Determining who should get what IIoT data when is a prerequisite for industrial organizations looking to transform their business.
The way a car maker applies paint to its vehicles might seem routine. But there’s a good chance that the company’s painting technique is rooted in decades of trial-and-error experimentation. Because car manufacturers are on a continuous quest to surpass consumers’ expectations for durability and appearance, it follows they would want to keep their proprietary painting techniques — along with details related to, say, prototypes of future vehicles secret.
Car makers — and industrial companies at large — are making expanded use of connected technology to drive the efficiency of production while limiting downtime. This expanding connectivity heightens the need for clear-cut data governance framework. For example, a car manufacturer might be inclined to share certain operational data from robotic spray painting equipment with the maker of that device while also working to protect sensitive information related to intellectual property. “You want that device to be connected because you need that manufacturer to remotely monitor, diagnose and fix problems with that robotic arm,” said Theresa Bui, direct of IoT strategy at Cisco. “But you don’t want to share data back to the device manufacturer that might even hint at how many layers of paint that robotic arm is spraying on your cars because that is proprietary information.”
Having a data governance framework is not just a vital for automakers but for industrial companies of all stripes. While many industrial firms now have experience deploying IoT projects, they are more often focused on driving constrained operational efficiencies rather than organization-level transformation.
“We see some of our customers make the digital transformation leap, but it is costly,” said Syed Zaeem Hosain, CTO of Aeris. “It is not something you do on a whim.”
Cybersecurity and privacy worries remain two of the chief hurdles to IoT adoption, and by extension, broader digital transformation in the industrial realm. A 2017 European-focused Accenture survey revealed that the topics were top of mind for 70 percent of 250 industrial leaders. Other research involving North American and Asian industrial companies have reached similar conclusions, including The IoT Institute IoT Implementation Practices Survey from last year.
Organizations that manage to not just connect their equipment but configure individual devices to work seamlessly and holistically together stand to gain a competitive advantage, Bui said. “The question becomes: How do we scale [an IIoT deployment] so that all of our connected things on the plant floor are working seamlessly together?” Bui said. “It is one thing to have a connected conveyor belt. It is another to align all of your industrial devices and analyze all of your data in context.” Once industrial devices are connected and capable of sending and receiving data, the next step is optimizing that data for to help the organization increase performance and ROI at scale, Bui said.
Sometimes, the impetus for digital transformation can be an external force such as regulation forces organizations to come up with a digitally-driven data governance framework, said Hosain. “Look at the trucking industry. Two decades ago, the industry was still very manual and paper-oriented.” But the Electronic Logging Devices (ELD) Mandate in the United States has forced most trucking firms to perform electric logging of all of the data and the assets. “This federal requirement is prompting massive change,” Hosain said, noting that simple mom-and-pop trucking operations have transformed into multibillion-dollar multi-truck businesses based on information sharing prompted in part by the anticipation of ELD regulation and the possibilities enabled by it.
But when looking at the broader industrial landscape, it is apparent that most organizations have learned how to connect their assets, a minority has devised a long-term vision of how connected technology can inform their business model and the evolution of their company. One of the reasons why is that data extraction is a challenging subject. “If you are a plant owner, you have all of these connected devices made by different device manufacturers,” said Bui. “Guess what? Each of those devices has a different data model and requires different extraction techniques.” Industrial professionals that have figured out how to connect things are now confronted with the challenge of harnessing data, figuring out where that data goes, who gets it and how often. Making that determination is a tradeoff between convenience on the one hand and privacy and secrecy on the other. While IIoT technology can give your organization unprecedented capabilities to understand your operations and customers, you don’t want that information to end up in the wrong hands.
Original source: Internet of Things Institute