Data Quality Issue Management and Remediation Training Course

Course Introduction

The Data Quality Issue Management And Remediation Training Course provides a practical and structured approach to identifying, documenting, prioritizing, investigating, resolving, and preventing data quality issues across organizational data environments. Participants learn how to establish systematic processes for managing defects affecting accuracy, completeness, consistency, timeliness, validity, uniqueness, integrity, and fitness for purpose while connecting data quality problems to their operational and business consequences.

The course examines the complete data quality issue lifecycle, from initial detection and issue registration through triage, impact assessment, root-cause analysis, remediation, validation, closure, and continuous monitoring. Participants learn how to distinguish isolated data defects from systemic quality problems and how to establish repeatable workflows that ensure issues are assigned to accountable owners, investigated using appropriate evidence, resolved within agreed priorities, and documented for future reference.

Participants will develop practical skills for designing data quality issue management frameworks, issue registers, classification schemes, severity models, escalation procedures, service-level expectations, and remediation workflows. The training explores how to establish clear responsibilities between data owners, data stewards, business users, technical teams, application owners, database administrators, and governance functions so that data quality issues are not simply identified but actively managed through accountable resolution processes.

The course addresses root-cause analysis techniques that help organizations move beyond repeatedly correcting individual records. Participants examine how defects can originate from source systems, application logic, integration processes, manual data entry, inadequate validation rules, transformation processes, unclear business definitions, metadata deficiencies, poorly designed workflows, or inappropriate governance practices. Emphasis is placed on permanent corrective action, preventive controls, automation, and process improvements that reduce recurrence.

Modern data environments introduce additional complexity to data quality issue management. The course therefore covers quality challenges associated with cloud environments, data warehouses, data lakes, APIs, streaming data, master data, self-service analytics, data products, third-party data, and artificial intelligence. Participants learn how automated monitoring, profiling, observability, lineage, metadata, and quality rules can support faster detection, diagnosis, prioritization, remediation, and verification of data quality issues.

Emerging practices such as automated remediation, intelligent anomaly detection, AI-assisted root-cause analysis, continuous data quality monitoring, DataOps, data observability, and preventive quality controls are incorporated into the training. Participants learn how to build sustainable issue management capabilities that combine people, processes, governance, technology, and measurement to reduce recurring defects and increase confidence in organizational data.

Duration

10 days

Who Should Attend

• Data Quality Managers and Data Quality Analysts

• Data Governance Managers and Data Governance Officers

• Data Stewards and Data Owners

• Data Management and Information Management Professionals

• Master Data Management and Reference Data Specialists

• Database Administrators and Data Engineers

• Data Architects and Enterprise Architects

• Business Intelligence and Analytics Professionals

• Data Integration and ETL/ELT Specialists

• Application Support and Systems Management Professionals

• IT Governance, Risk, and Compliance Professionals

• Internal Audit and Data Control Professionals

• Data Operations and DataOps Teams

• Business Process Owners and Operational Managers

• Digital Transformation and Data Transformation Professionals

• Professionals responsible for monitoring, resolving, or preventing organizational data quality issues

Course Objectives

• Understand the principles of data quality issue management and how systematic remediation processes contribute to reliable, trustworthy, usable, and business-ready organizational data.

• Develop the ability to identify and document data quality issues consistently using standardized definitions, issue categories, evidence requirements, impact descriptions, and accountable ownership structures.

• Apply structured data quality issue lifecycle practices covering detection, registration, classification, prioritization, investigation, remediation, validation, closure, and continuous monitoring.

• Evaluate data quality issues across dimensions including accuracy, completeness, consistency, timeliness, validity, uniqueness, integrity, conformity, and fitness for intended business purposes.

• Design effective issue severity and prioritization models that consider business impact, regulatory exposure, operational disruption, financial consequences, customer impact, and recurrence risk.

• Apply root-cause analysis techniques to determine whether data quality problems originate from source systems, processes, integrations, applications, people, policies, controls, or underlying data structures.

• Establish effective remediation workflows that assign responsibility, define resolution actions, establish deadlines, manage escalations, and provide transparent visibility into unresolved data quality issues.

• Differentiate between tactical data correction, corrective action, preventive action, and systemic process improvement when determining appropriate remediation strategies for recurring data defects.

• Analyze relationships between data quality issues, metadata, lineage, business processes, applications, integrations, data owners, and downstream consumers to understand the full impact of quality failures.

• Implement validation and verification practices that confirm whether remediation activities have genuinely resolved the underlying issue without introducing new defects or compromising related data.

• Develop meaningful data quality issue metrics, dashboards, key performance indicators, and management reports that demonstrate trends, remediation performance, recurring problems, and governance effectiveness.

• Establish continuous improvement mechanisms that use historical issue information, root-cause patterns, automation, monitoring, and lessons learned to prevent recurring quality problems and strengthen data management practices.

Comprehensive Course Outline

Module 1: Foundations Of Data Quality Issue Management

• Understanding data quality issues, defects, incidents, exceptions, and their impact on organizational information assets.

• Exploring the major data quality dimensions and how different quality failures affect business processes and decisions.

• Examining the relationship between data quality, governance, operational efficiency, compliance, analytics, customer experience, and organizational performance.

• Establishing the principles required to create a structured and repeatable data quality issue management capability.

Module 2: Data Quality Issue Lifecycle And Operating Model

• Designing a complete issue lifecycle covering detection, registration, triage, investigation, remediation, validation, closure, and monitoring.

• Defining the roles and responsibilities of data owners, data stewards, analysts, engineers, application teams, and business process owners.

• Establishing issue management workflows that support accountability, transparency, escalation, collaboration, and timely resolution.

• Integrating data quality issue management with broader incident, problem, change, risk, and governance management processes.

Module 3: Issue Detection And Identification

• Applying data profiling, validation rules, exception reports, user feedback, audits, and monitoring systems to identify quality issues.

• Designing automated and manual detection mechanisms for discovering missing, invalid, inconsistent, duplicated, or anomalous data.

• Establishing detection thresholds and alert conditions that distinguish meaningful quality problems from acceptable data variation.

• Using metadata, lineage, business rules, and operational context to understand where and how detected issues affect data consumers.

Module 4: Issue Registration And Documentation

• Designing standardized data quality issue records containing descriptions, affected datasets, evidence, ownership, impact, severity, and status.

• Establishing issue classification structures that support consistent categorization, analysis, reporting, prioritization, and trend monitoring.

• Developing documentation standards that preserve investigation findings, remediation decisions, validation evidence, and lessons learned.

• Creating centralized issue registers that provide visibility into open, resolved, recurring, overdue, and high-impact data quality problems.

Module 5: Data Quality Issue Classification And Prioritization

• Developing severity models that evaluate operational, financial, regulatory, analytical, customer, and reputational consequences.

• Applying business criticality and data sensitivity considerations when determining the priority of individual quality issues.

• Establishing risk-based prioritization approaches that focus remediation resources on issues with the greatest organizational consequences.

• Managing competing priorities, conflicting stakeholder expectations, limited resources, and large volumes of simultaneously detected issues.

Module 6: Data Quality Impact Assessment

• Evaluating how quality issues affect source systems, business processes, reports, dashboards, analytical models, customers, and downstream data consumers.

• Using data lineage and dependency analysis to identify affected applications, datasets, processes, interfaces, and decision-making activities.

• Assessing the scope and scale of quality problems by determining affected records, populations, business units, time periods, and data domains.

• Translating technical quality defects into measurable business consequences that support appropriate remediation decisions and resource allocation.

Module 7: Root-Cause Analysis And Problem Investigation

• Applying structured root-cause analysis techniques to identify the underlying causes of recurring or high-impact data quality issues.

• Investigating source-system configuration, business processes, application logic, integration mappings, transformation rules, and manual data entry practices.

• Using process analysis, five-whys techniques, cause-and-effect analysis, data lineage, and evidence review to support accurate diagnosis.

• Distinguishing symptoms from underlying causes so remediation activities address systemic weaknesses rather than repeatedly correcting individual records.

Module 8: Remediation Strategy And Corrective Action

• Developing remediation strategies appropriate for data correction, process redesign, application changes, integration fixes, and governance improvements.

• Distinguishing short-term containment from permanent corrective actions when responding to urgent or high-impact quality issues.

• Establishing remediation plans with accountable owners, actions, dependencies, timelines, resources, validation requirements, and escalation points.

• Evaluating remediation alternatives according to effectiveness, cost, risk, sustainability, technical feasibility, and potential downstream consequences.

Module 9: Data Cleansing And Technical Remediation

• Applying structured techniques for correcting inaccurate, incomplete, inconsistent, duplicated, obsolete, and improperly formatted data.

• Designing data cleansing rules that preserve data integrity while ensuring corrections are traceable, controlled, and appropriately authorized.

• Using SQL, data transformation processes, ETL/ELT pipelines, validation scripts, and data management tools to support technical remediation.

• Establishing controls that prevent corrected data from being overwritten, corrupted, duplicated, or degraded during subsequent processing.

Module 10: Preventive Controls And Quality Improvement

• Designing preventive validation controls that identify and block data quality defects before they enter operational or analytical systems.

• Integrating quality rules into application workflows, APIs, ingestion pipelines, integration processes, and data transformation environments.

• Establishing automated checks for completeness, validity, uniqueness, referential integrity, consistency, conformity, and business rule compliance.

• Using lessons from historical issues to strengthen processes, controls, training, system design, and governance practices that prevent recurrence.

Module 11: Data Quality Monitoring And Observability

• Establishing continuous data quality monitoring processes that detect emerging problems before they significantly affect downstream consumers.

• Exploring data observability capabilities for monitoring freshness, volume, distribution, schema changes, lineage, anomalies, and pipeline reliability.

• Designing alerts and thresholds that support timely investigation while minimizing excessive notifications and unnecessary operational workload.

• Integrating quality monitoring with dashboards, incident management, workflow systems, governance platforms, and operational support processes.

Module 12: Issue Validation, Closure, And Evidence Management

• Establishing validation procedures that demonstrate whether remediation activities have resolved the original data quality problem.

• Performing post-remediation quality checks to confirm corrected records, restored business rules, and improved downstream data behavior.

• Defining formal issue closure criteria covering evidence, stakeholder approval, control effectiveness, documentation, and residual risk.

• Maintaining remediation histories and evidence trails that support audits, governance reviews, accountability, and future root-cause investigations.

Module 13: Data Quality Issue Metrics And Reporting

• Developing key performance indicators for issue volumes, severity, aging, resolution times, recurrence rates, and remediation effectiveness.

• Designing dashboards that provide executives, governance teams, data stewards, and technical teams with appropriate levels of issue visibility.

• Analyzing issue trends to identify problematic systems, business processes, data domains, applications, or recurring sources of defects.

• Measuring the effectiveness of remediation programs through resolution quality, recurrence reduction, control improvements, and business outcomes.

Module 14: Governance, Accountability, And Escalation

• Establishing governance structures that define ownership, stewardship, accountability, escalation routes, decision rights, and remediation authority.

• Integrating data quality issue management with data governance councils, quality forums, risk committees, and operational management structures.

• Developing escalation mechanisms for overdue, high-risk, unresolved, disputed, or repeatedly occurring data quality issues.

• Managing stakeholder collaboration when remediation requires coordinated action across business units, technology teams, vendors, and data domains.

Module 15: Advanced Automation And Modern Data Environments

• Applying machine learning and AI-assisted techniques to detect anomalies, identify patterns, classify issues, and support root-cause investigations.

• Managing data quality issues across cloud platforms, data lakes, data warehouses, streaming pipelines, APIs, and distributed data architectures.

• Exploring automated remediation workflows that combine detection, diagnosis, correction, validation, notification, and evidence capture.

• Addressing data quality challenges in data products, self-service analytics, third-party data, artificial intelligence, and rapidly changing data ecosystems.

Module 16: Continuous Improvement And Emerging Practices

• Establishing continuous improvement programs that use issue histories, recurring patterns, metrics, lessons learned, and stakeholder feedback.

• Developing preventive quality strategies that move organizations from reactive issue correction toward proactive and continuously monitored quality management.

• Exploring emerging practices including intelligent data observability, AI-assisted remediation, DataOps, automated quality controls, and predictive issue detection.

• Building long-term data quality improvement roadmaps that strengthen trust, reduce recurring defects, improve operational efficiency, and increase the value of organizational data.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.