Industrial DataOps: Making Digital Twins Into Digital Siblings
Digital twins are quickly becoming an essential component of digital transformation and industry 4.0, as they help organizations gain new insights into their connected devices and systems. The new challenge is ensuring that the digital twin accurately reflects physical reality and creating a "digital sibling" that can evolve and provide value independently.
This is according to SaaS company Cognite, and they believe that the key to unlocking the full potential of digital twins lies in Industrial DataOps. Enterprise value, according to Cognite, can only be facilitated by efficient data management across the ever-changing nature of physical equipment and production lines.
"Each sibling shares a lot of the same DNA (data, tools, and practices) but is built for a specific purpose, can evolve on its own, and provides value in isolation," said Fredrik Holm, senior vice president for strategic alliances at Cognite.
Holm explained that this highlights that a digital twin is not an isolated entity but interacts with other systems, an "ecosystem". Therefore, data must be collected and managed in a scalable way to support that ecosystem.
The data backbone of digital twins/siblings
Industrial DataOps is a distributed yet governed way of managing data that bridges the gaps between different silos. The discipline builds on the principles of DevOps but is tailored for industrial use cases, with specific emphasis on making the data available and usable across the entire organization. It seeks to break down those silos, enabling industrial organizations to better access, share and leverage data.
According to Cognite, this is the data backbone that digital siblings need to build on to become as efficient and effective as possible. The governance structure exists to ensure data products are composable and consistent with how humans think about data, a graph of interconnected physical equipment or systems. The focus should be on data lineage, access rights, and monitoring data quality to achieve this goal.
Holm argued that the goal shouldn't lie in the technology but in creating a product that gives company-wide data accessibility for developing products and making data-driven decisions. In this way, companies can deploy digital twins rapidly and uphold and support lean processes.
Cognite's DataFusion platform, for example, allows companies to define what data should be added to data products, which should have access to the data, and how it relates to business needs. It also allows for monitoring data quality and how the data product is related to other products. Finally, it helps keep the data custodian network connected so that innovation and collaboration across different data domains can be achieved.
Image credit: iStockphoto/jacoblund
