How to incorporate master data management (MDM) into your information system strategies

September 14, 2023

Master data management (MDM) used with digital asset management systems (DAMS), product information management systems (PIMS), or customer relationship management (CRM) platforms can greatly enhance an organisation’s data quality, consistency, and decision-making capabilities. These systems serve distinct but interconnected purposes, and when integrated effectively, they can create a powerful data ecosystem that maximises the value of information across the organisation.


Image credit by Shubham Dhage on Unsplash


We have identified 10 strategy ideas for incorporating MDM with DAMS, PIMS, or CRM systems to empower your organisation. Don’t feel overwhelmed, your business doesn’t necessarily have to implement everything! Select a few options, then contact Informed Byte for a free initial discussion and consultation.


1. Understanding the Components:

  • MDM: MDM involves creating a single, accurate, and authoritative source of truth for essential business data, such as customer, product, or employee information.

  • DAMS: DAMS is designed to store, organise, and manage digital assets like images, videos, documents, and other multimedia files.

  • PIMS: PIMS centralizes product-related information, including specifications, pricing, descriptions, and other relevant data.

  • CRM: CRM platforms manage customer interactions and relationships, providing insights into sales, marketing, and customer service activities.


2. Data Mapping and Integration:

Integrating systems requires a comprehensive data mapping exercise. Identify the key data entities in each system, such as customer profiles, product information, or digital assets. Develop a mapping strategy to align data fields and attributes across systems to ensure consistency.


3. Data Governance:

Implement robust data governance practices to maintain data accuracy, integrity, privacy and regulatory compliance. Establish data stewardship roles to oversee data quality and resolve discrepancies. Create data governance policies and standards that define data entry rules, naming conventions, and data update procedures.


4. Data Cleansing and Enrichment:

Before integrating data, cleanse and enrich it to eliminate duplicates, inaccuracies, and inconsistencies. MDM tools can automate data cleansing processes, ensuring that only high-quality data enters the integrated ecosystem.


5. Master Data Hub:

Set up a centralised master data hub that serves as the authoritative source for common data elements shared across systems. This hub is managed by MDM tools and ensures that changes made to master data are reflected consistently across DAMS, PIMS, and CRM systems.


6. Real-time Synchronisation:

Enable real-time or near-real-time data synchronization between systems. This ensures that the most up-to-date and accurate information is available across all platforms, enhancing decision-making and customer experiences.


7. Cross-system Data Validation:

Implement cross-system validation rules to maintain data consistency. For instance, when a new customer is added in the CRM, the MDM system can validate the customer’s information against existing master data to prevent duplication.


8. Metadata Management:

Develop a robust metadata strategy to manage data descriptions, relationships, and attributes. Clear metadata enhances data discoverability and usability within DAMS, PIMS, and CRM platforms.


9. Workflow Automation:

Integrate workflow automation tools to streamline processes that involve data entry, approval, or updates. For example, when new product information is added in the PIMS, automated workflows can trigger approvals and data propagation to other systems.


10. Knowledge Management and Training:

Promote knowledge management practices to facilitate user adoption of the integrated ecosystem. Provide training to employees using these systems to ensure proper usage and adherence to data governance policies.


Next Steps

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