Master Data Management: An Approach
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Master Data Management: An Approach

 
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Chief Architect - Data, BI & Analytics
Master data management is an integral & vital part of overall Enterprise Data Governance strategy. As the name indicates, master data govern directly or indirectly the entire data ecosystem of the organization. Without the master data, none of the transactional data has any meaning. It is the core process used to Manage, Centralize, Organize, Categorize, Localize, Synchronize and Enrich the master data according to the business rules. Example – Product, Customer, Vendor, Supplier, Location, Employees master and others.

Vivek Mohan

Characteristics of Master Data Management Apps

MDM applications must have the following characteristic to successfully manage the master data, support governance and augment SOA and BI Systems.

  • Must be available and scalable
  • It must have a flexible, extensible, and open data model to hold the master data and all required attributes
  • It must manage metadata for items such as business entity relationship and hierarchies
  • It must manage the source systems to fully cross reference business objects and to satisfy seemingly conflicting data ownership requirements
  • A data quality function that can find and eliminate duplicate data
  • Must have a data quality interface to help prevent the new errors from entering the system even when the data entry is outside the MDM application itself
  • Must have a data cleansing function to keep the data clean ad up to date
  • Must have internal triggering mechanism to create and deploy change information to all the downstream apps
  • Must have a comprehensive data security system to control and monitor the data access, update rights, and maintain change history
  • Must have a UI that supports casual users and data stewards
  • Should manage data migration to ensure consistency as data moves across real time enterprise
  • Should have business intelligence structure to support profiling, compliance, and business performance indicators
  • Should have a single platform to manage all master data objects
  • Must have an analytical foundation for directly analyzing master data

 

Benefits of MDM

  • Comprehensive knowledge of the business entity with single source of truth
  • Unified view of master data enables improvements in meeting customer expectation
  • Consistent reporting and analytics reducing inconsistency
  • Improved risk management, more reliable and consistent information
  • Reduced complexity of integrating new data and systems and thereby making it more flexible
  • Improved operation efficiency and reduced cost
  • Improved decision making
  • Better analysis and planning
  • Regulatory compliance
  • Increased information quality
  • Quicker results
  • Improved business productivity
  • Simplified application development

Factors to consider implementing MDM

  • Effective technical infrastructure for collaboration
  • Organizational preparedness
  • Round Trip enterprise acceptance and integration
  • Measurably high data quality
  • Governance

MDM Strategy should be built around these 6 Disciplines

  • Governance: Directives that manage the organizational bodies, policies, principles, and qualities to promote access to accurate and certified master data. Essentially, this is the process through which a cross-functional team defines the various aspects of the MDM program.
  • Measurement: How are you doing based on your stated goals? Measurement should look at data quality and continuous improvement.
  • Organization: Getting the right people in place throughout the MDM program, including master data owners, data stewards and those participating in governance.
  • Policy: The requirements, policies, and standards to which the MDM program should adhere.
  • Process: Defined processes across the data lifecycle used to manage master data.
  • Technology: The master data hub and any enabling technology.

Different Stages of MDM: Stage 0,1 and 2 needs involvement of the Business at a greater extent

  • Stage 0: Identify sources of master data
  • Stage 1: Identify the producers and consumers of the master data
  • Stage 2: Collect and analyze metadata for your master data
  • Stage 3: Appoint data stewards
  • Stage 4: Implement a data governance program and data governance council
  • Stage 5: Develop the master data model
  • Stage 6: Design the infrastructure
  • Stage 7: Generate and test the master data
  • Stage 8: Modify the producing and consuming systems
  • Stage 9: Implement maintenance processes

MDM Challenges

  • Establishing enterprise wide data governance which has emerged as a critical factor for MDM success
  • Isolated islands of information
  • Organizational preparedness
  • Data Governance
  • Metadata management
  • Technology Integration
  • Anticipating change
  • Create a partnership between Business and IT
  • Managing Change

MDM Application Areas

  • Data Quality
  • Compliance
  • Improve efficiency
  • Retain customers
  • Merger and Acquisition
  • Improve decision making
  • Cross Reference
  • Golden Records

Roles and Responsibilities

  • Program Manager: Owns the data management strategy and platform.
  • Master Data Management Expert: Anchor and Architect the program
  • Business SME: Provides deep knowledge of application functionality and requirements and participates in workshops, planning and execution of the review and testing activities.
  • Data Architect/Modeler: Provides leadership and guidance with enterprise data strategies, especially as they relate to MDM
  • Data Stewards: Individuals who interact with the master data and/or business processes. These are the business users of the MDM system and act as stewards/maintainers of the data
  • Governance Council: The Master Data Governance Council (MDGC) is the decision-making and policy-making authority for matters related to data. The MDGC oversees the implementation of data standards and quality assurance to ensure that the MDM team and Data Stewards are developing, maintaining, and providing acceptable system data for the use of others.
  • End Users/Systems

Few Use Cases

  • Product Classification/reclassification
  • Adding new attributes
  • Adding new entity
  • Making product active/inactive
  • Maintaining single version of truth from disparate multiple data sources by avoiding version conflict and duplication

Conclusions

There is an obvious need to deliver a single, well defined, accurate, relevant, complete, and consistent view of master data across channels, departments, and geographies. Implementation of effective MDM solution in a company can lead to:

  • Establishment of operation environment for storage, maintenance, and updates of critical master data
  • Single version of ‘truth’ to all customer channels, front and back office systems through multiple interfaces
  • Accelerated execution of business processes with significant reduction in product launch cycle times
  • Elimination of data duplication, integrity issues
  • Efficient master data management through suitable built-in workflows and processes that maintain clean, current, and consistent product/location data information
  • Reduced data entry points
  • Reduced data maintenance costs
  • Consistency in cross-system data across the extended value chain
  • Reduction in service assurance issues
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