Data Quality Management: Principles, Dimensions, and Improvement Lifecycle

What assumption do companies make about their systems regarding data?

Companies assume that if systems are properly functioning, data must be reliable and trustworthy.

What is a common issue with customer data in claims systems?

Customer data such as name, phone number, and email is often outdated.

What contributes to the quality of data in organizations?

All data management disciplines contribute to the quality of data.

What is the benefit of having a formal data quality management framework?

Companies with a formal framework experience fewer data quality issues.

How should data quality be treated in organizations?

Data quality should be treated as an ongoing effort, not a project with a start and end date.

What are some business drivers for establishing a data quality management program?

Increasing data value, reducing risks and costs, improving efficiency, and protecting the organization's reputation.

What are the consequences of poor data quality?

Fines, lost revenue, lost customers, and negative media exposure.

What are the goals of successful data quality programs?

Developing a governed approach, defining standards, measuring and monitoring data quality, and advocating for improvements.

What principles should successful data quality programs focus on?

Criticality, lifecycle management, prevention, root cause remediation, governance, standards-driven, objective measurement, and connection to SLAs.

What does data quality refer to?

Data quality refers to the characteristics of high-quality data and the processes used to measure or improve it.

What is critical data?

Critical data is data that is essential for regulatory reporting, financial reporting, business policy, ongoing operations, and business strategy.

What are the six core data quality dimensions identified by DAMA UK?

Completeness, uniqueness, timeliness, validity, accuracy, and consistency.

What role does metadata play in data quality?

Metadata defines what data represents and is essential for formalizing data quality measures.

What is ISO 8000?

ISO 8000 is the international standard for data quality, defining characteristics that can be tested for data conformance.

What is the purpose of the Data Quality Improvement Lifecycle?

To assess the relationship between inputs and outputs to ensure process requirements are met and outputs conform to expectations.

What are common types of data quality business rules?

Definitional conformance, value presence, format compliance, value domain membership, range conformance, consistency rules, accuracy verification, uniqueness verification, and timeliness verification.

What are common causes of data quality issues?

Lack of leadership, data entry processes, data processing functions, system design, and insufficient regression testing.

What is data profiling?

Data profiling is a form of data analysis used to inspect data and assess its quality using statistical techniques.

What is data cleansing?

Data cleansing, or scrubbing, transforms data to conform to standards and rules by detecting and correcting errors.

What is data enhancement?

Data enhancement is the process of adding attributes to a data set to increase its quality and usability.

What questions should be asked to define high-quality data?

What do stakeholders mean by high-quality data? What is the impact of low-quality data? How will higher quality enable business strategy?

What is the importance of a data quality strategy?

A data quality strategy aligns priorities with business strategy and guides the execution of data quality improvements.

What does a data quality SLA specify?

A data quality SLA specifies expectations for response and remediation for data quality issues, including covered data elements and timelines.

What should data quality reporting focus on?

Data quality scorecards, trends, SLA metrics, issue management, and positive effects of improvement projects.

What techniques can be used for data quality?

Preventive actions, corrective actions, quality checks, effective metrics, statistical process control, and root cause analysis.

What implementation guidelines should be considered for data quality programs?

Metrics on data value, IT/business interaction models, changes to project execution, business processes, and funding for remediation.