Business Intelligence (BI) solutions have been around for a long time and have been applied successfully to numerical and transactional systems such as ERP (Enterprise Resource Planning) and CRM (Customer Resource Management). But they’ve fallen short with less hierarchical, more complex, and unstructured data sources like PLM (Product Lifecycle Management).
There are tangible, technical reasons behind BI’s shortcomings. Here is a summary:
- Engineering data is complex. It’s made up of varied configurations, geometric data and multi-discipline data. This data isn’t simple or hierarchical. It has inherent, networked relationships. Traditional BI systems are not designed to accommodate these types of relationships.
- The PLM data structure is multi-dimensional. Configurations and product structure data are not suited to traditional “data cubes.” They are much more suited to nodes in a graph database. Graph data analysis isn’t compatible with conventional BI tools.
- Need to unlock information from text. Semantics help unlock meaning from text, translating input from multiple systems, including documents and social media, into usable data. PLM analytics uses semantic clustering algorithms, leveraging machine learning to unlock hidden trends, knowledge, and insights.
- Performance is critical. Developing BI cubes is inefficient. They become very difficult to change after the fact, and they take too long to rebuild. PLM analytics satisfies new information demands in seconds instead of hours or days by analyzing indexed data, enabling iterative, “what-if” analysis.
- Geometry is complex. PLM analytics understands information in the context of an assembly and 3D space. It provides information graphically with appropriate 3D renderings. Analytics must be 3D model/CAD/product structure aware.
- PLM analytics includes more advanced techniques such as clustering data and predictive analytics including machine learning.
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