A go-at-it-alone approach won’t work

Of all the new capabilities that manufacturers can gain by adopting Industrial Internet of Things (IIoT) technology, data analytics can be among the most powerful—and the most challenging. Manufacturers make things and, as such, they’re accustomed to working with materials and machines, not bits and bytes. Though it’s true that many have experience working with data from ERP systems and MES, few have the internal resources, skills and knowledge to capitalize on the high-volume data streams of IIoT analytics.

Compounding the challenge is a talent shortage. Pure-play data companies such as Google, Amazon and Facebook are more likely to appeal to the best and brightest. They have the data that talented data scientists want to work with, can (and do) offer generous salaries, and are located in high-tech, fast-growing cities. Manufacturers will find it difficult to compete: They’re not viewed as data-centric companies where data scientists can thrive, are unable to meet or beat the competition’s salary, and tend to be located far away from high-tech centers.

Manufacturers simply aren’t going to be able to hire their way to a data-centric future. To bridge the divide between what manufacturers know they must do and what they’re able to do, they’ll need to work with partners and consultants—at least until a critical mass of data scientists becomes available or they can upskill their process engineers in the data sciences.

But that begs the question: If a manufacturer doesn’t have expertise in the IIoT and data analytics, what do they look for in a partner?

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