What are the key PDM skills and competencies that you look for in your PLM team members?

Data quality is 50% of the PDM/PLM challenge, the other 50% being the information flow architecture (not just system but process and people). Data quality is a challenge because the people who create the data often don't see it as a priority (they are busy thinking about Engineering, sourcing, manufacturing etc). It is a priority for the people who 'police' data quality, but are often powerless to author data. Therefore, in the same way that governments try to influence the health of a nation, organisations need to influence the creators of data, so that the information is regarded with the same respect as the product, contracts, factories etc themselves. This is not just a matter of policy or rules but human behavior.

The data quality is important to make a right decision. It has to be captured at the right time of the process. I have seen the plm implementation where the system forcing people to add fake data as at the specific time and specific context , the users do not know the value

In addition to what's mentioned, PDM / PLM consultants should understand the strategic importance of data quality. They must align data quality efforts with broader business goals, ensuring that the systems support decision-making, innovation, and competitiveness. This involves establishing clear data governance frameworks, assigning ownership, and promoting a data-driven culture within the organization. Staying current with industry standards and emerging technologies is essential for adapting data quality practices to evolving business needs. This proactive approach can transform PDM / PLM from a mere tool into a strategic asset that enhances efficiency and drives innovation.

Data integration in PDM/PLM involves seamlessly connecting different aspects of product development. For example, it enables engineers to access manufacturing data directly, ensuring designs align with production capabilities. This streamlines workflows, reduces errors, accelerates time-to-market, and enhances overall product quality. Expertise in APIs, middleware, and ETL processes is crucial for this. Successful integration optimizes processes, enhances collaboration, and facilitates data-driven decision-making, ultimately boosting product lifecycle efficiency.

Now a days you should not create / replicate the data to different system unless it is required for processing in the target system. If it is just for information or to bring the context, use mashup of data from the source / origin of the system

Data collaboration in PDM/PLM plays a pivotal role in New Product Development (NPD). For example, during the design phase, cross-functional teams can collaboratively review 3D CAD models in real time (using Product view in Windchill or JT2Go in Teamcenter). Engineers, designers, and suppliers can provide instant feedback, identify issues, and make necessary adjustments. This agile collaboration accelerates design iterations, reduces errors, and ensures product designs align with both technical specifications and market demands. Effective communication and the use of collaborative design tools are vital for seamless NPD data collaboration.

Data is used across the business value chain. It can be internal or external and is very important to have a data collaboration in conjunction with standard data exchange format. The people who are passion about PLM must be aware of these capability and standards

Analysis of the data from the field is a critical step in continuous improvement of the products as well as the lifecycle management process. This needs to include field performance data and the as designed versus as used. This is of specific importance if you have plans to build Digital Twin of the product.

While individual steps to maintain data quality, integration, security, collaboration, analytics & innovation are crucial, focus on maintaining product's integrity, quality, competitiveness, and relevance should be the top priority. For example in clinical trials analysis, it's not just about converting data into CDISC standards, producing 100% accurate query reports, or applying necessary blinding techniques. It's about how efficiently & effectively you can do all these things to make your research study data comprehensive & robust. Every step, from data collection & cleaning to defining data features and performing advanced analytics, is crucial. The goal is to develop a team that excels in all areas & ensures efficient product lifecycle.