Moving towards “Engineering Knowledge Management” from “Engineering Data Management”
The word knowledge management is very frequently used, let’s put it, misused. The reason I am saying it is misused is because people often confuse between Data and Knowledge. Professor Ray R. Larson of the School of Information at the University of California, Berkeley, has defined the information hierarchy beautifully and explained the difference between Data, Information, Knowledge and Wisdom
- Data – The raw material of information
- Information – Data organized and presented by someone
- Knowledge – Information read, heard, or seen, and understood
- Wisdom – Distilled and integrated knowledge and understanding
Generally most of the PLM systems are also designed as Product “Knowledge Management” management system. But as said earlier these systems end being data management systems. All this data even though categorised, structured doesn’t translate into Knowledge. It is derived thru reports only as information.
Also there are other places where lot of data is generated related to product. That is mainly thru the feedback. The feedback comes in way of direct feedback thru feedback form or thru social media or indirect feedback thru the warranty requests. All these data translated into information. It is important that systems learn from these data & information points and learn itself to ensure that the Engineering Data Management systems becomes in true sense as “Knowledge Management” systems.
We need to look at analysis of all this data generated and which can be source for information related to product using latest trends as in Big Data. Even though data generated by PLM systems is not as same volumes and frequency assumed by Big Data Analysis, we need to look at these trends and adopt them to Engineering environment. It is also important to understand human reaction, analysis of this information presented and how this information is interpreted and reacted upon by humans & make system more intelligent to take present information with “experience”. To make these systems more intelligent we will have to look at Structured Searches, KBE, Big Data Analysis and many other such technologies / trends.
Conclusion: We need to really analyze the maturity of data and information generated in Engineering space and make the systems self learning and convert the Engineering data management systems to “Engineering Knowledge Management” system. These are my views…