DQ-1220 Exam Dumps : DAMA Data Quality Specialist

DAMA-DMBOK2 identifies four principal types of data that may be represented in data models: Category Information, Resource Information, Business Event Information, and Detail Transaction Information. Because options A, B, C and E are all legitimate categories, All of these is correct.

Category information classifies things—for example, classifying customers by market segment or products by size or colour. Resource information represents business resources required for operations, including entities such as Customer, Product, Supplier, Facility and Account. These resource entities frequently overlap strongly with Master and Reference Data Management.

Business event information records events created during operational processes, such as orders, invoices or withdrawals. Detail transaction information represents highly granular observations, including point-of-sale records, clickstream data, social-media interactions, sensor outputs and other high-volume events.

This classification matters to Data Quality because quality controls must reflect the semantics and lifecycle of the data being evaluated. Reference and master data require strong uniqueness, definition and consistency controls; event data require integrity and timeliness; high-volume detailed data often require automated profiling and statistical monitoring.

Data models also generate important metadata—definitions, relationships, domains and constraints—which becomes the basis for measurable Data Quality rules.

Reference Topics: DAMA-DMBOK2 Chapter 5 — Types of Data that are Modeled; Chapter 10 — Reference and Master Data; Chapter 13 — Data Quality Rules and Dimensions; Metadata Management.

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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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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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