Product-related data is one of the very aspects of PLM implementations. When you talk about PLM implementation, the topic of product-related data (or IP) is very often becomes a center of the conversation. There are multiple sources of this type of data in the organization. In my view, one of the PLM goals is to have a control of this data and provide tools to manage the overall lifecycle. One of the PLM implementation challenges is to provide wide support for product-related data. The topic I want to discuss is related the ability of PLM product to handle full scope of this product lifecycle data.
I had chance to read an article Oracle, SAP working on Exadata support. The core of this conversation is about how to scale up and provide extensive support for big data handling in the organization. Have a read of this article and make you opinion. Mine is simple — both Oracle and SAP understood the size of the potential problem (data size). They are working in multiple directions to find a solution for data sizing in transactional enterprise application. Should PLM care? This is a very good question in my view…
PLM and Product Lifecycle Data Problem
One of the challenges PLM is having for many years is getting control of product-related data. My observation shows that product-related data is not completely controlled by PLM systems in the majority of PLM implementations. Even with a very successful PLM implementation, data is scattered between multiple data sources and PLM is only one of them. In addition to that, product-related data can be located in the diverse set of applications used for product development.
Product Data, Size and PLM value
The full value of Product Lifecycle Management is directly dependent on how what scope of product-related data is covered by PLM. The wider scope can maximize PLM value for organizations. With all current developments, PLM is looking on starting from design to manufacturing strategies and development of social-oriented application, sizing can easily become one of the potential bottlenecks related to the ability to support large scope of data.
What is my conclusion? I think, to understand sizing of product lifecycle data is important in order to build right operational and strategic plans related to data management. Data is growing fast. Future PLM implementation can suffer from problems related to data sizing. How to scale up PLM implementation in terms of size can be one of the most important questions in the future. Just my thought…