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Data Quality Management (DQM)

Definition - What does Data Quality Management (DQM) mean?

Data quality management is an administration type that incorporates the role establishment, role deployment, policies, responsibilities and processes with regard to the acquisition, maintenance, disposition and distribution of data. In order for a data quality management initiative to succeed, a strong partnership between technology groups and the business is required.

Information technology groups are in charge of building and controlling the entire environment, that is, architecture, systems, technical establishments and databases. This overall environment acquires, maintains, disseminates and disposes of an organization's electronic data assets.

Techopedia explains Data Quality Management (DQM)

When considering a business intelligence platform, there are various roles associated with data quality management:
  • Project leader and program manager: In charge of supervising individual projects or the business intelligence program. They also manage day-to-day functions depending on the budget, scope and schedule limitations.
  • Organization change agent: Assists the organization in recognizing the impact and value of the business intelligence environment, and helps the organization to handle any challenges that arise.
  • Data analyst and business analyst: Communicate business needs, which consist of in-depth data quality needs. The data analyst demonstrates these needs in the data model as well as in the prerequisites for the data acquisition and delivery procedures. Collectively, these analysts guarantee that the quality needs are identified and demonstrated in the design, and that these needs are carried to the team of developers.
  • Data steward: Handles data as a corporate asset.
An effective data quality management approach has both reactive and proactive elements. The proactive elements include:
  • Establishment of the entire governance
  • Identification of the roles and responsibilities
  • Creation of the quality expectations as well as the supporting business strategies
  • Implementation of a technical platform that facilitates these business practices
The reactive elements include the management of issues in the data located in existing databases.
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