DQ-1220 Exam Dumps - DAMA Data Quality Specialist

The required capability is Data Lineage. Lineage documents how data originates, moves, transforms, and ultimately appears in downstream reports, applications, and analytical products.

When a report value is wrong, lineage allows investigators to trace the value backwards through calculations, mappings, staging structures, integration processes, source tables, and potentially external providers. This prevents teams from guessing where the defect originated.

Metadata Management is the principal discipline responsible for collecting and maintaining this contextual information. DAMA's public framework identifies lineage and definitions as central Metadata Management capabilities.

Lineage is also essential for change-impact analysis. If a source element changes, teams can determine which reports, models, or applications depend upon it.

For Data Quality, lineage supports root-cause analysis and coordinated remediation. A downstream value may be accurate relative to the transformation applied but still wrong because the source or transformation logic was incorrect.

Reference Topics: DAMA-DMBOK2 — Metadata Management; Data Lineage; Source-to-Target Mapping; Root-Cause Analysis; Data Integration.

===============

The process is Survivorship. In Master Data Management, survivorship determines which value should become the preferred or authoritative representation when multiple source records contain conflicting values for the same attribute.

Rules may prioritize particular systems, use recency, trust scores, verification status, completeness, or combinations of factors. For example, a verified customer-service update might outrank an older marketing-system telephone number.

Survivorship occurs after or in conjunction with matching and entity resolution. The organization first determines that records from different sources represent the same real-world customer and then determines which attribute values should populate the mastered representation.

The process requires governance because the "best" value is a business decision, not merely a technical one. Data Stewards should approve the rules, and metadata should record source priority, rule logic, and lineage.

DAMA's framework positions Reference and Master Data Management as the discipline responsible for ensuring consistent core entities across the organization.

Reference Topics: DAMA-DMBOK2 Chapter 10 — Master Data Management; Matching; Survivorship; Authoritative Values; Chapter 13 — Consistency and Accuracy.

===============

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.

===============

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.

===============

A high percentage of returned mail is primarily a Data Quality problem. Returned correspondence frequently results from inaccurate, incomplete, obsolete, incorrectly formatted, or otherwise unusable address data. The operational symptom therefore indicates that address information is failing to meet its intended business purpose.

Published versions of this DAMA question identify Data Quality as the intended knowledge area. The underlying principle aligns directly with DMBOK2 Chapter 13: quality is assessed relative to the requirements of the business process consuming the data.

Several quality dimensions may contribute. Accuracy concerns whether the address correctly represents the customer's actual location. Completeness concerns missing apartment numbers, postal codes, or address components. Validity evaluates whether values conform to accepted formats and domains. Currency addresses whether a previously correct address has become obsolete. Consistency can also be involved where address information differs between systems.

Reference and Master Data Management may provide customer-master consolidation and standardized geographic reference values, but the direct defect represented by returned mail is poor-quality address data.

A mature response should involve profiling returned-mail records, determining defect patterns and root causes, establishing address-quality rules, correcting affected records, and introducing preventive validation at capture.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Fitness for Purpose; Accuracy; Completeness; Validity; Currency; Profiling; Root-Cause Analysis.

===============

A point materially outside established control limits is a classic signal of special-cause variation. Statistical Process Control distinguishes routine variation inherent in a stable process from unusual variation introduced by an identifiable event or change.

When a Data Quality process normally produces a stable percentage of invalid records and a sudden observation lies beyond the expected statistical range, investigation should focus on what changed. Possible causes include a new source system, software deployment, changed mapping, revised business rule, unusual batch, or manual processing error.

The current DMBOK2 Chapter 13 revision retains Statistical Process Control concepts while reorganizing them into supporting material and explicitly maps Shewhart/Deming improvement stages to Data Quality processes.

The signal does not prove what caused the defect, nor does it establish that performance has improved or deteriorated permanently. It indicates that the observed variation is sufficiently unusual to justify analysis.

Quality teams should preserve the evidence, correlate the timing with system and process changes, use lineage to identify affected sources, and investigate before adjusting the process unnecessarily.

Reference Topics: DAMA-DMBOK2 Chapter 13 — Statistical Process Control; Control Charts; Common Cause; Special Cause; Continuous Improvement.

===============

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.

===============

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.

===============