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| Section | Objectives |
|---|---|
| Data Quality Improvement | - Data Cleansing Methods - Root Cause Analysis |
| Data Quality Assessment | - Data Profiling Techniques - Data Quality Metrics and KPIs |
| Data Quality Fundamentals | - Definition of Data Quality - Data Quality Dimensions (Accuracy, Completeness, Consistency, Timeliness, Validity, Uniqueness) |
| Data Quality Management | - Data Quality Governance - Roles and Responsibilities (Data Stewardship) |
| Data Quality Operations | - Monitoring and Reporting - Data Quality Tools and Technologies |
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NEW QUESTION # 87
A team uses a fishbone diagram during investigation of recurring missing customer email addresses. What is the primary purpose of the diagram?
Answer: C
Explanation:
A fishbone diagram, also known as an Ishikawa or cause-and-effect diagram, is used to organize and explore potential root causes of a problem.
For recurring missing email addresses, categories might include People, Process, Technology, Data, Policy, Training, and External Sources. Potential causes could include optional form design, unclear business requirements, API mapping defects, missing supplier data, or inconsistent onboarding procedures.
The technique helps prevent teams from prematurely assuming that the most visible symptom is the actual cause. It encourages structured investigation across multiple dimensions of the process.
Once hypotheses are identified, evidence should be collected to confirm or reject them. The diagram itself does not prove causation.
In Data Quality programs, root-cause analysis is essential because repeated cleansing without process correction allows defects to recur. DAMA's Chapter 13 revision explicitly retains and strengthens the treatment of common causes and the improvement lifecycle.
Reference Topics: DAMA-DMBOK2 Chapter 13 - Root-Cause Analysis; Cause-and-Effect Diagram; Issue Management; Continuous Improvement.
NEW QUESTION # 88
Project costs will increase faster by adding to the data model than adding to the process model because:
Answer: A
Explanation:
Each additional data item creates obligations across multiple lifecycle processes, which is why option D is the most complete answer. DAMA-DMBOK2 treats data as an asset that must be managed throughout an interconnected lifecycle rather than merely stored in a database. Activities include creation or acquisition, validation, integration, transformation, storage, maintenance, access, use, protection, retention, archiving, and disposal.
Consequently, introducing another meaningful data element may require changes to data models, metadata repositories, interfaces, mappings, business rules, validation controls, security classifications, quality measurements, lineage documentation, reporting logic, retention rules, and stewardship responsibilities. The cost is therefore cumulative across several management processes.
Option A identifies only migration. Option B reduces the impact to storage, which is usually a minor component. Option C applies only where a warehouse is involved. Option E identifies one legitimate consideration-security-but security is only one part of the complete management obligation.
The Data Quality implication is substantial. Every additional Critical Data Element may require explicit definitions of validity, completeness, accuracy, timeliness, and consistency, together with monitoring and issue-management procedures. This demonstrates why organizations should avoid collecting data indiscriminately and should manage data according to genuine business value and requirements.
Reference Topics: DAMA-DMBOK2 Chapter 1 - Data Lifecycle; Data as an Asset; Chapter 5 - Data Modeling; Chapter 13 - Data Quality Requirements and Monitoring.
NEW QUESTION # 89
How is Data Governance defined?
Answer: A
Explanation:
DAMA-DMBOK2 defines Data Governance as the exercise of authority and control over the management of data assets. This definition distinguishes governance from the operational execution of Data Management.
Independent literature describing DAMA's framework uses the same core formulation, and the DAMA Wheel places Data Governance across the other knowledge areas through this authority-and-control function.
Governance establishes decision rights, accountability, policies, standards, stewardship, issue escalation, compliance expectations, and mechanisms for monitoring whether data is being managed appropriately. It determines who is authorized to make decisions and what rules must be followed; individual Data Management knowledge areas then execute the corresponding operational activities.
Option E is closer to the broad definition of Data Management, because it refers to planning, implementation, and control across the information lifecycle. Option A describes an assessment or maturity activity. Option B describes selected governance benefits rather than its definition, while option C characterizes a collection of functions.
For Data Quality, governance provides the authority needed to define critical data, approve quality requirements and thresholds, assign accountability, prioritize remediation, and enforce standards. Without this authority structure, profiling may identify defects but the organization may lack the decision mechanisms required to correct their underlying causes.
Reference Topics: DAMA-DMBOK2 Chapter 3 - Data Governance Definition; Authority and Control; Policies and Standards; Chapter 13 - Data Quality Governance.
NEW QUESTION # 90
In 2009, ARMA International published GARP for managing records and information. GARP stands for:
Answer: C
Explanation:
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.
NEW QUESTION # 91
A Data Quality process has remained within statistical control limits for six months, but management wants the average defect rate reduced further. What is the most appropriate conclusion?
Answer: D
Explanation:
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.
NEW QUESTION # 92
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