Mixed Deck — All CDS Topics Flashcards

Which monitoring technique compares today's record count against the trailing 30-day average to detect anomalies?

Answer: Volume-based trend monitoring

Volume-based trend monitoring uses historical averages as a baseline to flag unusual spikes or drops in record counts.

Which metric is most useful for measuring the effectiveness of a data lifecycle management program?

Answer: Percentage of data assets with defined and enforced retention policies

Tracking policy coverage across data assets measures how consistently the lifecycle program governs data from creation to disposal.

How does poor data quality affect business intelligence?

Answer: It results in inaccurate analytics and poor decisions

Poor data quality significantly undermines the effectiveness of business intelligence (BI) initiatives. When data is inaccurate, incomplete, or inconsistent, any analytics performed on it will yield flawed insights and reports. This directly leads to poor, misinformed business decisions, potentially causing financial losses, missed opportunities, and operational inefficiencies.

What is the primary purpose of system monitoring?

Answer: To detect issues proactively and maintain system health

System monitoring enables proactive issue detection and health maintenance, reducing downtime and ensuring consistent service delivery.

What is the purpose of a staging environment?

Answer: To test configurations and changes before deploying to production

Staging environments replicate production conditions for testing configurations and changes, reducing the risk of deployment failures affecting live systems.

A company's address data was loaded using abbreviations ('St' vs 'Street', 'Blvd' vs 'Boulevard'). What type of data quality issue is this?

Answer: Consistency / standardization issue

Inconsistent representation of the same concept (abbreviated vs. spelled-out street suffixes) is a consistency or standardization issue that hinders matching and aggregation.

What is the purpose of data lineage tracking?

Answer: To trace data origin, transformations, and movement through systems

Data lineage tracking traces data from origin through all transformations and movements, supporting audit requirements, troubleshooting, and impact analysis.

What is the primary purpose of a data quality scorecard in a monitoring program?

Answer: To provide a consolidated view of data quality dimensions across critical datasets

A data quality scorecard aggregates metrics across dimensions like completeness, accuracy, and timeliness into a single governance view.

Which activity is part of the 'data creation' stage in a typical data lifecycle model?

Answer: Validating data at point of entry against defined standards

Entry validation at the creation stage ensures data meets quality and format standards before it enters downstream systems.

A data steward is managing resistance from a senior manager who believes the new data governance program adds bureaucracy. What is the most effective response?

Answer: Demonstrate the program's ROI with concrete examples of cost savings or risk reduction relevant to their business area

Connecting governance benefits to outcomes the manager cares about — cost, risk, efficiency — converts skeptics by making value tangible and personal.

Why is change management important during implementation?

Answer: It ensures smooth transitions and user adoption of new systems

Change management facilitates smooth transitions by addressing user concerns, providing training, and ensuring organizational readiness for new systems.

A data steward discovers that two business units use different definitions for 'active customer' in their separate reports. Which governance action resolves this?

Answer: Establish a canonical business glossary definition and align both reports to it

A canonical business glossary provides an agreed-upon definition that eliminates conflicting interpretations across reports.

Why is a data dictionary important?

Answer: It ensures standardized definitions and formats across systems

A data dictionary is a centralized repository that provides standardized definitions, formats, and characteristics for all data elements within an organization's systems. Its importance lies in ensuring consistency and clarity across different applications and departments. This standardization helps prevent misinterpretations, improves data quality, and facilitates data integration and governance.

What is the primary benefit of automating repetitive tasks?

Answer: Reduced human error and increased consistency of results

Automation reduces human error and increases consistency by executing tasks the same way every time, freeing staff for higher-value work.

In MDM, 'party data' most commonly refers to which master data domain?

Answer: Entities such as customers, suppliers, employees, and partners

Party data is the MDM domain covering all types of organizational and individual entities that a business interacts with, including customers, vendors, and employees.

Which metric is most critical when monitoring data pipeline latency in a real-time reporting environment?

Answer: End-to-end processing time from ingestion to availability

End-to-end latency directly measures how quickly data becomes available for decision-making in real-time environments.

Which communication strategy is most effective when introducing a new data governance policy to business unit stakeholders who are resistant to change?

Answer: Engage stakeholders early with workshops that explain the business value and gather their input

Early engagement through collaborative workshops builds stakeholder buy-in by demonstrating business value and incorporating their concerns into the policy design.

How should a root cause analysis be conducted?

Answer: Systematically investigate underlying causes rather than just symptoms

Root cause analysis investigates underlying causes systematically, preventing recurrence rather than just addressing symptoms temporarily.

What is the COST OF POOR DATA QUALITY (CPDQ) model primarily used for?

Answer: Quantifying the financial impact of data defects to justify DQ investments

The CPDQ model quantifies costs such as rework, failed decisions, compliance fines, and lost revenue to build a business case for data quality programs.

Which practice ensures that data quality rules remain aligned with evolving business requirements over time?

Answer: Continuous rule review cycles tied to business process change management

Embedding DQ rule reviews into the change management process ensures rules are updated whenever business processes, regulations, or data sources change.