DAMA DQ-1220 - DAMA Data Quality Specialist

Whether the rule is mandatory is a business governance decision and should be approved through the appropriate Data Owner, Data Steward, or governance authority responsible for the Supplier domain.

A developer can implement a database constraint, but technical implementation does not establish business policy. The organization must determine whether every active supplier genuinely requires a tax identifier, whether exceptions exist by jurisdiction or supplier type, and what consequences should follow when the rule is violated.

The approved rule should be documented as metadata and linked to the relevant business term, physical fields, authoritative sources, threshold, and responsible steward.

This illustrates the separation between governance and execution. Governance defines and authorizes the requirement; operational and technical teams implement the validation and monitoring.

DAMA's public framework places Governance across the Data Management knowledge areas and Metadata Management as the mechanism for maintaining definitions, lineage, and usage context.

Reference Topics: DAMA-DMBOK2 Chapter 3 — Governance and Stewardship; Chapter 13 — Data Quality Rules; Metadata Management; Supplier Master Data.

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The primary issue is Uniqueness because more than one record represents the same real-world entity. Uniqueness evaluates whether unintended duplicate instances exist within a dataset or across integrated datasets.

Duplicate customer records can arise because of spelling variation, abbreviations, different contact details, weak matching rules, or inconsistent identifier capture. Exact duplicate detection alone is therefore insufficient. Effective entity resolution frequently requires probabilistic or deterministic matching across names, addresses, identifiers, telephone numbers, and other attributes.

DAMA-aligned Data Quality frameworks define uniqueness in terms of avoiding duplicate representation of the same entity. They also recognize that duplicate records may differ in individual attribute values while still representing the same person or object.

The issue is closely related to Master Data Management. An MDM process may match the three source records, establish that they belong to one customer, and apply survivorship rules to construct an authoritative master representation.

The correct remediation should also address the source process that allowed duplicates to be created; otherwise cleansing will merely remove symptoms while new duplicates continue to appear.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Uniqueness; Matching; De-duplication; Chapter 10 — Master Data Management; Entity Resolution.

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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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The DAMA-DMBOK2 Environmental Factors Hexagon illustrates the relationship between People, Process, and Technology. It provides a visual key for interpreting the Knowledge Area Context Diagrams and emphasizes that effective Data Management depends on the coordinated interaction of these three dimensions.

DMBOK2 places Goals and Principles at the center of the hexagon because they guide how people perform activities and how technology is selected and used. People-related factors include roles, responsibilities, organization, and culture. Process-related factors include activities, techniques, and methods. Technology-related factors include tools and the mechanisms that support Data Management deliverables.

This model is especially relevant to Data Quality. A profiling or cleansing platform alone cannot establish sustainable quality. The organization also needs accountable Data Owners and Data Stewards, repeatable issue-management and improvement processes, business-defined rules, and governance mechanisms. Conversely, well-designed processes will fail if supporting technologies cannot monitor or enforce required controls.

Option A relates more closely to the structure of DMBOK Knowledge Area Context Diagrams. The DAMA Wheel represents the Knowledge Areas, while the Environmental Factors Hexagon specifically emphasizes the People–Process–Technology relationship.

Reference Topics: DAMA-DMBOK2 Chapter 1 — DAMA-DMBOK Framework; Environmental Factors Hexagon; People, Process and Technology; Chapter 13 — Data Quality Organization, Processes and Tools.

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A process can be statistically stable yet still perform at an unacceptable quality level. Statistical control means that variation is predictable within the established process; it does not mean that the average level of defects satisfies business expectations.

For example, a process may consistently produce a 3% defect rate with very little month-to-month variation. If the business requirement is below 0.5%, the process is stable but incapable of meeting the desired quality level without improvement.

The appropriate response is therefore deliberate process improvement aimed at changing the underlying process and reducing its baseline defect rate. This differs from reacting to random individual observations within normal control limits.

After improvement, new performance data should be collected and control parameters reevaluated once the revised process reaches a stable state.

DAMA's revised Chapter 13 explicitly maps Shewhart and Deming improvement-cycle stages to Data Quality processes and preserves Statistical Process Control as supporting material.

The central principle is that control and capability are different questions: control asks whether the process is stable; business quality requirements determine whether that stable performance is good enough.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Statistical Process Control; Process Stability; Continuous Improvement; Shewhart/Deming Cycle; Thresholds.

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GARP stands for Generally Accepted Recordkeeping Principles. ARMA International developed the framework to describe the characteristics of an effective records and information-management program. The principles address accountability, transparency, integrity, protection, compliance, availability, retention, and disposition.

These principles align closely with DAMA-DMBOK2's treatment of Document and Content Management. Information must be created, organized, protected, maintained, retrieved, retained, and ultimately disposed of according to business, legal, regulatory, and historical requirements. Records management is therefore not simply about long-term storage. It establishes controls throughout the information lifecycle.

For example, the Integrity principle addresses the authenticity and reliability of records. Availability requires information to be retrievable accurately and efficiently. Retention requires organizations to keep records for justified periods, while Disposition governs appropriate final handling after those obligations expire.

These controls interact directly with Data Quality. Records that cannot be found, trusted, interpreted, or demonstrated to be authentic are not fit for their intended purpose even if they physically exist.

Metadata Management supplies classification, retention, provenance, ownership, and lifecycle metadata necessary to implement these principles consistently.

Reference Topics: DAMA-DMBOK2 — Document and Content Management; Records Management; GARP; Retention; Disposition; Integrity; Metadata Management.

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Discovering and documenting metadata about physical data assets provides visibility into how data moves and is transformed between systems. This is a core function of technical metadata and data lineage.

DAMA-DMBOK2 states that technical metadata describes the technical characteristics of data, the systems that store it, and the processes that move it within and between systems. Examples include physical table and column names, ETL job information, source-to-target mappings, and lineage documentation with upstream and downstream impact information. The certification question therefore points directly to option D; the same interpretation is independently reflected in published CDMP-oriented material.

This capability is critical to Data Quality because a defect observed in a report or downstream application may not originate in the current system. It may have been introduced through extraction logic, transformation rules, reference-data conversion, aggregation, or loading.

Documented physical metadata allows analysts to trace the affected value backward, identify its source, inspect each transformation, and isolate the point at which the defect occurred. It also supports change-impact analysis: modifying a source field or mapping can reveal which downstream assets may be affected.

Metadata therefore provides the evidence required for systematic root-cause analysis rather than symptom-based correction.

Reference Topics: DAMA-DMBOK2 Metadata Management — Technical Metadata; Source-to-Target Mapping; Data Lineage; Data Integration and Interoperability; Chapter 13 — Root-Cause Analysis and Quality Monitoring.

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The immediate requirement is Business Glossary governance. The issue is semantic: two departments use the same term but assign different meanings to it.

A governed business glossary establishes agreed terminology, definitions, synonyms, related concepts, ownership, and usage context. Resolving the definition does not necessarily mean forcing every business process to use one operational rule; there may be legitimate variants. The critical requirement is that those variants be explicitly named and documented so consumers understand the distinction.

Metadata Management provides the structures needed to preserve and distribute these definitions. DAMA's public Metadata Management guidance identifies business glossaries and data dictionaries as core mechanisms for improving shared understanding.

This semantic clarity is fundamental to Data Quality. A completeness or accuracy score for "Active Customer" is meaningless if departments measure different populations under the same label.

After the definition is governed, technical metadata should link the glossary concept to physical fields, calculations, reports, and quality rules.

Reference Topics: DAMA-DMBOK2 — Business Glossary; Metadata Management; Data Stewardship; Semantic Consistency; Data Quality Rules.

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The primary dimension affected is Consistency. Consistency evaluates whether equivalent information agrees across datasets, systems, representations, or related attributes when business rules require agreement.

The same customer identifier is represented with conflicting lifecycle statuses at the same time. Each value might individually satisfy its local datatype and permitted-value rules, so both could be valid in isolation. However, unless a legitimate business rule explains the difference, the two representations are inconsistent.

Resolving the issue requires more than choosing one value arbitrarily. The organization must establish the authoritative source for customer status, understand whether the systems apply different definitions of "Active" and "Closed," examine synchronization timing, and trace transformation or integration logic. Metadata should document the business meaning and source authority, while lineage should reveal how status information is propagated.

Master Data Management may also be relevant where customer-state information is shared across multiple operational systems. Governance must determine ownership and conflict-resolution rules.

DAMA's revised Chapter 13 identifies Consistency among the standardized Data Quality dimensions and explicitly strengthens its relationship with Metadata, Reference/Master Data, and Data Integration.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Consistency; Authoritative Sources; Metadata Management; Master Data Management; Data Integration.

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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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