You should definitely avoid these mistakes when maintaining master data
Challenges
- Disparate and inconsistent data landscapes:
Disparate systems, inconsistent formats and media disruptions make it difficult to use reliable data. - Manual processes and lack of automation:
Time-consuming data maintenance ties up resources - errors creep in and quality suffers. - Regulatory pressures and lack of governance:
Without clear accountability and processes, gaps in compliance and data security arise. - No unified data strategy:
Responsibility for data often stops at the departmental level - the big picture is missing. - Untapped digital potential:
Legacy data structures hinder modern, AI-enabled processes and data-driven business models.
Solutions
- Clear data quality:
Automated processes and centralised governance ensure a consistent, valid database. - Efficient data maintenance:
Intelligent workflows and validation rules minimise manual effort and error sources. - Security & Compliance:
Clear roles, documented approval processes and validation rules build confidence in the data. - End-to-end lifecycle management:
From creation to archiving, your master data is managed in a holistic and structured way. - Future-proof through structure:
Prepare your data landscape for digitalisation, AI and automation.
Definition & Meaning of Master Data Management
What is MDM?
Master data management (MDM) describes the strategic approach to company-wide organization, quality assurance and maintenance of master data. The aim is to create a uniform, reliable database that consistently supports processes, decisions and analyses - regardless of systems or departments.
What is MDG?
MDG (Master Data Governance) is a tool within Master Data Management (MDM). While MDM forms the strategic framework for handling master data, SAP MDG provides the technical solution to implement this framework - for example through rules, workflows and cross-system data distribution.
MDG (Master Data Governance) is a tool within Master Data Management (MDM). While MDM forms the strategic framework for handling master data, SAP MDG provides the technical solution to implement this framework - for example through rules, workflows and cross-system data distribution.
Why master data management is essential for modern companies
High master data quality is the backbone of efficient business processes and data-driven decisions.
At the same time, regulatory requirements are increasing and digital transformation demands intelligently structured, up-to-date data. Organisations are facing typical challenges:
The foundation for MDM
What is the meaning of data quality?
A central, trusted database is the key to enterprise-wide efficiencies, informed decisions and a compliant IT architecture. Data governance and data quality form the solid foundation of any successful MDM strategy - and make organisations ready for the data-driven future.
Good data quality means:
- Accuracy, completeness, timeliness, consistency, unambiguity
- Minimisation of errors, redundancies and outdated informationWhat is data quality?
Data Quality Management (DQM)
Structured data quality management (DQM) ensures that requirements for good data quality are systematically met - through clear rules, responsibilities and technical checking mechanisms.
Efficient methods for ensuring and improving data quality:
- Data cleansing, standardization, validation, profiling
- Automated checks and continuous monitoring
What is a Data Governance Framework?
Data governance defines how data is handled within an organisation - organisationally, technically and legally. It creates the conditions for data to be used centrally, securely and wisely.
A governance framework defines rules, roles and processes for handling data:
- Define responsibilities
- Ensure compliance
- Integrate data security and lifecycle management
Core functions of an MDG system
Systems Integration & Data Modelling
- Standardized data structure for ERP, CRM, BI etc.
- Avoidance of silos through centralized data storage
Quality assurance and automatic error handling
- Validation and cleansing rules
- Regular audits and reports
Master Data Workflows & Responsibilities
- Structured approval processes
- Clear assignment of roles for maintenance and approval
- Employee training on workflow handling
Choosing the right MDG solution
Important criteria for tool selection
The right technology is the foundation for a successful MDG implementation. In addition to functionality, integration and scalability are critical.
- Scalability and flexibility
- Integration with existing systems
- Support for regulatory requirements
Successful Implementation of Master Data Management
Step-by-step to Data Success:
- Establishment of clear governance policies
- Regular quality checks & Data monitoring KPIs
- Automated alerting and reporting
- Data minimisation & Data security strategies
Data-driven process optimization & SAP automation
Data as the key to efficient business processes
- SAP process automation using data analysis
- Use of RPA, AI and SAP Build Process Automation
- Optimized decisions & lower error rates
Master Data Maintenance in SAP S/4HANA
- Solution for efficient and compliant SAP Business Partner Management
Easy & Standardized management of customers, suppliers & contacts
Conclusion: Success with a strong data strategy
The path to data-driven excellence
A well-designed master data strategy is the foundation for efficient, secure and compliant business processes. Whether through classic MDM principles or SAP MDG:
When you control your data, you control your business.
Your contact person
Christian Wagner
Managing Director
Consultant Master Data Governance