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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Fundamentals | 20% | - Definition of Data Quality - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Data Quality in the Data Lifecycle |
| Topic 2: Data Quality Improvement | 20% | - Validation Rules - Data Cleansing Techniques - Data Enrichment - Standardization |
| Topic 3: Data Quality Assessment and Measurement | 25% | - Benchmarking - Metrics and KPIs - Profiling Techniques - Root Cause Analysis |
| Topic 4: Data Quality Governance | 15% | - Data Stewardship - Data Quality Roles and Responsibilities - Policies and Procedures - Continuous Monitoring |
| Topic 5: Data Quality Frameworks and Standards | 20% | - ISO 8000 (Data Quality) - Other Industry Standards - DAMA DMBOK Framework |
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NEW QUESTION # 74
The goal of data governance is to enable an organisation to manage data as an asset. To achieve this, the DG programs must be:
Answer: D
Explanation:
DAMA-DMBOK2 explicitly states that a Data Governance program must be sustainable. Governance is not a temporary implementation project that ends after policies, committees, or stewardship roles are established. It is an ongoing organizational capability requiring continuing leadership, sponsorship, ownership, decision rights, and operational integration.
The DMBOK2 governance guidance describes sustainable governance as "sticky": it must survive beyond its initial implementation and become embedded in normal business and Data Management practices.
Sustainable governance depends specifically on business leadership, sponsorship, and ownership.
This distinction matters for Data Quality because quality improvement is likewise continuous. New applications, data sources, business processes, regulatory requirements, and transformations continually create new risks. Governance must therefore continue assigning accountability, approving definitions and quality rules, resolving cross-domain disputes, and overseeing remediation.
Options focused on financial registration or assigning a dollar value confuse data-as-an-asset thinking with formal accounting treatment. Data valuation can support investment decisions, but it is not a prerequisite for Data Governance. Likewise, treating governance as a fixed-duration initiative contradicts the operating model described by DAMA.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Governance Goals and Principles; Sustainable Governance; Leadership; Sponsorship; Ownership; Chapter 13 - Data Quality Governance.
NEW QUESTION # 75
Adoption of a Data Governance program is most likely to succeed:
Answer: B
Explanation:
DAMA-DMBOK2 recommends an incremental implementation strategy for Data Governance because governance introduces changes in accountability, decision rights, standards, processes, and organizational behaviour. Attempting an enterprise-wide implementation immediately creates unnecessary cultural and operational risk.
The implementation guidance is explicit that most governance rollout strategies are incremental, commonly beginning with a significant initiative such as Master Data Management or with a particular region, business unit, or domain. Enterprise-wide deployment as the initial step is described as uncommon.
Incremental implementation provides several control advantages. The organization can validate roles and decision mechanisms, establish stewardship practices, prove business value, refine policies, develop metrics, and correct weaknesses before expanding the governance framework. This aligns closely with Data Quality management, where organizations normally prioritize Critical Data Elements and high-value domains rather than attempting to measure and remediate every data element simultaneously.
Executive sponsorship is important, but a mandate alone does not produce sustainable adoption. Likewise, individual leadership charisma cannot substitute for operating processes, accountability, metrics, and change management. A short consultant-driven exercise also conflicts with DAMA's treatment of governance as an ongoing organizational capability.
Incremental rollout therefore offers the strongest mechanism for embedding governance permanently while demonstrating measurable value.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Implementation Guidelines; Organization and Culture; Adjustment and Communication; Chapter 13 - Data Quality Improvement and Governance.
NEW QUESTION # 76
What is a steward?
Answer: B
Explanation:
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.
NEW QUESTION # 77
What is the final step in the development of a business-data-driven roadmap?
Answer: A
Explanation:
A business-data-driven roadmap translates the desired enterprise data architecture into an executable sequence of initiatives. DAMA-DMBOK2 explains that business capabilities consume and produce data, which creates dependencies between capabilities. The roadmap begins with relatively independent capabilities and progresses toward capabilities whose execution depends on data generated elsewhere. The architecture therefore establishes a coherent dependency chain before implementation work is organized.
The decisive final planning action is to turn that dependency sequence into implementable projects.
Consequently, establishing the sequence of projects that will establish the data value chain is the best answer.
Describing the data value chain, analysing data flows, identifying dependency challenges, and resequencing capabilities are analytical steps that precede portfolio sequencing.
DMBOK2 states that the roadmap should move from capabilities with the least dependency toward those with progressively greater dependencies, thereby providing the implementation order. It also emphasizes integrating the Enterprise Data Architecture roadmap with enterprise milestones, resources, and business- capability workstreams.
From a Data Quality perspective, sequencing matters because upstream master and reference data must reach an acceptable quality state before downstream transactional processes can depend on it.
Reference Topics: DAMA-DMBOK2 Chapter 4 - Data Architecture; Develop a Roadmap; Data Dependencies; Data Value Chain; Chapter 13 - Data Quality and other Knowledge Areas.
NEW QUESTION # 78
Examples of transformation include:
Answer: A
Explanation:
Data transformation includes operations such as format changes, structural changes, semantic conversion, de- duplication, and re-ordering. These activities modify source information so that it conforms to the syntactic, structural, or semantic requirements of a target environment.
A format transformation may convert dates from DD/MM/YYYY to an ISO representation. Structural transformation may split, combine, flatten, or restructure attributes. Semantic conversion changes representation while preserving intended meaning-for example, translating a source status code into the standardized enterprise code. De-duplication identifies multiple records representing the same real-world entity, while re-ordering changes the sequence or organization of records or attributes. The listed combination aligns directly with DAMA-oriented transformation guidance.
The distinction from the distractors is important. Organizational change, infrastructure replacement, or application modernization may trigger data transformation, but they are not themselves data-transformation techniques. "Re-duplication" is also inconsistent with the objective of improving integrated datasets.
Transformation is strongly connected to Data Quality. Poorly specified conversion rules can create invalid values, truncate data, introduce semantic inconsistencies, or produce duplicate entities. Consequently, transformations should be documented through mappings and metadata, tested against quality rules, reconciled with source totals, and monitored for exceptions.
Reference Topics: DAMA-DMBOK2 Chapter 8 - Transformation and Mapping; ETL/ELT; Chapter 13 - Standardization, Cleansing, De-duplication and Validation.
NEW QUESTION # 79
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