concept#Data#Analytics#Data Management#Data Quality
Master Data Management (MDM)
Master Data Management (MDM) manages and harmonizes critical enterprise data across different systems.
MDM is a strategic approach to managing an organization's core data sources.
Maturity
Established
Cognitive loadMedium
Classification
- ComplexityMedium
- Impact areaBusiness
- Decision typeDesign
- Organizational maturityAdvanced
Technical context
Integrations
CRM SystemsERP SoftwareBI Tools
Principles & goals
Uniqueness of DataAccessibility of InformationData Security
Value stream stage
Build
Organizational level
Enterprise
Use cases & scenarios
Use cases
Scenarios
Compromises
Risks
- Data Loss During Migration
- Faulty Data Integration
- Insufficient Staff Training
Best practices
- Conduct regular data reviews.
- Establish clear data policies.
- Ensure staff training.
I/O & resources
Inputs
- Existing Data Sources
- Technological Infrastructure
- Resource Allocation
Outputs
- Cleansed and Harmonized Data
- Central Data Platform
- Real-Time Data Analytics
Description
MDM is a strategic approach to managing an organization's core data sources. It ensures data consistency and supports decision-making through accurate and reliable information.
✔Benefits
- Improved Data Quality
- Increased Efficiency
- Reduced Costs
✖Limitations
- High Implementation Costs
- Complexity of Data Integration
- Lack of Employee Acceptance
Trade-offs
Metrics
- Data Error Rate
Measure of the accuracy of data.
- Time to Integrate Data
Time taken to integrate data.
- User Satisfaction
Degree of satisfaction of users with the provided data.
Examples & implementations
Case Study A
A company implemented MDM to improve data integrity.
Case Study B
Optimization of supplier data through a new MDM system.
Case Study C
Customer data was successfully unified.
Implementation steps
1
Analyze the existing data infrastructure.
2
Develop an implementation plan.
3
Conduct the trainings.
⚠️ Technical debt & bottlenecks
Technical debt
- Legacy data systems.
- Lack of system compatibility.
- Insufficient data security measures.
Known bottlenecks
Data InconsistenciesHigh Complexity of Data IntegrationLack of System Compatibility
Misuse examples
- Not reconciling data from different sources.
- Not allowing user customization.
- Accepting insufficient data quality.
Typical traps
- Creating excessive complexity.
- Lack of documentation of processes.
- Ignoring user feedback.
Required skills
Data AnalysisProject ManagementSystem Integration
Architectural drivers
Real-time Data AvailabilityScalability of SystemsIntegration of Existing Systems
Constraints
- • Compliance with Legal Regulations
- • Technological Constraints
- • Budget Constraints