Important Things to Consider When Implementing IIoT

Important Things to Consider When Implementing IIoT, Advanced Analytics, and Big Data

Important Things to Consider When Implementing IIoT, Advanced Analytics, and Big Data
We are all inundated by IIOT, advanced analytics, and “Big Data” claims of transformative business value. Start now, start big, and move fast. This marketing pressure can be confusing, and result in misfires, disillusionment, and doomed initiatives that do not deliver on business value, consume scarce capital and, even more importantly, soak up organizational bandwidth with lost opportunity costs.

Industrial and commercial customers, however, should move cautiously. The significant challenges include cyber security and data governance; gathering, normalizing and integration of the IIoT “lots of little pipes” operational data with existing industrial data Operations Technology (OT) fabric from “big pipe” data sources like SCADA and DCS.

These are key considerations because companies are not just collecting industrial data to analyze at a future date, like ad companies trying to micro-target demographic segments. Their entire operation depends on real-time visibility and the ability to understand and control their processes safely and optimally in real time. Without rock-solid reliability, highly secure, and real-time visibility, productivity, financial viability—even the safety and health of their workers and communities where they operate—can be imperiled. A more conservative approach is required.

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Do not take this as a negative towards moving forward with IIoT and advanced analytics. There are many successful use cases that reinforce the opportunity.

For example, MOL PLC, a Hungarian inte­grated O&G company, has presented in sev­eral public venues the generation of over $500 Mn EBITDA by using advanced analytics for things like hydrogen em­brittlement corrosion and the application of machine learning in several refining processes. Oth­er areas include dynamic Integrity Operating Windows (IOW), and advanced predictive CBM. (Ref­erence OSIsoft 2016 UC)

It is more about how versus if. MOL, like other companies, used a very prescriptive approach to their IIoT, advanced analytics, and big data journey.

My Perspective on Doing Things Differently:

1. They did not forget that it is about delivering business value, supporting a business strategy, and achieving a return on investment, not applying IIoT and advanced analytics for technology sake. Start small and strategically with a sound business use case, end-user input, support, and joint IT/OT accountability.

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2. They started the journey of creating an operational data infrastructure (OT) as a foundational element of an overarching IIoT, advanced analytics, and big data strategy. If you listen to the marketing hype, you may have come to the conclusion that all of your data will end up in a data lake in the cloud, one massive (and growing) storehouse that will contain everything from sensor data to customer records. Successful implementations leverage fit for purpose technologies to address the unique characteristics and challenges of time series data.

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