Big Data Governance Training Course

Big Data Governance Training Course

Introduction

The digital age is defined by an exponential growth in data, transforming it from a mere resource into an invaluable asset. Yet, with great volume comes great responsibility and significant risk. Our "Bad Data or Better Data or Best Data" training course addresses this critical challenge head-on, empowering organizations to transcend the pitfalls of poor data quality and unlock their true potential. We delve into the complexities of big data governance, providing a holistic framework to manage the entire data lifecycle from creation to retirement. The course is built on the premise that data must be actively governed to drive strategic value, mitigate risk, and ensure regulatory compliance.

Big Data Governance Training Course is designed to equip professionals with the skills to establish and maintain a robust data governance ecosystem. We emphasize practical, real-world applications, moving beyond theoretical concepts to focus on actionable strategies. By tackling issues like data veracity, metadata management, and data lineage, participants will learn how to turn chaotic data into a clean, trustworthy, and actionable resource. Ultimately, this course is not just about managing data; it's about building a data-driven culture that fosters operational excellence, improves data quality, and enhances data security to make better, more informed business decisions.

Course Duration

5 days

Course Objectives

1. Define and implement a robust big data governance framework.
2. Ensure data quality and veracity for reliable business intelligence.
3. Establish clear data stewardship and data ownership roles.
4. Master metadata management and data cataloging for improved discoverability.
5. Develop and enforce data privacy and security protocols.
6. Navigate complex regulatory compliance with frameworks like GDPR and CCPA.
7. Drive a data-driven culture and secure executive buy-in.
8. Implement data lineage and traceability for auditable data flows.
9. Leverage Master Data Management (MDM) for a single source of truth.
10. Mitigate data risk and prevent costly data breaches.
11. Optimize data storage and data lifecycle management.
12. Enable advanced AI and machine learning initiatives with high-quality data.
13. Create a roadmap for scalable and sustainable data governance.

Organizational Benefits

• Enhanced data accuracy and consistency leading to more reliable reporting and analytics.
• Reduced exposure to data breaches, compliance violations, and legal penalties.
• Empowered leadership with trustworthy, high-quality data for strategic planning.
• Streamlined data processes and reduced time wasted on data cleansing and validation.
• Accelerated innovation and competitive advantage through confident use of data.
• Ensured adherence to evolving data regulations, building customer trust and avoiding fines.

Target Audience

• Data Governance Professionals.
• Data and Analytics Leaders.
• IT & Technology Managers.
• Business Leaders.
• Compliance and Risk Officers.
• Data Scientists & Analysts.
• Project and Program Managers.
• Cloud Engineers.

Course Outline

Module 1: The Foundation of Data Governance

• Introduction to Big Data Governance.
• Defining the Governance Framework
• Roles and Responsibilities.
• Data Literacy and Culture.
• Case Study: Analyzing a major company's failure to govern its customer data, leading to inaccurate market analysis and a failed product launch.

Module 2: Data Quality & Veracity

• Data Quality Dimensions.
• Data Profiling: Tools and techniques to assess the current state of data quality.
• Data Cleansing and Validation: Practical steps to correct and prevent bad data.
• Establishing Data Quality Rules.
• Case Study: A large retail chain uses data profiling to discover a 30% error rate in its customer addresses, which was costing millions in failed deliveries and marketing.

Module 3: Metadata and Data Lineage

• Metadata Management.
• Data Cataloging: Building a searchable inventory of organizational data assets.
• Understanding Data Lineage.
• Ensuring Traceability: Tools and techniques for creating an auditable trail for data.
• Case Study: A financial institution traces a reporting error to a single faulty data transformation script, using data lineage to quickly pinpoint and fix the issue.

Module 4: Master Data Management (MDM)

• The Single Source of Truth.
• Identifying Master Data.
• MDM Implementation Strategies.
• Data Synchronization: Ensuring consistency of master data across all systems.
• Case Study: A healthcare provider unifies patient records across multiple hospitals using an MDM solution, improving patient care and billing accuracy.

Module 5: Data Security and Privacy

• Data Classification.
• Access Control and Encryption.
• Data Masking and Tokenization:
• Incident Response and Breach Management.
• Case Study: A social media company faces a major privacy breach, prompting a deep dive into its data governance practices to rebuild trust and fortify security.

Module 6: Regulatory Compliance & Ethics 

• Navigating the Regulatory Landscape.
• Compliance as a Governance Mandate.
• Ethical Data Use.
• Auditing and Reporting: Preparing for and passing data governance audits.
• Case Study: A global tech company overhauls its data policies in response to a GDPR fine, showcasing the real-world impact of non-compliance.

Module 7: The Technology of Data Governance

• Data Governance Tools: A survey of leading platforms and their capabilities.
• AI and Machine Learning in Governance.
• Data Governance in the Cloud: Challenges and best practices for cloud-based data.
• Data Governance as Code: Adopting DevOps principles for data management.
• Case Study: A logistics company implements a new data catalog and governance tool, enabling self-service analytics and reducing data-related support tickets by 50%.

Module 8: Building the Business Case & Roadmap 

• Calculating the ROI of Governance.
• Gaining Stakeholder Buy-in.
• Building a Phased Roadmap.
• Continuous Improvement: The importance of an agile approach to governance.
• Case Study: A manufacturing firm creates a compelling business case, securing a multi-million dollar budget for a data governance program by linking it directly to supply chain optimization and cost savings.

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: [email protected] 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.