Why graph analyzes will rule PLM in the future?

PLM is all about data. It is about products, requirements, configurations, Bill of Materials, CAD Models manufacturing instructions and zillions of other documents. What is specially interesting about product lifecycle is the fact how data is interconnected. When you think about CAD model, assemblies and drawings, the relations are mostly obvious. Even so, PDM systems are working hard to maintain these relationships during the change process. However, let think beyond design and engineering department. Think about the whole lifecycle of the product. Think about usage of components on a global scale.Think about supply chain and design suppliers. Think about product behaviors in a socially connected world.

Data is complex. To "understand" data and find right relationships is a complex tasks. Try to use these relationships and contextual data to drive better decision process is even more complex. This is a right time to start thinking about graphs and this is where graph models come to place. This is a good time refresh your university notes about graph theory :). My recommendation is to add some practical sense to that and look on every day use cases like Facebook friends model and Page Rank.

I’m getting lots of graph-related links from big data publications these days. If you feel uncomfortable with the term big data (it trails too much hype these days), just think about data complexity beyond the level we can handle today with relational databases and excel spreadsheets. Earlier tonight, on my plane from San Francisco to Boston, I was reading Infoworld article – Graph analysis will make big data even bigger. Here is a passage I specifically liked:

Social networks transformed the Internet into a complex web of relationships; social graph analysis offers a way to understand those relationships. When it comes to social graph analysis, that task can be simple if you’re only interested in a few individuals, only investigating one type of connection among them, and only mining one static pool of behavioral data associated with them. On the other hand, if you’re trying to assess the shifting behavioral patterns of every possible relationship among every person, place, and thing on the planet, plus all the things they might be saying to each other, dynamically and in real time with perfect predictions about what they might do at every point in the future … you’re living in a science-fiction fantasy world.

The sci-fi fantasy is coming to our everyday life these days in many places. You don’t think about it, but it is around you in Facebook, Yelp, Twitter, LinkedIn and many other applications. However, it is not true when you come to your engineering office. In many situations you are surrounded by applications developed 10-15 years ago.

The enterprise software world s waking up to the potential of graphs analyses in a wide range of applications. It looks like a promising segment. These days is sold very often under "noSQL" umbrella. Oracle noSQL announcement few days ago is just one example.

What is my conclusion? Analyzing complexity is a big task. Nobody will disagree with the importance of the analyzes. However, the biggest challenge is to drive simple conclusions out of this complexity. You can get it easy from Facebook and Yelp pages as a recommendation what restaurant to go. Now think about simplification of design or supply chain process. In a future, PLM applications will need to handle more complexity, more data and do more analyzes. This is a way to make your application smarter. In my view, graph models will come to solve product lifecycle problem we cannot even think about today. Just my thoughts…

Best, Oleg

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