DAMA CDMP DQ-1220 New Questions

The correct answer is storage is cheap and easily expanded. DAMA-DMBOK2 discusses retention and disposal as important lifecycle-management responsibilities. Although information that no longer provides business, legal, regulatory, historical, or evidentiary value should normally be disposed of according to approved retention policies, organizations often postpone disposal because modern storage appears inexpensive and technically easy to expand.

This reasoning is deceptive. The acquisition cost of storage is only one component of total information-management cost. Retaining unnecessary information also increases backup requirements, recovery time, discovery obligations, privacy exposure, security risk, metadata-management effort, migration complexity, and the volume of information that must be governed. DMBOK2 therefore stresses that non-value-adding information should not be retained merely because storage capacity is readily available.

From a Data Quality perspective, excessive retention also increases the population of obsolete, redundant, and potentially inconsistent information. This makes profiling, lineage analysis, master-data reconciliation, and authoritative-source identification more difficult.

A sound governance program consequently combines retention schedules, legal requirements, metadata classification, defensible disposal procedures, and clear accountability so that data is retained for legitimate reasons rather than technological convenience.

Reference Topics: DAMA-DMBOK2 — Document and Content Management; Retention and Disposal; Information Lifecycle; Data Governance; Chapter 13 — Data Quality and Obsolete Data.

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The need to manage data movement efficiently is a primary business driver for Data Integration and Interoperability (DII). DAMA-DMBOK2 states this directly. Modern organizations operate hundreds or thousands of databases, applications, files, services, data stores, external interfaces, and analytical platforms. Data must continually move among these environments, often across organizational boundaries.

Without disciplined integration management, data movement becomes fragmented, expensive, difficult to monitor, and highly dependent on duplicated point-to-point interfaces. DII addresses this problem by establishing controlled mechanisms for extraction, transformation, messaging, replication, orchestration, APIs, data virtualization, and other forms of information exchange.

From a Data Quality perspective, every movement of data presents an opportunity either to preserve quality or to damage it. Source-to-target mappings must maintain semantic meaning, transformations must be controlled, reference values must remain consistent, and lineage must show how values were altered.

Metadata Management therefore records mappings, interface definitions, transformation rules, and lineage. Master Data Management uses integration mechanisms to distribute governed master and reference data consistently between systems.

Data Warehousing is a major consumer of integration capabilities, but the broader discipline whose explicit driver is efficient movement across systems is Data Integration and Interoperability.

Reference Topics: DAMA-DMBOK2 Chapter 8 — Business Drivers; Data Integration and Interoperability; Data Movement; Chapter 13 — Consistency, Integrity and Transformation Quality.

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In DAMA terminology, stewardship is based on the established concept of managing an asset on behalf of another party. DAMA-DMBOK2 defines the underlying term directly: “A steward is a person whose job it is to manage the property of another person.” It then applies that concept specifically to data: Data Stewards manage data assets on behalf of others and in the best interests of the organization.

A Data Steward is therefore much more than a stakeholder or sponsor. Stewardship carries explicit operational accountability. Typical responsibilities include defining and clarifying business data, participating in issue resolution, supporting standards and policies, establishing quality requirements, approving metadata definitions, and ensuring governance decisions are applied within the steward's data domain.

The relationship to Data Quality is particularly important. DMBOK2 identifies Data Stewards as participants in identifying and resolving data-related issues and in ensuring governance policies are followed. They frequently establish or approve Critical Data Elements, Data Quality rules, acceptable thresholds, business definitions, and remediation priorities. Metadata Management records those definitions and rules, while Master Data Management applies stewardship decisions to shared entities such as Customer, Product, Supplier, or Location.

A stakeholder may have an interest in the data, and a sponsor may provide authority or funding, but neither term inherently includes stewardship accountability.

Reference Topics: DAMA-DMBOK2 Chapter 3 — Types of Data Stewards; Stewardship; Data Quality Issue Management; Chapter 13 — Data Quality Governance and Responsibilities.

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A strong Release Management process is critical when a Data Warehouse evolves incrementally. DAMA-DMBOK2 states directly that Release Management supports incremental development by coordinating new capabilities, enhancements, production deployment, and recurring maintenance of deployed warehouse assets.

A Data Warehouse is rarely completed in one implementation. Business requirements evolve, additional subject areas are onboarded, models are extended, transformation rules change, new reports are introduced, and defects are corrected. Release Management provides the controlled mechanism for packaging these changes into predictable production increments.

This requires prioritization of the backlog, coordination between business and technical teams, regression testing, deployment control, documentation, reconciliation, and validation of new or changed data structures. Without disciplined release management, incremental development can produce incompatible transformations, unstable reports, inconsistent historical treatment, and uncontrolled changes to definitions.

Data Quality should therefore form part of each release gate. New mappings and transformations should be profiled and reconciled; quality thresholds should be retested; metadata and lineage must be updated; and known exceptions should be documented.

Agile development may be used as a delivery method, but DAMA specifically identifies Release Management as the process critical to sustaining incremental warehouse evolution.

Reference Topics: DAMA-DMBOK2 Data Warehousing and Business Intelligence — Maintain Data Products; Release Management; Incremental Development; Chapter 13 — Quality Monitoring and Change Control.

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