If data is an asset that needs to be managed, then master data is the most strategic information asset in the enterprise. Master data is comprised of the organization’s core business entities, such as Customers, Suppliers, Products, and Materials. The objective of Master Data Management (MDM) is to consolidate this data from various sources across the enterprise and enable access to the highest quality, most accurate, and most current version of these key data elements.

SKML's clients have derived numerous benefits through MDM implementations. Examples include mastering Vendor data to eliminate the creation and maintenance of duplicate vendors and eliminate duplicate payments -- which were obfuscated prior to the MDM initiative. A clear, high-level understanding of Vendor also gives Procurement more negotiating leverage by providing a consolidated view of the total amount of purchases on an enterprise-wide basis.

Customer master data provides transparency of critical Customer data to optimize pricing decisions. Margins can be improved by marketing and selling to higher-margin customers. Our clients have also seen revenue enhancement from improved data quality for CRM. This often comes through more efficient customer service design, productivity improvement for customer facing staff, and a reduction in indirect marketing costs through elimination of duplicate customer records.

Key considerations when planning for MDM include:

• The MDM architecture and technologies that are most appropriate for the organization
• Alignment with data governance and stewardship:
  • To build consensus on data values -- names, codes, definitions, usage guidelines;
  • To define valid formats and ranges;
  • To identify systems of record;
  • To define usage rights and policies, resolve issues, and monitor compliance
• Alignment with enterprise data modeling:
  • To consistently define master data entities and attributes
  • To align ontologies with the enterprise semantic view
• Data Integration Architecture
  • To identify data sources and system consumers
  • To map / align sources and consumers and define transformations
  • To design consistent and scalable data services for validation, cleansing and consolidation, inquiry, subscription and replication
• Metadata Management
  • To map logical master data entities / attributes to physical instantiations
  • To identify and understand current differences in data between systems
  • To document business rules for consistent understanding and reference
  • To support definition of consistent data services specifically for MDM
  • To provide metrics for monitoring issue resolution and compliance
•   Data Quality Management
  • To define master data quality requirements and metrics
  • To define the current master data quality baseline
  • To monitor, report and audit master data quality improvement
  • To assess the impact of MDM on overall information quality
  • To assess the overall business value derived from MDM
• Managing the people/cultural/organizational issues
• Determining the essential implementation steps and the best order to take them

Guidelines for Success:

• Plan ahead:
  • Define the problem: the business impact and benefits
  • Define the strategy: the MDM program and components
  • Define the Architecture:  MDM and data integration tools
  • Define the Roadmap (incremental implementation plan)
  • Gain MDM program commitment
• Manage scope – prioritize!
  • Which Master Data?
  • Which Reference Data?
• An iterative and incremental delivery approach
• An integrated EIM approach that includes data governance, data architecture and enterprise data modeling, data integration architecture, data quality management, and metadata management.

SKML has worked with industry-leading organizations that recognize the critical business value of their master data.

SKML's MDM consulting services include:

• MDM Strategies and Implementation Roadmaps

• MDM Business Case Development and Prioritization

• MDM Education, Socialization, and Change Management

• MDM Architecture and Technology Selection

• MDM Project Management and Implementation

• MDM Program Management and Guidance, including Alignment with:

  • Data Governance & Stewardship

  • Data Architecture

  • Data Integration Architecture

  • Data Quality Management

  • Metadata Management