Asset Data Model Blogs

You’re Building the Wrong Twin!

Everyone is talking about Digital Transformation, Industrial Analytics, IoT, and Industry 4.0. Much of the discussion advocates a crawl, walk, run approach by taking on the low hanging fruit of smaller, limited use cases using Proof of Concepts (POCs). This typically requires a lot of data wrangling to be completed before the use case can even be executed. More often than not, these data wrangling activities enable just one use case. So when things change operationally, applications fail and they lack the scalability to be deployed enterprise-wide.

If you find yourself manually mapping data, building specialized integrations, or manipulating 40,000 row X 26,000 column spreadsheets then you need to consider whether the solution will scale across your organization and provide you with the runway that supports you as your operations and use cases change over time.

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Using Asset Data Models to Empower Your Industrial Organization

When I speak with CIOs and their staff, the topic of digital transformation always leads to a discussion around how time-series data is the starting point, but it’s difficult to work with and organize in a way that represents how equipment and assets exist in the physical world.

Industrial companies have begun to address the problem by adopting Asset Data Models, which represent the physical structures and relationships of industrial equipment and processes. Asset Data Models are crucial for equipment benchmarking, cross-site comparisons, and underpin every kind of analytics. 


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