The biggest difficulty about turning a widget factory into a smart manufacturing powerhouse might not be setting up an industrial IoT or upgrading production lines. It could be…spreadsheets.
At its core, smart manufacturing and infrastructure is all about data, and the integration of IT and OT is the foundation to get there. Yet the biggest challenges enterprises might face aren’t technical, experts told IT Brew. They’re human.
Data interoperability? You wish!
Jonathan Lang, IDC Manufacturing Insights’ research manager , who runs worldwide surveys on IT/OT convergence, said companies commonly cite security and compatibility as their biggest concerns. But the most serious issue, he told IT Brew, can be reams of indecipherable, sometimes ancient spreadsheets.
“The software systems, the controls, the SCADA systems, the different historian systems, and other sorts of data stores that, in an OT environment, when those systems were originally purchased, configured, set up, and so forth, there wasn’t any sort of data governance, there weren’t many standards then,” Lang said. “Even the individuals that set those systems up and configure those systems…had no appreciation or no future view into the way that this data would be consumed.”
For example, Lang said, three engineers in the same department might have three totally different ways of logging data. This can naturally frustrate IT teams who traditionally manage big data and understandably hope it’s at least in a comprehensible format.
“When you have data that’s been historically configured basically in Morse code that an individual engineer understands because they’re the ones who set the system, and they’ve traditionally exported that data into an Excel spreadsheet and run their own process to collate, and organize, and decipher this data,” Lang said, “What you end up with today is…totally incomprehensible.”
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Some companies have turned to machine-learning to extract and organize OT data, McKinsey associate partner Michael Chang wrote to IT Brew, but the process still involves a “significant amount of manual effort.” Businesses can save themselves some headaches by redefining “how they interact with data and the dashboard,” for example, by making sure those three engineers start recording data in an identical manner.
Turning on the data pipeline isn’t just flipping a switch
Both Chang and Lang recommend that enterprises approach IT/OT transformation with a realistic—and incremental—mindset.
“Agile does not mean the full solution will be faster to release (it might do the opposite), but it allows for active collaboration between the digital and business teams to pressure-test and validate which functions are truly necessary and which can simply be integrated,” Chang wrote.
Lang added that organizations should keep in mind that they can’t jump straight to analytics—first, they have to reckon with how to absorb a backlog of messy records.
“Just be prepared for the fact that this data is going to be poorly structured, if it’s even available, [and] unusable without some tribal knowledge from operations,” Lang said. “And that what they need to build for their organization is an effective data ingestion capability for OT data.”—TM
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