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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Quality Assessment and Measurement | 25% | - Root Cause Analysis - Benchmarking - Metrics and KPIs - Profiling Techniques |
| Topic 2: Data Quality Frameworks and Standards | 20% | - ISO 8000 (Data Quality) - DAMA DMBOK Framework - Other Industry Standards |
| Topic 3: Data Quality Improvement | 20% | - Standardization - Validation Rules - Data Enrichment - Data Cleansing Techniques |
| Topic 4: Data Quality Governance | 15% | - Data Quality Roles and Responsibilities - Data Stewardship - Policies and Procedures - Continuous Monitoring |
| Topic 5: Data Quality Fundamentals | 20% | - Dimensions of Data Quality (completeness, accuracy, consistency, timeliness, validity) - Data Quality in the Data Lifecycle - Definition of Data Quality |
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93. Frage
Which of the following is the best example of a 'documented data quality rule'?
Antwort: E
Begründung:
Option D is the strongest example because it expresses a Data Quality requirement that is specific, measurable, associated with identified data, and explicitly connected to business need. It identifies the relevant data-transaction data from satellite systems-sets a measurable timeliness threshold of 12:05 a.m., and explains the business reason: ensuring that branches have access to actual customer balances for the required operating window. Published versions of this DAMA question identify the same choice.
This is fundamentally a Timeliness rule. DMBOK2 emphasizes that Data Quality requirements should be defined according to business expectations rather than vague aspirations. A useful rule needs sufficient precision to support objective measurement, exception reporting, and remediation.
Option E captures an important quality expectation-actual customer balances-but it lacks the same precise operational threshold. Option A is a security/confidentiality requirement rather than a Data Quality rule.
Options B and C primarily describe processing procedures and do not express the business-quality requirement as cleanly.
Once documented, the rule should be managed as metadata, assigned to an accountable steward, linked to its Critical Data Elements, monitored through metrics, and escalated when the specified threshold is breached.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Quality Requirements; Data Quality Rules; Timeliness; Measurement; Business Impact; Metadata Management.
94. Frage
A company has five systems that can update a customer's legal name, and no system is designated as authoritative. What is the greatest Data Quality risk?
Antwort: B
Begründung:
The principal risk is inconsistent customer-name values across systems. When multiple applications can independently update the same business fact without an authoritative source or coordinated Master Data process, conflicting representations are likely to emerge.
The problem is architectural and governance-related as much as it is a Data Quality issue. The organization must determine where the authoritative value is created, who is responsible for approving changes, how updates propagate, and how conflicts are resolved.
DAMA's public framework emphasizes a single trusted or consistent approach to shared core entities through Reference and Master Data Management.
The relevant Data Quality dimensions include Consistency, Accuracy, Currency, and potentially Uniqueness.
An application may hold a technically valid legal name but still contain an outdated or conflicting value.
Metadata and lineage should document which systems create, master, distribute, or merely consume the attribute.
Reference Topics: DAMA-DMBOK2 - Authoritative Sources; Master Data Management; Data Architecture; Consistency; Lineage; Stewardship.
95. Frage
A business case for adding a new master data management solution is dependent on achieving greater value from:
Antwort: B
Begründung:
An MDM investment is justified when the business value obtained from centralized coordination of shared data exceeds the additional cost of managing and integrating another controlled representation of that data.
This is the economic trade-off expressed by option C and is also the intended answer in published versions of this DAMA question.
DAMA-DMBOK2 positions Master Data Management as a mechanism for improving consistency and control over enterprise entities such as customers, products, suppliers, employees, and locations. Uncoordinated local copies create redundant maintenance, inconsistent definitions, duplicate records, reconciliation costs, and integration complexity. DAMA sources emphasize that controlling shared master and reference data reduces both cost and risk generated by inconsistencies between systems.
However, introducing an MDM repository also incurs costs: integration, stewardship, matching, survivorship logic, synchronization, governance, maintenance, and potentially another physical copy of shared information.
The business case must therefore demonstrate that improved coordination, quality, reuse, reporting, and operational consistency outweigh those costs.
MDM does not require all contributing systems to be decommissioned, nor does it eliminate Reference Data Management. Similarly, "mining golden records" is not the fundamental economic basis for MDM.
Reference Topics: DAMA-DMBOK2 Chapter 10 - Master Data Management; Business Drivers; Shared Data; Central Coordination; Data Integration; Data Quality and Stewardship.
96. Frage
A Data Quality incident affects several downstream systems. What should be determined before individual teams independently correct their copies?
Antwort: A
Begründung:
The organization should determine the root cause and authoritative remediation point before multiple teams independently modify downstream copies.
If a defective value originates in a source system and is distributed to five downstream applications, correcting those five copies independently may provide temporary relief but leaves the upstream defect intact.
The incorrect value may simply be redistributed again.
Lineage should be used to trace the defect through source systems, transformations, integration layers, Master Data hubs, warehouses, and reports. Once the origin is understood, governance can determine which system or process is authoritative and where correction should occur.
This approach minimizes inconsistent local fixes and reduces the risk that different teams apply incompatible interpretations of the same issue.
DAMA's Chapter 13 revision explicitly strengthens the relationship between Data Quality and Metadata Management, Reference/Master Data Management, Modeling, and Data Integration. Those connections are precisely what enable cross-system issue resolution.
Downstream correction may still be required after the authoritative source is fixed, but it should be coordinated.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Issue Management; Metadata Lineage; Authoritative Sources; Data Integration.
97. Frage
Profiling shows that whenever Account_Status is "Closed", Closure_Date is normally populated. However,
4% of closed accounts have a null Closure_Date. What type of profiling has revealed this issue?
Antwort: C
Begründung:
This is cross-column dependency profiling. The issue cannot be identified merely by counting null values in Closure_Date because some nulls may be legitimate for active accounts. The defect becomes meaningful only when the relationship between two attributes is examined.
The implied business rule is: if Account_Status = Closed, then Closure_Date must be populated. Dependency profiling examines relationships among values and can reveal conditional completeness, inconsistent combinations, functional dependencies, and other multi-attribute patterns.
The next step is to confirm the inferred pattern with business stakeholders rather than automatically converting it into an enforced rule. There may be exceptional account categories where a closed status legitimately lacks a closure date.
Once approved, the rule should be documented as metadata, assigned to a Data Steward, measured against an agreed threshold, and monitored. Root-cause investigation can then determine whether missing dates arise from source-screen design, process bypass, conversion defects, or integration failures.
DAMA's revised Chapter 13 places Data Quality techniques in a more practical sequence and strengthens their linkage with Data Modeling and Metadata Management.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Data Profiling; Dependency Analysis; Completeness; Business Rules; Metadata Management.
98. Frage
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