Course Introduction
Data quality management and master data management are essential disciplines for organizations seeking to improve the accuracy, consistency, reliability, and usability of their information assets. As businesses increasingly depend on data to support operational activities, financial management, customer relationships, regulatory compliance, and strategic decision-making, poor-quality information can lead to reporting errors, inefficient processes, duplicated records, and unreliable business outcomes. This course provides a comprehensive introduction to data quality principles, master data concepts, governance practices, and practical techniques for managing organizational information throughout its lifecycle.
The programme explores the dimensions of data quality and their importance in maintaining dependable information across organizational systems. Participants will examine accuracy, completeness, consistency, validity, uniqueness, timeliness, and integrity as key measures of information quality. They will learn how data quality problems arise through manual data entry, disconnected applications, inconsistent business definitions, outdated records, system migrations, and inadequate information management procedures. Emphasis is placed on understanding how these problems affect business performance, customer service, financial reporting, operational efficiency, and organizational accountability.
A major component of the course focuses on master data fundamentals and the management of critical business information shared across multiple systems and departments. Participants will examine customer, supplier, employee, product, asset, location, and organizational master data, including the processes required to create, maintain, validate, and distribute authoritative records. The programme introduces master data models, unique identifiers, reference data, data hierarchies, entity matching, record linkage, and the principles of establishing a single trusted view of important organizational entities. Participants will also explore the relationship between master data management, transactional data, metadata, and enterprise information architecture.
The course addresses practical data quality assessment and improvement techniques, including data profiling, validation rules, standardization, cleansing, deduplication, enrichment, reconciliation, and exception management. Participants will learn how to identify unreliable records, investigate root causes, establish data quality rules, and monitor improvements through measurable indicators. The programme also examines the responsibilities of data owners, data stewards, database administrators, business users, and information technology teams in maintaining reliable organizational data. Practical attention is given to developing data quality scorecards, issue registers, business rules, documentation standards, and continuous improvement procedures.
Contemporary data management developments are incorporated throughout the programme, including cloud-based master data platforms, automated data quality monitoring, data catalogues, metadata management, and integration across enterprise applications. Participants will explore how artificial intelligence and machine learning can support entity resolution, anomaly detection, automated classification, and data cleansing while recognizing the need for human validation and responsible oversight. The course also addresses data privacy, cybersecurity, regulatory compliance, data lineage, access management, and emerging data governance challenges associated with distributed and increasingly automated information environments.
By the end of the course, participants will be better equipped to assess organizational data quality, understand master data structures, identify information management weaknesses, and contribute to effective data improvement initiatives. They will gain practical knowledge of data cleansing, standardization, validation, governance, and master data maintenance processes. These capabilities will help organizations reduce information errors, improve system interoperability, strengthen reporting reliability, enhance customer and supplier information management, and establish a more dependable foundation for business intelligence and evidence-based decision-making.
Duration
5 days
Who Should Attend
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Data quality managers and data quality analysts
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Master data management specialists
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Data governance managers and officers
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Data stewards and data owners
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Database administrators and database developers
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Business intelligence managers and analysts
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Data engineers and data integration specialists
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Business analysts and systems analysts
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Information management professionals
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Enterprise data architects
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Customer relationship management specialists
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Finance and accounting information managers
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Procurement and supplier management professionals
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Human resource information systems specialists
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IT managers and digital transformation professionals
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Risk, compliance, and internal audit officers
Course Objectives
By the end of this course, participants will be able to:
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Explain the fundamental principles of data quality management and master data management and describe their importance in enterprise information management and business intelligence.
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Identify and evaluate key data quality dimensions, including accuracy, completeness, consistency, validity, uniqueness, timeliness, and integrity, using appropriate assessment criteria.
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Differentiate between master data, transactional data, reference data, and metadata, and explain their respective roles in maintaining consistent organizational information.
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Identify critical master data domains, including customers, suppliers, employees, products, assets, and locations, and establish appropriate structures for managing authoritative records.
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Apply data profiling, validation, standardization, cleansing, deduplication, and enrichment techniques to identify and correct common information quality problems.
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Develop data quality rules, validation criteria, business definitions, and monitoring indicators to improve the reliability and consistency of organizational datasets.
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Establish master data management processes involving record creation, approval, modification, matching, merging, hierarchy management, and distribution across enterprise systems.
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Define data governance responsibilities, including data ownership, stewardship, accountability, escalation procedures, and documentation requirements for sustainable data management.
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Implement appropriate data quality monitoring, issue resolution, privacy, security, and compliance controls to protect organizational information throughout its lifecycle.
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Develop practical data quality and master data improvement strategies that strengthen reporting accuracy, system interoperability, operational efficiency, and organizational decision-making.
Comprehensive Course Outline
Module 1: Fundamentals of Data Quality Management and Master Data
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Understanding data quality management concepts, principles, objectives, and the importance of reliable information in modern organizational environments.
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Exploring the relationship between data quality, master data management, business intelligence, enterprise information management, and evidence-based decision-making.
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Examining common causes of poor-quality information, including manual entry errors, inconsistent formats, duplicated records, outdated information, and disconnected systems.
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Assessing the organizational consequences of unreliable data for customer service, financial reporting, operational performance, compliance, and strategic planning.
Module 2: Data Quality Dimensions, Standards and Assessment Frameworks
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Understanding the major dimensions of data quality, including accuracy, completeness, consistency, validity, uniqueness, timeliness, and referential integrity.
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Establishing measurable data quality criteria and business rules for assessing the reliability of customer, financial, operational, and administrative information.
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Examining data quality standards, assessment frameworks, scorecards, measurement methodologies, and organizational performance indicators.
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Identifying data quality risks and evaluating the impact of inaccurate, incomplete, inconsistent, or outdated records on business processes and reporting outcomes.
Module 3: Master Data Management Concepts and Core Data Domains
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Understanding master data management principles and the role of authoritative records in maintaining consistent information across organizational applications and departments.
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Examining key master data domains, including customer, supplier, employee, product, asset, location, and organizational structure information.
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Differentiating master data from transactional data, reference data, metadata, and analytical data within enterprise information environments.
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Exploring master data models, unique identifiers, entity relationships, data hierarchies, classifications, and common business definitions.
Module 4: Data Profiling, Cleansing and Standardization Techniques
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Applying data profiling techniques to examine dataset structures, identify missing values, detect anomalies, and assess the overall quality of organizational records.
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Implementing data cleansing techniques to correct inaccurate information, resolve inconsistencies, remove irrelevant records, and improve dataset completeness.
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Standardizing names, addresses, dates, product descriptions, identification fields, organizational classifications, and other frequently used data attributes.
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Developing deduplication, matching, merging, and record reconciliation procedures to reduce redundant information and establish consistent master records.
Module 5: Master Data Modelling and Reference Data Management
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Developing master data models that define business entities, attributes, relationships, identifiers, hierarchies, and rules for maintaining authoritative information.
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Understanding reference data management and the importance of standardized codes, categories, classifications, geographic information, and organizational identifiers.
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Applying entity resolution, record matching, data linking, and survivorship rules to consolidate duplicate or conflicting records from different systems.
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Establishing data dictionaries, naming conventions, reference tables, metadata definitions, and documentation standards for consistent information management.
Module 6: Data Quality Rules, Validation and Monitoring
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Designing validation rules, business constraints, mandatory fields, permissible values, and logical checks to prevent inaccurate or incomplete information from entering systems.
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Applying automated data quality checks to identify duplicates, invalid values, missing attributes, inconsistent relationships, and unexpected changes in datasets.
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Developing data quality scorecards, dashboards, exception reports, and key performance indicators for monitoring information reliability.
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Establishing issue classification, root cause analysis, escalation procedures, corrective actions, and verification processes for resolving data quality problems.
Module 7: Data Governance, Ownership and Stewardship
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Understanding enterprise data governance frameworks and the responsibilities of data owners, data stewards, custodians, administrators, and business users.
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Establishing policies, procedures, accountability structures, decision rights, and approval workflows for managing organizational master data.
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Developing data ownership arrangements and stewardship practices that support consistent information definitions and coordinated management across departments.
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Exploring metadata management, data catalogues, data lineage, documentation, and audit trails to improve transparency and accountability.
Module 8: Master Data Integration and Enterprise Information Systems
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Understanding how master data is shared across enterprise resource planning, customer relationship management, human resource, procurement, finance, and operational systems.
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Examining data integration approaches involving ETL and ELT processes, application programming interfaces, database synchronization, and enterprise data platforms.
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Managing master data consistency across cloud applications, legacy databases, distributed systems, and departmental information repositories.
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Exploring master data management architectures, centralized and decentralized models, registry approaches, and hybrid implementation strategies.
Module 9: Data Security, Privacy and Emerging Technologies
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Applying data access controls, authentication, authorization, encryption, data classification, and information protection measures to sensitive organizational records.
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Understanding data privacy requirements, regulatory obligations, retention policies, consent management, and responsible handling of personal information.
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Exploring artificial intelligence and machine learning applications in data profiling, anomaly detection, entity matching, automated cleansing, and intelligent data classification.
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Evaluating cloud-based master data management, automated data quality monitoring, generative AI-assisted data management, and emerging enterprise governance technologies.
Module 10: Data Quality Improvement Strategies and Practical Implementation
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Conducting organizational data quality assessments to identify critical data domains, existing weaknesses, operational risks, and improvement priorities.
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Developing practical data cleansing and master data consolidation plans using validation rules, matching procedures, standardization, and reconciliation techniques.
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Establishing master data management implementation roadmaps covering governance structures, technology requirements, responsibilities, timelines, and performance measures.
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Designing continuous improvement programmes involving periodic data audits, quality monitoring, staff awareness, issue resolution, and measurable organizational outcomes.
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.
