Training course

Overview

Data Quality Management for Managers is a professional five-day training course designed to equip managers with the knowledge and practical leadership skills required to oversee data quality, establish effective controls, and ensure that organizational information is reliable for operational and strategic decision-making. Managers increasingly depend on accurate data for performance management, financial oversight, resource allocation, customer management, risk assessment, compliance, and business planning. This course provides a management-focused approach to data quality, emphasizing governance, accountability, performance measurement, risk management, process control, and continuous improvement.

The course introduces managers to the core dimensions of data quality, including accuracy, completeness, consistency, validity, uniqueness, timeliness, integrity, and relevance. Participants learn how to evaluate data-quality performance without needing to become technical data specialists and how to establish practical management controls around data collection, validation, preparation, reporting, and use. Practical management tools such as data-quality scorecards, KPI dashboards, control matrices, data-quality checklists, issue registers, risk matrices, data dictionaries, responsibility frameworks, and management review templates are incorporated throughout the training.

Through management case studies, practical exercises, simulations, and realistic organizational scenarios, participants examine how poor data quality affects operational performance, reporting accuracy, compliance, customer experience, financial results, and strategic decisions. The course focuses on identifying recurring data-quality problems, determining their root causes, assigning accountability, prioritizing remediation activities, monitoring corrective actions, and establishing sustainable quality controls. Participants also explore data governance, data stewardship, data ownership, metadata, data lineage, master data, privacy, security, and responsible data-management practices from a managerial perspective.

By the end of the Data Quality Management for Managers course, participants will be able to establish effective managerial oversight of data quality, evaluate organizational data-quality risks, monitor performance, and lead improvement initiatives across teams and departments. Participants will develop practical capabilities for setting quality expectations, reviewing data-quality metrics, managing exceptions, allocating improvement resources, coordinating remediation, and embedding continuous improvement into operational processes. The course culminates in a management-focused capstone exercise through which participants develop a data-quality management framework, governance structure, performance scorecard, and improvement action plan suitable for implementation within their organization.

Course Duration

5 Days (40 Hours)

Target Participants

This course is suitable for:

• Departmental managers responsible for operational or management data

• Operations, finance, HR, sales, marketing, procurement, and administrative managers

• Business intelligence and reporting managers

• Data management and data governance managers

• IT and digital transformation managers

• Risk, compliance, audit, and quality managers

• Managers responsible for performance reporting and organizational KPIs

• Supervisors and team leaders preparing for broader data-management responsibilities

• Managers responsible for implementing data-quality improvement initiatives

• Senior professionals who need managerial oversight skills for data quality

Course Objectives

By the end of the training, participants will be able to:

• Explain the managerial importance of data quality and reliable management information

• Identify the major dimensions and characteristics of high-quality organizational data

• Assess the operational, financial, strategic, and compliance impacts of poor data quality

• Establish managerial data-quality standards, expectations, and accountability structures

• Evaluate data-quality metrics, KPIs, scorecards, dashboards, and management reports

• Identify and prioritize data-quality risks and improvement opportunities

• Establish effective preventive, detective, and corrective data-quality controls

• Manage data-quality issues, exceptions, escalations, and remediation activities

• Apply data governance, stewardship, ownership, metadata, and data lineage principles

• Use root-cause analysis to address recurring data-quality problems

• Develop practical data-quality policies, procedures, checklists, and review processes

• Monitor team and departmental data-quality performance

• Integrate data quality with risk management, compliance, internal controls, and audit

• Evaluate opportunities for automation and technology-enabled data-quality monitoring

• Lead continuous improvement initiatives that strengthen data reliability and organizational performance

• Develop a practical managerial data-quality management framework and implementation roadmap

Course Content

Day 1: Foundations of Data Quality Management for Managers

Module 1: Managerial Principles, Responsibilities, and Data Quality

Topics

1. Introduction to Data Quality Management for Managers
2. The Managerial and Strategic Value of High-Quality Data
3. Understanding the Organizational Data Lifecycle
4. Data Quality Dimensions: Accuracy, Completeness, Consistency, Validity, Uniqueness, Timeliness, Integrity, and Relevance
5. Common Sources and Causes of Poor Data Quality
6. Operational, Financial, Customer, Compliance, and Strategic Impacts of Poor Data
7. Data Quality Requirements, Standards, Business Rules, and Acceptance Criteria
8. Managerial Roles, Data Ownership, Accountability, and Decision Rights
9. Establishing Data Quality Policies, Procedures, and Management Expectations
10. Practical Exercise: Assessing Data Quality Risks in a Departmental Business Dataset

Day 2: Data Quality Assessment and Management Performance

Module 2: Measuring, Monitoring, and Reporting Data Quality

Topics

1. Data Profiling and Data Quality Assessment for Managers
2. Identifying Missing, Duplicate, Invalid, Inconsistent, and Anomalous Data
3. Data Validation, Verification, Reconciliation, and Sampling
4. Establishing Data Quality Rules and Management Control Points
5. Developing Data Quality KPIs, KRIs, Thresholds, and Tolerance Levels
6. Designing Data Quality Scorecards and Management Dashboards
7. Data Quality Issue Registers, Exception Reporting, and Escalation
8. Setting Data Quality Targets and Measuring Performance Improvement
9. Reviewing Data Quality Reports and Making Management Decisions
10. Case Study: Analyzing a Data Quality Dashboard and Developing Management Actions

Day 3: Governance, Controls, and Data Quality Risk

Module 3: Managerial Data Governance and Quality Control

Topics

1. Data Governance Principles and Managerial Responsibilities
2. Data Stewardship, Ownership, Custodianship, and Accountability
3. Designing Departmental and Enterprise Data Quality Governance Structures
4. Data Dictionaries, Metadata, Business Glossaries, and Standard Definitions
5. Data Lineage, Traceability, and Data Lifecycle Controls
6. Master Data Management and Critical Business Data
7. Preventive, Detective, and Corrective Data Quality Controls
8. Data Quality Risk Assessment and Risk-Based Prioritization
9. Root-Cause Analysis Using 5 Whys, Fishbone, Pareto, and Related Techniques
10. Practical Exercise: Investigating a Recurring Data Quality Problem and Developing a Managerial Remediation Plan

Day 4: Data Quality Improvement, Technology, and Change Management

Module 4: Advanced Managerial Data Quality Improvement

Topics

1. Data Cleansing, Standardization, Transformation, and Remediation Strategies
2. Managing Duplicate Records, Conflicting Values, Exceptions, and Anomalies
3. Designing Corrective and Preventive Action Plans
4. Monitoring Data Quality Across Departments, Systems, and Business Processes
5. Automating Data Validation and Data Quality Monitoring
6. Using Excel, Power Query, SQL, Dashboards, and Data Quality Platforms
7. Data Quality in Cloud, Integrated, and Enterprise Data Environments
8. Managing Data Quality Change, Communication, and Stakeholder Engagement
9. Applying PDCA, Lean, Six Sigma, and Continuous Improvement Principles
10. Practical Simulation: Leading a Department-Wide Data Quality Improvement Initiative

Day 5: Strategic Data Quality Leadership and Continuous Improvement

Module 5: Data Quality Strategy, Governance, and Managerial Excellence

Topics

1. Designing a Managerial Data Quality Management Framework
2. Establishing Data Quality Objectives, Standards, Governance, and Operating Procedures
3. Prioritizing Data Quality Initiatives Using Business Impact, Risk, and Value
4. Developing Executive and Management Data Quality Scorecards
5. Integrating Data Quality with Risk Management, Compliance, Audit, and Internal Controls
6. Building Data Quality Culture, Accountability, and Team Capability
7. Establishing Sustainable Data Quality Monitoring and Management Review Processes
8. Developing Data Quality Maturity Assessments and Continuous Improvement Roadmaps
9. Capstone Exercise: Designing a Complete Data Quality Management Framework for a Department or Organization
10. Final Case Study, Practical Assessment, and Managerial Data Quality Improvement Action Plan