Data Quality Profiling, Rules and Remediation Training Course

Course Overview

Learning Objectives

• By the end of this course, participants will be able to:
• Identify and document critical data elements for a business process.
• Profile datasets to detect missing, invalid, duplicate and inconsistent values.
• Translate business requirements into precise, testable data quality rules.
• Test, version and maintain rules in a controlled rule catalogue.
• Decide where rules should run: at data entry, in pipelines or in reporting.
• Set thresholds and tolerances with data owners for each critical rule.
• Design data quality scorecards and trend KPIs by domain and dimension.
• Log, classify and prioritize data quality issues by business impact.
• Apply structured root-cause analysis techniques to recurring data defects.
• Plan and execute standardization, matching, deduplication and cleansing safely.
• Embed preventive controls and stewardship routines to sustain improvement.

Who Should Attend

Learning Outcomes

• By the end of the course, participants will be able to:
• Select and document the critical data elements that matter most to the business.
• Produce a profiling report that quantifies defects in a real dataset.
• Write clear, testable data quality rules across all core dimensions.
• Maintain a rule catalogue with owners, versions and test evidence.
• Agree realistic thresholds and tolerances with data owners.
• Build scorecards and KPIs that show quality trends by domain.
• Estimate the business cost of poor data quality.
• Run an issue register with severity, ownership and service levels.
• Use five whys, fishbone and Pareto analysis to isolate root causes.
• Plan safe cleansing, matching and deduplication with rollback options.
• Embed preventive controls at the point of data entry.
• Present a practical remediation plan to stewards and data owners.

Course Outline