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The correct answer is Data models. DAMA-DMBOK2 explicitly explains that data models and other Data Architecture artifacts are commonly created or captured within development projects and are subsequently standardized and managed by Data Architects.

Development projects routinely produce conceptual, logical, and physical representations of the data required by a solution. If each project develops those structures independently without architectural review, inconsistent terminology, duplicated entities, conflicting definitions, and incompatible relationship structures can accumulate across the enterprise. Data Architects therefore review project models, reconcile them with enterprise standards, and identify structures suitable for reuse.

An Enterprise Data Model is itself an important architectural artifact, but the wording of the question refers specifically to artifacts typically generated in individual development projects and later standardized. The DMBOK2 passage uses data models in precisely that context.

This process has a direct Data Quality benefit. Standardized models improve consistency of definitions, domains, keys, relationships, and constraints. They also provide metadata from which integrity, uniqueness, validity, and completeness rules can be derived.

Reference Topics: DAMA-DMBOK2 Chapter 4 — Data Architecture; Architectural Artifacts; Development Projects; Chapter 5 — Data Modeling and Design; Chapter 13 — Consistency and Integrity.

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DAMA-DMBOK2 defines Data Stewardship in terms of accountability and responsibility for data and for the processes that ensure its effective control and use. The wording in option E closely matches the DMBOK2 definition: stewardship describes accountability and responsibility for data and processes that ensure the effective control and use of data assets.

Stewardship may be formalized through named positions or responsibilities embedded within existing business roles. Its practical scope commonly includes managing business terminology, defining valid values and business rules, establishing or approving Data Quality requirements, resolving data issues, applying standards, and supporting governance decisions.

Option C is too narrow and also confuses stewardship with the broader policy-setting responsibilities of Data Governance. Stewards participate in developing and implementing policies and standards, but stewardship is not merely a policy-creation role. Similarly, it is not a privacy technology function or a project-management construct.

Within Data Quality Management, Data Stewards are critical because quality must be defined relative to business requirements. They help determine what “fit for purpose” means, establish acceptable thresholds, prioritize defects according to business impact, and participate in root-cause remediation.

Metadata Management records stewardship decisions, while Master Data Management uses those decisions to govern shared enterprise entities.

Reference Topics: DAMA-DMBOK2 Chapter 3 — Data Stewardship; Steward Responsibilities; Business Glossary; Chapter 13 — Data Quality Governance and Issue Management.

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The ideal combination is a Data Architect and a Data Steward. DAMA-DMBOK2 states explicitly that, ideally, both roles should be assigned to each subject area and even to individual entities within a subject area. This arrangement integrates architectural control with business accountability.

The Data Architect contributes enterprise-wide structural knowledge: subject-area boundaries, entities, relationships, integration requirements, architectural standards, and alignment with the broader Enterprise Data Architecture. The Data Steward contributes authoritative business knowledge and accountability for terminology, business rules, valid values, quality expectations, and appropriate use of the data.

DMBOK2 also describes the enterprise data model as something that should be developed and maintained jointly by Data Architects and Data Stewards working together in subject-area teams.

This pairing is especially valuable for Data Quality because structural correctness alone does not guarantee fitness for purpose. A technically valid model may still contain ambiguous definitions or inappropriate business rules. Conversely, business requirements without architectural discipline can create duplicated or inconsistent structures.

Working together, the Architect and Steward ensure that definitions, models, metadata, lineage, quality rules, and governance decisions remain aligned. DBAs, analysts, modellers, and business analysts are important contributing roles, but they do not provide the same combined architectural and governance accountability.

Reference Topics: DAMA-DMBOK2 Chapter 4 — Data Architecture Governance; Subject Areas; Chapter 3 — Data Stewardship; Enterprise Data Models; Chapter 13 — Data Quality Accountability.

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The integration specification requires a documented transformation rule defining how pounds are converted to kilograms.

Source-to-target mappings should identify source attributes, target attributes, transformation logic, units, datatypes, validation expectations, and handling of exceptions. Without this metadata, different interfaces could apply different conversion factors or rounding rules and create inconsistent downstream values.

The rule should specify the approved conversion factor, precision, rounding method, treatment of nulls, and any acceptable source ranges. Testing should confirm that the transformed values remain within defined quality thresholds.

This illustrates the relationship between Data Integration, Metadata Management, and Data Quality that DAMA's current Chapter 13 revision makes more explicit.

The source value may be completely accurate in pounds while the target value becomes inaccurate through faulty transformation. Therefore, quality responsibility extends beyond original data capture.

Lineage should retain both the source and transformation information so downstream analysts can understand how the kilogram value was derived.

Reference Topics: DAMA-DMBOK2 — Data Integration and Interoperability; Source-to-Target Mapping; Transformation; Metadata Lineage; Accuracy.

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