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Top 7 Product Data Management PDM Best Practices in 2024

Product data management, or PDM, is an essential part of any business since it enables: 

  • Data integration 
  • Smoother supply chain operations 
  • Efficient product development 
  • SEO 
  • And an overall higher customer satisfaction level. 

However, achieving effective product data management is easier said than done. You can implement a PDM tool, but just throwing money at expensive software will not make all your troubles go away. Effective product data management can be achieved by combining sophisticated software and strategic best practices.

This article explores the top 7 PDM best practices that business leaders must consider to ensure an effective product data management process in their supply chains.

1. Conduct product data transcription

A report1 by Verdantix surveyed 161 executives from the manufacturing sector and identified that more than half of the companies still used a mix of spreadsheets and paper to operate. This type of data requires transcription before it can be added to any PDM system.

As a best practice, managers should make sure that all spreadsheet and paper-based data within the organization is transcribed into digital form with consistency and quality. In large organizations, this process can be difficult to do since it becomes highly manual and repetitive. Tools such as OCR can be used, but even then, you might require human intervention. To overcome this issue, we recommend working with a crowdsourcing service.

2. Understand the business processes that need to be supported

You must understand how your product data is used across the organization if you wish to streamline the flow and management of that data. Understanding company procedures like marketing, sales, customer support, and operations that rely on product data is necessary to do this.

Once you know which business processes need to be supported by the PDM solution, you can determine the product data requirements for each process. This can help you filter out the unnecessary data fields and prioritize the essential ones.

3. Define your master data management strategy and structure

Your data model serves as an outline for the structure of the product data. How you will govern your product data moving forward is determined by your master data management plan. This covers details like who will have access to modify the data, how changes will be recorded and audited, and how versioning of the data will be managed. 

You can choose from the following data models:

  • Hierarchical model: In this model, the data has a parent-child-like relationship in which the data is arranged into a tree-like structure. For instance, a sales order has multiple sales items (Child entity) but can only be linked to one sales order (Parent entity).
  • Relational model: This model involves storing all related data in a single location. For instance, a supplier’s details are stored in one table, with its name, location, contact person, etc.
  • Entity-relationship (ER) model: This model breaks down your data into different categories to make it more organized. The categories are:
    • Entities: a product or a customer are separate entities
    • Relationships: The connections between the entities
    • Attributes: Something which describes the entity, for instance, product or customer name
  • Dimensional model: It enables teams to share information across many departments for efficient decision-making and cooperation. This model is mainly used by data warehouses. 

4. Identify which systems and data need to be integrated

4.1. Data integration

You need to also identify which data will be shared across different systems. After that, you need to automate the data-sharing process by using tools such as RPA to make sure the latest data is available throughout the organization.

4.2. System integration

System integration is one of the most important reasons for implementing a PDM tool. You can ensure that your product data is always correct and up-to-date and that all relevant departments have access to it by integrating your PDM system with ERP, PLM, CRM, and eCommerce.

5. Create a process for maintaining product data governance

In the current business environment, products and services are frequently changing. These changes are also reflected in their data. Having a proper product data governance mechanism in place can help you efficiently manage these changes. A governance strategy can contain the following systems:

  • Key roles, including users who are/are not authorized to make changes
  • How will the changes be made
  • A process for approving or rejecting changes
  • A system for tracking and auditing changes.

6. Select a PDM software that facilitates all of these best practices and keep it updated

A good PDM system can help you streamline your product data management process. Most of the software on the market offer system integrations and automated processes to help simplify your product data management process. 

Considering these best practices will ensure that your PDM tool and business processes are aligned and work in synergy. 

It can also be beneficial to dedicate a team that can regularly update and change the PDM system as the business requirements change.

7. Leverage dashboards and reports to track KPIs

Finally, it is important to track the performance of your projects, and product data is an essential element in measuring that. By using dashboards and reports that monitor KPIs on a regular basis, you can identify problems early and take action before they escalate. You can look for a solution that offers built-in dashboards. Some important KPIs for PDM are:

  • Data accuracy
  • Data timeliness
  • Data completeness
  • Errors in the data

Further reading

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  1. OREM, Utah (Sep 18, 2019). More Than Half of Companies Still Use Paper and Spreadsheets to Manage Supply Chain Risk, Creating Significant Issues in Managing External Contractors and Suppliers. Retrieved: Dec 07, 2022.
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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