At Altares, we regularly speak with companies that want to start with data management but aren’t sure how to approach it or when it becomes truly beneficial. There’s no fixed point at which an organization gains value from a master data management strategy, but there are several criteria that can indicate readiness.
Signs that it’s time for a master data management strategy
You're working with multiple systems and data sources
- For example: separate systems for CRM, ERP, e-commerce, finance, HR.
- Customer, product, or supplier data is stored in multiple places → risk of inconsistency.
Signal: You have duplicate customers or products, or reports differ between systems.
Data is crucial for your business processes or customer experience
Examples:
- Customer service needs quick access to accurate customer information.
- Incorrect product data leads to returns or misunderstandings.
- Finance faces fines or delays due to incorrect supplier data.
Signal: Data errors lead to operational or financial problems.
You have significant growth ambitions
- Mergers, acquisitions, international expansion, new business units, etc.
- More systems, markets, and people → greater risk of data confusion.
Signal: You are growing faster than your data structure can handle.
You want to digitize or automate
Think of self-service portals, AI, reporting, process automation.
Signal: Your innovations are held back by poor or inconsistent data.
Interesting read: From AI FOMO to smart sales: why good data and MDM are crucial
Step-by-step plan for MDM
Recognize these signs and want to start with an MDM strategy? Here’s a step-by-step plan to define a solid strategy:
Step 1. Define the objectives
- Why do you need MDM? Which signs do you recognize?
- Which problems do you want to solve? For example, duplicate customer records, incorrect product data, inconsistencies between systems, etc.
Step 2. Identify the key data elements
Examples:
- Customer data
- Product data
- Supplier data
- Employee data
Start small and choose a domain that has significant impact and is manageable; afterwards, you can expand it step by step.
Step 3. Involve stakeholders and create governance
- MDM requires collaboration between IT, business, data owners, and compliance.
- Appoint a data team that includes the various disciplines
- Assign data owners and data stewards to clarify responsibilities.
Step 4. Analyze the current situation (as-is)
- Which systems contain master data?
- Where are the bottlenecks (inconsistencies, duplicates, outdated data)?
- How is data currently managed and shared?
Interesting read: From noise to wisdom: How data leads to better decisions
Step 5. Define the future situation (to-be)
- Which data standards, formats, and rules do you want to apply?
- Where and how will the ‘single source of truth’ be stored?
- How will data be synchronized with other systems?
Step 6. Choose the right MDM solution/technology
Consider factors such as:
- Cloud vs. on-premise
- Integration possibilities with existing systems (ERP, CRM, etc.)
- Workflow and data quality tools
Step 7. Develop an implementation plan
- Start small with a pilot or proof of conce
- Iterative rollout (e.g., by data domain or department)
- Define KPIs (such as % data quality, number of duplicates, etc.)
Step 8. Establish data quality and data standards
- Develop validation rules, match/merge processes, and a uniform working method
- Use data quality tools to maintain the quality of the data
- Provide training to users to encourage correct data entry and raise awareness of the benefits it offers them.
Step 9. Monitoring and optimization
- Set up processes to track activities and maintain data quality
- Conduct regular reviews of governance, policies, and technology.
Conclusion
A master data management strategy is not an end in itself, but a necessity once data becomes fragmented, errors impact processes, or your organization grows and digitizes. Recognize the signs? Then it’s time to get started. This roadmap helps you step by step toward control over your data, and thus over your business.
