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April 13, 2009

Data Quality Steps For Successful MDM Program

Filed under: Data Cleansing, Data Quality — Tags: — Olga Belokurskaya @ 4:57 am

It’s surely no secret that data quality management and MDM are two key factors of enterprise information management. They are interrelated for without DQM, MDM is simply a pile of the data storage as well as DQM cannot bring ROI to the organization without MDM. Actually data quality management plays a role of a building block of an MDM hub as quality and accurate data is a key to the success of an MDM program.

In-depth analysis of the quality and health of data is a prerequisite of the MDM program. Here are data quality management steps suggested at, which are needed to support an agile MDM program:

  1. Identify and qualify the master data and its sources. The definition of master data may be different for different business units. The first step involves identifying and qualifying master data for each business unit in the organization
  2. Identify and define the global and local data elements. More than one system may store/generate the same master information. Additionally, there could also be a global version as well as local versions of the master data. Perform detailed analysis to understand the commonalities and differences between local, global and global-local attributes of data elements.
  3. Identify the data elements that require data cleansing and correction. At this stage, the data elements supporting the MDM hub that require data cleansing and correction have to be identified. Communication with the stakeholders is necessary so that as part of the MDM initiative, data quality will be injected into these selected data elements on an organization-wide basis.
  4. Perform data discovery and analysis. Data collected from source applications needs to be analyzed to understand the sufficiency, accuracy, consistency and redundancy issues associated with data sets. Analyze source data from both business and technical perspectives.
  5. Define the strategy for initial and incremental data quality management. A well-defined strategy should be in place to support initial and incremental data cleansing for the MDM hub. Asynchronous data cleansing using the batch processes can be adopted for initial data cleansing. Industry-standard ETL and DQM commercial off-the-shelf tools should be used for initial data cleansing. The incremental data cleansing will be supported using synchronous/real-time data cleansing.
  6. Monitor and manage the data quality of the MDM hub. Continuous data vigilance is required to maintain up-to-date and quality data in an MDM hub. Data quality needs to be analyzed on a periodic basis to identify the trends associated with the data and its impact over the organization MDM program.

In fact, data quality management is the foundation for an effective and successful MDM implementation. A well defined strategy improves the success probability of an MDM program. Organization should embark a data discovery and analysis phase to understand the health, quality and origin of the master data.

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