Data Quality

Question 1

What messes up reliable and trustworthy data

Answer 1

• Lack of understanding of poor quality data on Org success
• Bad planning
• Siloed system design
• Inconsistent development processes
• Incomplete documentation
• Lack of standards
• Lack of governance

Question 2

Business Drivers for Establishing a formal Data Quality Management Program

Answer 2

• Increase the value of organizational data and the opportunities to use it
• Reducing risks and costs associated with poor quality data
• Improving organizational efficiency and productivity
• Protecting and enhancing the organization’s reputation

Question 3

DQ Goals

Answer 3

• Developing a governed approach to make data fit for purpose based on data consumers’ requirements
• Defining standards and specifiations for data quality controls as part of data lifecycle
• Defining and implementing processes to measure, monitor, and report on data quality levels
• Identifying and advocating for opportunities to improve the quality of data, through changes to processes and systems and engaging in activities that measurably improve the quality of data based on data consumer requirements

Approach/standards/processes/proactiveness

Question 4

DQ Principles

Answer 4

• Criticality: A DQ Program should focus on the data most critical to the enterprise and its customers. Priorities for improvement should be based on the criticality of the data and on the level of risk if data is not correct
• Lifecycle management: Across lifecycle, from creation/procurement to disposal. Managing data within and between systems
• Prevention: The focus of DQ Program should be on preventing data errors and conditions that reduce the usability of data; it should not be focused on sumply correcting records
• Root cause remediation: Improving the quality of data goes beyond correcting errors. Problems with the quality of data should be understood and addressed at their root causes, rather than just their symptoms. Cuz these causes are often related to process or system design, improving data quality often requires changes to processes and systems that support them.
• Governance: DG Activities must support the development of high quality data and DQ program activities must support and sustain a governed data environment
• Standards - driven: All stakeholders in the DL have data quality requirements. To the degree possible, these requirements should be defined in the form of measurable standards and expectations agains which the quality of data can be measured.
• Objective measurement and transparency: DQ levels need to be measured objectively and consistently. Measurements and measurement methodology should be shared with stakeholders since they are arbiters of quality.
• Embedded in business processes: Business process owners are responsible for the quality of data prodeced through their processes. They must enforce data quality standards in their processes.
• Systematically enforced: System owners must systematically enforce data quality requirements.
• Conencted to service levels: DQ reporting and issues management should be incorporated into Service Level Agreement.

Question 5

Concept: Data Quality

Answer 5

• Data is of high quality to the degree that it meets the expectations and needs of data consumers
• Data quality is thus dependent on context and on the the needs of the data consumer
• Expectation related to quality is not always known. Customers may not articulate. DM Professional need to better understand the requirement.

Question 6

Concept: Critical Data

Answer 6

• One principle of data quality management is to focus on improvement efforts on data that is most important to the org and its customers, in order to make direct, measurable impact on business needs.
• Data can be assessed based on whether it is required by:
• Regulatory Reporting
• Financial reporting
• BUsiness Policy
• Ongoing operations
• Business strategy, especially efforts at competitive differentiation
• Master data is critical by definition. Data sets or indicidual data elements can be assessed for criticality based on the processes that consume them, the nature of the reports they appear in, or the financial, regulatory, or reputational risk to the organization if something were to go wrong with the data.

Question 7

Concept: Data Quality Dimensions: Strong-Wang Framework

Answer 7

• DQ Dim is a measurable feature or characteristic of data

4 General Categories and 15 dimensions:

• Intrinsic DQ: Accuracy; Objectivity; Believability; Reputation
• Contextual DQ: Value -Added; Relevancy; Timeliness; Completeness; Appropriate amount of data
• Representational DQ: Interpretability; Ease of understanding; Representational consistency; Concise representation
• Accessibility DQ: Accessibility; Access Security