereas with machine intelligence, the more data it is fed the more effective it becomes, ultimately becoming predictive.”

She adds that it is also important to have a holistic overview of the business, which machine learning can provide. For example, in the case of a utility, of the whole grid so that in the case of a problem, its root cause can be properly identified.

The data integration choice

When it comes to selecting a data platform Mangat says that in addition to machine intelligence it should be customisable to specific use cases, as these will vary both across and within industry verticals, and have a high degree of automation.

As an example, she cites the case of a large east coast utility, where Bit Stew was able with one engineer to integrate 54 data source systems in 1.5 weeks, whereas with traditional methods the job would have taken a team of engineers several months.

“Speed gives a competitive advantage and even a small improvement in efficiency with for example, a wind turbine, can result in millions of dollars in improved income.”

With utilities under growing cost pressures, every dollar counts. And given the challenges around data, and with fewer than one third of organizations having a high degree of confidence that their data integration tools can accommodate the scale of IIoT data integration, the pace and extent of IIoT development would appear now to be in the hands of savvy and competent data integrators.

For more on IIoT applications in critical grid infrastructure, join Engerati’s In-focus programme IoT - the path to the intelligent grid.