Data Quality Specialized Credential

Data Quality Specialized Credential Content Outline

1. Data Quality Fundamentals (15%)
   1. Understand the definition and importance of data quality.
   2. Identify categories of data, such as master, reference, transactional, historical, and meta.
   3. Identify the causes of bad data quality and common sources / types of data quality issues.
   4. Recognize the cost of poor data quality (e.g., prevention, failure) and its impact on organizations​.
   5. Understand the role of data quality in decision-making and organizational performance.
2. Data Quality Dimensions and Measurement (30%)
   1. Understand the importance of data quality measurement.
   2. Understand the primary dimensions of data quality: accuracy, completeness, consistency, timeliness, validity, integrity, and uniqueness.
   3. Describe the interrelationships between the data quality dimensions and the significance these interrelationships have on an organization.
   4. Understand how data profiling is used to assess the health of the data.
   5. Understand what data should be targeted for measurement.
   6. Identify methods for measuring the primary dimensions of data quality: accuracy, completeness, consistency, timeliness, validity, integrity, and uniqueness.
3. Data Quality Management Strategies (25%)
   1. Understand the key elements of a data quality strategy.
   2. Understand data quality maturity and recognize the various levels within the data quality maturity model.
   3. Differentiate between proactive vs. reactive approaches to data quality management.
   4. Use Six Sigma DMAIC methodology to systematically enhance data quality.
   5. Apply profiling, validation, and root cause analysis to detect and resolve data issues.
   6. Understand the concept of data cleansing, and apply processes, such as parsing, deduplication, enrichment, and validation techniques to cleanse the data.
4. Data Quality Success Factors and Governance (30%)
   1. Understand common myths (e.g., data is expensive, data quality is data accuracy, data is not my responsibility) about data quality and the challenges associated with data quality initiatives (e.g., lack of awareness, ROI, resistance to change).
   2. Identify critical elements of success, such as leadership support, cross-functional collaboration, and effective communication.
   3. Recognize the importance of continuous improvement, education, and measuring return on investment (ROI) for long-term success.
   4. Explain the concept of data governance, its importance, and its relationship with data quality management.
   5. Apply key components of a governance framework, including policies, processes, and roles, to ensure consistent data quality across the organization.
   6. Describe common misconceptions (e.g., responsibility lies with IT, data governance is a project), the risks of failing to implement strong governance, and strategies for successful data governance integration.

Data Quality Specialized Credential Description

Data Quality Specialized Credential E-Learning

Prerequisites:

There are no prerequisites for the Data Quality Specialized Credential e-learning.

Who Should Attend:

This course is designed for anyone interested in attaining their Specialized Credential in Data Quality or gaining knowledge in the content areas.

Note:

Taking this course does not constitute, nor imply the successful passing of the ASQ Data Quality Specialized Credential examination.

Format

Web-based, self-paced training modules that may involve combinations of text, visuals, audio, practical case studies, interactive simulations, and knowledge checks (see specific courses for features and tools). These web-based courses require an internet-connected device.

Product Access

Access expires after 365 days

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