Strategy for Corporate Data Quality: As CDQM is affected by various business drivers and requires involvement of multiple divisions in an organization; it must be considered a company-wide endeavor.
Corporate Data Quality Controlling: Effective CDQM requires compliance with standards, policies, and procedures. Compliance is monitored according to previously defined metrics and performance indicators and reported to stakeholders.
Corporate Data Quality Organization: CDQM requires clear roles and responsibilities for the use of corporate data.
The CDQM organization defines tasks and privileges for decision making for CDQM.
Corporate Data Quality Processes and Methods: In order to handle corporate data properly and in a standardized way across the entire organization and to ensure corporate data quality, standard procedures and guidelines must be embedded in company’s daily processes.
Data Architecture for Corporate Data Quality: The data architecture consists of the data object model - which comprises the unambiguous definition and the conceptual model of corporate data - and the data storage and distribution architecture.
Applications for Corporate Data Quality: Software applications support the activities of Corporate Data Quality Management. Their use must be planned, monitored, managed and continuously improved.
The definition provided by DAMA International, the professional organization for the data management profession, is: "Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise." This broad definition encompasses professions which may not have direct technical contact with lower-level aspects of data management, such as relational database management
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