Training Course on Data Governance and Quality Management

Training Course on Data Governance & Quality Management

Introduction

Training Course on Data Governance & Quality Management is meticulously designed to equip professionals with the essential knowledge and practical skills required to implement and manage robust Data Governance and Data Quality Management frameworks within any organization. In today's data-driven landscape, data integrity, regulatory compliance, and data literacy are paramount for informed decision-making and sustainable business growth. This program delves into the symbiotic relationship between governance and quality, ensuring participants can establish a single source of truth, mitigate risks, and unlock the true value of their data assets.

Organizations are grappling with an explosion of data, making effective data stewardship a critical competitive advantage. This course provides a comprehensive roadmap, covering everything from establishing data policies and data stewardship models to leveraging data profiling, data cleansing, and metadata management techniques. Through practical exercises and real-world case studies, attendees will gain actionable insights to foster a data-driven culture, enhance operational efficiency, and ensure adherence to evolving data privacy regulations like GDPR and CCPA.

Course Duration

10 days

Course Objectives

1. Design and implement comprehensive data governance models, integrating people, processes, and technology.
2. Understand and apply key data quality dimensions: accuracy, completeness, consistency, timeliness, validity, and integrity.
3. Define roles, responsibilities, and accountability for effective data stewardship across the enterprise.
4. Utilize advanced tools and methodologies for data profiling and data quality assessment.
5. Execute practical data cleansing, data standardization, and deduplication strategies.
6. Implement robust metadata management and data cataloging solutions for enhanced data discoverability.
7. Navigate and adhere to critical data privacy laws (GDPR, CCPA) and industry-specific regulations.
8. Understand and apply best practices for data security, access control, and data protection.
9. Optimize data quality for superior business intelligence, data analytics, and AI/ML initiatives.
10. Explore the application of AI and Machine Learning in automating data governance and quality processes.
11. Champion organizational data literacy and promote a strong data-driven decision-making environment.
12. Design and implement continuous data quality monitoring and performance measurement systems.
13. Prepare for future challenges and opportunities in cloud-native data governance, DataOps, and ethical AI governance.

Organizational Benefits

• Leads to more accurate insights and trusted data for critical business decisions.
• Reduces legal risks, avoids penalties, and builds customer trust.
• Streamlines data-related processes, reduces rework, and optimizes resource utilization.
• Proactively identifies and mitigates data-related security, privacy, and compliance risks.
• Maximizes the value derived from data assets, driving innovation and competitive advantage.
• Provides a solid foundation for informed strategic planning and execution.
• Fosters collaboration and data sharing across departments.
• Implements robust controls to protect sensitive information.

Target Audience

1. Data Governance Leaders & Program Managers
2. Data Stewards & Data Custodians
3. IT Professionals & Data Architects
4. Business Analysts & Data Analysts
5. Compliance & Risk Management Professionals
6. Senior Executives & Department Heads
7. Information Security Professionals.
8. Anyone aspiring to a career in data management, data governance, or data quality.

Course Outline

Module 1: Introduction to Data Governance & Quality Management

• Defining Data Governance and Data Quality in the modern enterprise.
• The critical link between data governance, data quality, and business value.
• Understanding the "garbage in, garbage out" principle.
• Key drivers for establishing data governance: compliance, analytics, trust.
• Case Study: A financial institution facing regulatory fines due to inconsistent customer data.

Module 2: Building the Data Governance Framework

• Components of a successful data governance framework: people, processes, technology.
• Establishing a Data Governance Council and defining organizational roles.
• Developing a Data Governance Charter and operationalizing data policies.
• Implementing a phased approach to data governance rollout.
• Case Study: How a large retail chain structured its data governance office to centralize data ownership.

Module 3: Data Stewardship: Roles & Responsibilities

• Defining the role of a Data Steward and their accountability.
• Empowering business users as data owners and data consumers.
• Establishing a clear hierarchy of data ownership and responsibilities.
• Best practices for training and empowering data stewards.
• Case Study: A healthcare provider empowering frontline staff to improve patient data accuracy.

Module 4: Data Quality Dimensions & Assessment

• Deep dive into the 6 core dimensions of data quality: accuracy, completeness, consistency, timeliness, validity, integrity.
• Methods for assessing current data quality maturity.
• Developing data quality metrics and Key Performance Indicators (KPIs).
• Tools and techniques for data profiling and data quality analysis.
• Case Study: An e-commerce company using data profiling to identify inconsistencies in product catalog data.

Module 5: Data Cleansing & Standardization Techniques

• Strategies for identifying and resolving data quality issues.
• Practical approaches to data cleansing, parsing, and formatting.
• Implementing data standardization and normalization rules.
• Techniques for data deduplication and record matching.
• Case Study: A telecommunications firm cleaning customer address data to improve marketing campaign effectiveness.

Module 6: Metadata Management & Data Cataloging

• Understanding metadata: technical, business, and operational.
• The importance of a centralized data catalog for data discovery.
• Implementing metadata repositories and data lineage tracking.
• Utilizing data cataloging tools for enhanced data findability.
• Case Study: A manufacturing company using a data catalog to improve data searchability and understanding across departments.

Module 7: Master Data Management (MDM)

• Introduction to Master Data Management and its role in data governance.
• Identifying critical master data domains (customer, product, vendor).
• Strategies for building a single source of truth for master data.
• Implementing MDM solutions and best practices.
• Case Study: A global corporation implementing MDM to achieve a consistent view of their customer base.

Module 8: Data Privacy Laws & Regulatory Compliance

• Overview of key data privacy regulations: GDPR, CCPA, HIPAA, etc.
• Strategies for ensuring compliance with data protection principles.
• Implementing data anonymization and pseudonymization techniques.
• Conducting data privacy impact assessments (DPIAs).
• Case Study: A technology company adapting its data practices to comply with GDPR's right to be forgotten.

Module 9: Data Security & Access Control

• Fundamentals of data security within a data governance framework.
• Implementing role-based access control (RBAC) and data masking.
• Strategies for data encryption and secure data transmission.
• Developing incident response plans for data breaches.
• Case Study: A financial services firm enhancing data security measures to protect sensitive customer financial information.

Module 10: Data Governance for Business Intelligence & Analytics

• Ensuring data quality for reliable BI dashboards and reports.
• Optimizing data governance for accurate analytics and predictive modeling.
• Connecting data governance to strategic business outcomes.
• Measuring the impact of data governance on business intelligence initiatives.
• Case Study: An advertising agency improving marketing ROI through high-quality data feeding their analytics platform.

Module 11: Data Governance in Cloud & Hybrid Environments

• Challenges and opportunities of data governance in cloud computing.
• Strategies for governing data across multi-cloud and hybrid infrastructures.
• Best practices for cloud data security and compliance.
• Leveraging cloud-native data governance tools and services.
• Case Study: A growing SaaS company implementing data governance for its rapidly expanding cloud data lake.

Module 12: Automation & Emerging Technologies in Data Governance

• The role of AI and Machine Learning in automating data quality checks.
• Exploring Robotic Process Automation (RPA) for data governance tasks.
• Leveraging blockchain for enhanced data lineage and trust.
• Trends in autonomous data governance and self-service data management.
• Case Study: A research institution using AI to automate metadata tagging and data classification.

Module 13: Fostering a Data-Driven Culture & Data Literacy

• Strategies for promoting data literacy across the organization.
• Communicating the value of data governance to all stakeholders.
• Building a culture of data accountability and ownership.
• Training programs and workshops to enhance data understanding.
• Case Study: A government agency launching an internal "Data University" program to upskill its workforce.

Module 14: Data Quality Monitoring, Auditing & Reporting

• Implementing continuous data quality monitoring systems.
• Designing effective data quality dashboards and alerts.
• Conducting regular data quality audits and reviews.
• Strategies for root cause analysis and proactive issue resolution.
• Case Study: A logistics company using real-time data quality dashboards to identify and resolve delivery data errors.

Module 15: Future Trends & Best Practices in Data Governance

• Emerging trends in data governance: DataOps, Ethical AI Governance, Data Mesh.
• The evolving landscape of data regulations and compliance.
• Best practices for continuous improvement and adapting to change.
• Future-proofing your data governance strategy.
• Case Study: A forward-thinking enterprise exploring Data Mesh principles to decentralize data ownership and access.

Training Methodology

This training course employs a highly interactive and practical methodology designed for maximum engagement and knowledge retention. It will incorporate:

• Instructor-Led Presentations: Clear and concise explanations of core concepts.
• Interactive Discussions: Fostering knowledge sharing and problem-solving among participants.
• Practical Exercises & Workshops: Hands-on activities to apply learned concepts.
• Real-World Case Studies Analysis: In-depth examination of successful data governance implementations and challenges.
• Group Activities & Collaboration: Promoting teamwork and diverse perspectives.
• Q&A Sessions: Addressing specific queries and facilitating deeper understanding.
• Templates & Checklists: Providing actionable resources for immediate application.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.