A few days ago, I wrote about Data Products. It’s an interesting trend that, in my view, can push new application development for engineering and manufacturing. For the last 20 years, we’ve seen the rise of increasingly large “platforms.” While these platforms offer extensive capabilities, they also create adoption challenges.
Think about how easy it is to use an iPhone app. Now compare that to an enterprise application. The contrast is stark. Enterprise applications are often complex, requiring extensive training and configuration before delivering value. So, how can we make enterprise applications easier to adopt?
Data Products and the Challenge of Data Modeling
One possible answer lies in Data Products. The idea is simple—break down monolithic PLM systems into smaller, more focused data services. These services can be easier to integrate and adopt. However, data modeling is a tricky part of this transition. How do we make data interoperable and self-explanatory?
Traditional PLM architectures often struggle with rigid data structures, making it hard to share and integrate data across different systems. A more flexible and connected approach is needed to make Data Products successful.
The Role of Graph Models
Graph models offer a promising solution. Unlike traditional databases, which separate data from its structure, graph models define both data and relationships using the same language. This makes it easier to connect, query, and analyze data across multiple domains.
A great example of this approach is discussed in the article From a Monolithic PLM Landscape to a Federated Domain and Data Mesh by Y. Hooshmand, J. Resch, P. Wischnewski, and P. Patil. The paper explores how OWL/RDF technologies can define data models that form a foundation for applications.
The key takeaway is that alternative data modeling techniques can offer more flexibility in managing and using engineering and manufacturing data.
The Next Step: Graph-Based Models for Data Products
Taking this idea further, we can think about using graph-based models to power Data Products. Each data service can be interoperable through a graph-based approach tailored to specific datasets. This approach allows companies to absorb data from multiple sources and turn it into targeted data services for specific business needs—such as Engineering Change Order (ECO) reviews, BOM cost analysis, or impact analysis.
By providing companies with graph-based data services, we enable them to move away from rigid, monolithic platforms and towards a more agile and connected data architecture. The result? Faster adoption, better data interoperability, and more powerful insights.
What is my conclusion?
To truly modernize enterprise applications in manufacturing, we need to rethink PLM data architecture. Moving away from monolithic PLM systems and embracing Data Products powered by graph-based models can unlock new possibilities. This shift enables companies to build modular, interoperable data services that make engineering and manufacturing data more accessible and actionable.
The future of PLM isn’t about building bigger platforms. It’s about creating smarter, more connected data services.
Just my thoughts…
Best, Oleg
Disclaimer: I’m the co-founder and CEO of OpenBOM, a digital-thread platform providing Collaborative Workspace with PDM, PLM, and ERP capabilities. With extensive experience in federated CAD-PDM and PLM architecture, I’m advocates for agile, open product models and cloud technologies in manufacturing. My opinion can be unintentionally biased.
