There is a lot going on in database space these days. Few days ago I posted – PLM, RDBMS and Future Data Management Challenges and I’ve got quite a few comments discussing multiple data management and modeling topics. My main point in that post was an alert to PLM to wake up and check how new technological development in database and data management can provide a competitive advantage or improve existing PLM solutions.
Today, I want to continue this conversation with discussion about different levels of data management and data models. I was reading dataversity blog post – The Data Model Pyramid. Take your time and read this post. In addition, you can navigate here to read related blog post by Steve Huberman – Key features needed in data modeling tools.
First take a look on the pyramid.
Clearly, two top levels – Business Subject Area model and Application Subject area model represent a specific set of data models required for any database driven solution. PLM is not an exception from the rule. However, high level of diversity in product development and manufacturing brought software vendor to develop their own tools for data modeling, which relies on the set of private data-management tools and abstractions. I found the following passage from Steve Huberman post interesting:
There are dependencies between the different types of data models shown in the pyramid, between data models and other artifacts or models that represent other aspects of business and requirements, the enterprise and solutions architecture, and application design. The activities required when producing and managing data models are only part of a wider set of business and technology activities; integration with associated activities is key to the success of data modeling.Without a tool that provides specialized support for data modeling, the data modeler cannot hope to work effectively in this environment.
Later in the article, Steve defines the set of features required from data modeling tools from different standpoints – core modeling, usability, integration, collaboration, management and communication. It made me think about what will happen tomorrow with PLM data modeling tools. It will be interesting to see if many years of private data modeling tools will come to sort of unification and standardization (yes!) on tools to deliver a variety of BSAMs and ASAMs. The key unsolved problem, from my perspective is the ability to populate and maintain multiple BSAMs tailored to specific business needs.
What is my conclusion? PLM was long time relying on private tools to manage and operate with data modeling delivered by vendors. I believe future of data modeling will provide a shift towards more openness in tools and, as a result of that, a shift towards faster data model tailoring, customization and efficiency. Just my thoughts…