Master Data Management and Data Governance have common goals:

  • Enable IT to be more responsive to business change,
  • Reduce cost while increasing data value and confidence in data (very hot in today’s economy!),
  • Mitigate risk of human capital dependencies,
  • Speed up systems development and implementation,
  • Improve data quality and integrity while resolving data integration issues and
  • Provide Business and IT accountabilities that support data as a critical asset.

MDM has been a hot buzz word lately and has added a couple of terms into our vocabulary. One is Customer Data Integration or CDI.  Presently, many MDM applications concentrate on customer data because this aides the sales and marketing process, and can help improve revenue and the bottom line.  Both have the objective of providing and maintaining a consistent view of an organization’s core business entities and both tend to involve data that is scattered across a range of application systems.
MDM and CDI are often presented as technologies, but in reality they are business applications. The objective of both MDM and CDI is to provide a consistent view of dispersed reference data. MDM provides for the ‘single version of the truth’ for data objects critical to the business (customer, product, employee, supplier, etc.) by:

  • Merging all records and transactions critical to the business into an authenticated master list or file,
  • Allowing applications and users to access data in a consolidated view,
  • Provides a cross reference for keys and different values of data and
  • Enhances security (encryption, data splitting and scrambling) for critical data.

But let’s not forget the focus of MDM – establishing a "system of record repository" for corporate ‘master’ data and this is dependent on the business requirements.
Techniques involved in MDM data integration include:

  • Data consolidation captures data from multiple source systems and integrates into a single persistent store or view,
  • Data federation provides a single virtual view of one or more source data files and
  • Data propagation – copying of data from one location to another.

These techniques may be implemented using ETL, EII or EAI, or other technologies like enterprise data replication (EDR), enterprise content management (ECM), and so forth.  It is quite common for a MDM or CDI application to use a hybrid approach that involves several data integration techniques and technologies. 
Some of the challenges include difficult decisions on what to do with the master data. An example here would be whether the integrated view of the reference data must be kept current with source systems, or whether some level of data latency is acceptable. Another example is whether the MDM and CDI data can be modified or is read-only
The typical results of a well-implemented MDM solution include:

  • A certified, persistent repository of all master data,
  • An audit trail of each master data element,
  • A complete history of changes to master data elements over time,
  • Consistently applied security policies/procedures,
  • Simplified application design and
  • Hopefully - An SOA-driven architecture that separates application from data.

These typical MDM results yield the following benefits:

  • A single version of the truth that breaks down operational silos,
  • Consistency, simplification and uniformity of business processes,
  • Reduced data redundancy and, therefore, cost,
  • Compliance with organizational, corporate and regulatory policies and
  • Easier integration during mergers and acquisitions.

What are the typical challenges to MDM one may ask?  They include, but are not limited to:

  • May have extra ‘common’ data separate from all applications that store it,
  • Matching and determining correct values may require complex algorithms,
  • Creates another data store that could quickly get out of sync, if multiple versions are kept and
  • Success depends on data quality, governance and stewardship.

Master Data and Data Quality are intricately connected; poor quality of master data leads to reduced confidence in transactional data, which results in suboptimal business decision-making.

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