Master Certificate in Data Governance & Business Intelligence — Certification Brochure

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London Academy of Professional Training
Certificate
Master Certificate in Data Governance & Business Intelligence
Leadership Level
REF: IT-DSA-L  •  IT Centres  •  Data Science & Artificial Intelligence
6Subjects 30Chapters 180Classes 430Total Marks 60%Pass Mark LifetimeValidity
At a Glance
ReferenceIT-DSA-L
AwardCertificate
Assessment430 marks — 258 to pass (60%)
PrerequisitesNone
ValidityLifetime
Awarding BodyLAPT — London Academy of Professional Training
Curriculum
1 Advanced Data Governance 5 chapters · 30 classes  85 marks
• 1 Fundamentals of Data Governance Frameworks — 6 classes
› 1.1 Understanding the Core Components of Data Governance
› 1.2 Exploring Data Roles and Responsibilities
› 1.3 Integrating Data Policies into Business Processes
› 1.4 Evaluating Data Governance Maturity Models
› 1.5 Implementing Key Performance Indicators for Governance
› 1.6 Case Studies: Successful Data Governance Frameworks
• 2 Regulatory Compliance and Ethical Considerations in Data Management — 6 classes
› 2.1 Understanding Regulatory Frameworks in Data Management
› 2.2 Identifying Key Compliance Requirements for Data Governance
› 2.3 Exploring Ethical Considerations in Handling Sensitive Data
› 2.4 Implementing Data Protection Measures and Best Practices
› 2.5 Assessing the Risks and Challenges of Data Non-Compliance
› 2.6 Developing Strategies for Ethical Data Use and Compliance
• 3 Data Quality Management and Metadata Strategies — 6 classes
› 3.1 Understanding Data Quality and Its Impact on Business Decisions
› 3.2 Identifying and Defining Data Quality Dimensions
› 3.3 Implementing Data Quality Assessment Techniques
› 3.4 Exploring Metadata and Its Role in Data Governance
› 3.5 Designing Effective Metadata Management Strategies
› 3.6 Applying Best Practices for Sustaining Data Quality
• 4 Data Stewardship and Organizational Roles in Governance — 6 classes
› 4.1 Understanding Data Stewardship Roles and Responsibilities
› 4.2 Exploring the Intersection of Data Governance and Business Operations
› 4.3 Identifying Key Organizational Roles in Data Governance
› 4.4 Developing Effective Communication Strategies for Data Stewards
› 4.5 Implementing Data Stewardship Frameworks in Business Environments
› 4.6 Evaluating the Impact of Organizational Roles on Data Governance Success
• 5 Implementing Data Governance in Business Intelligence Systems — 6 classes
› 5.1 Exploring the Role of Data Governance in BI Systems
› 5.2 Identifying Key Stakeholders and Their Responsibilities
› 5.3 Establishing Data Governance Frameworks for BI
› 5.4 Integrating Data Quality Processes in Business Intelligence
› 5.5 Implementing Data Privacy and Compliance in BI Systems
› 5.6 Evaluating Success and Continuous Improvement in Data Governance
2 Strategic Business Intelligence 5 chapters · 30 classes  85 marks
• 1 Understanding the Fundamentals of Strategic Business Intelligence — 6 classes
› 1.1 Defining Strategic Business Intelligence
› 1.2 Exploring the Key Components of Business Intelligence
› 1.3 Analyzing the Role of Data in Strategic Decision Making
› 1.4 Identifying Business Intelligence Tools and Technologies
› 1.5 Examining Case Studies in Successful Business Intelligence
› 1.6 Applying Strategic Insights to Business Scenarios
• 2 Data Collection and Integration for Business Intelligence — 6 classes
› 2.1 Understanding Data Sources in Business Intelligence
› 2.2 Exploring Data Collection Techniques
› 2.3 Analyzing Challenges in Data Integration
› 2.4 Implementing Data Integration Strategies
› 2.5 Assessing Data Quality for Business Insights
› 2.6 Leveraging Integrated Data for Strategic Decision Making
• 3 Advanced Data Analytics and Visualization Techniques — 6 classes
› 3.1 Understanding the Role of Advanced Analytics in Business Intelligence
› 3.2 Exploring Key Data Visualization Techniques
› 3.3 Leveraging Predictive Analytics for Strategic Decision Making
› 3.4 Implementing Data Visualization Tools and Software
› 3.5 Analyzing Complex Data Sets Using Machine Learning Algorithms
› 3.6 Integrating Visualization Techniques into Business Intelligence Reports
• 4 Strategic Implementation of Business Intelligence Systems — 6 classes
› 4.1 Understanding the Core Components of BI Systems
› 4.2 Identifying Business Needs and BI Solutions
› 4.3 Designing a Strategic BI Implementation Plan
› 4.4 Mapping Data Sources for Business Intelligence Integration
› 4.5 Implementing Data Governance Frameworks
› 4.6 Monitoring and Evaluating BI System Performance
• 5 Governance and Ethical Considerations in Business Intelligence — 6 classes
› 5.1 Exploring Data Governance Principles
› 5.2 Understanding Ethical Considerations in Business Intelligence
› 5.3 Assessing Legal and Compliance Issues in Data Management
› 5.4 Analyzing the Impact of Bias in Business Intelligence
› 5.5 Implementing Data Governance Frameworks Effectively
› 5.6 Evaluating Case Studies on Ethical Data Usage
3 Data Quality Management 5 chapters · 30 classes  65 marks
• 1 Understanding Data Quality Concepts and Dimensions — 6 classes
› 1.1 Introduction to Data Quality: Defining Key Concepts
› 1.2 Exploring Data Quality Dimensions: Framework and Importance
› 1.3 Analyzing Accuracy and Completeness: Core Data Quality Metrics
› 1.4 Evaluating Consistency and Timeliness: Ensuring Reliable Data
› 1.5 Assessing Validity and Uniqueness: Overcoming Common Challenges
› 1.6 Applying Data Quality Principles: Case Studies and Best Practices
• 2 Developing Data Quality Frameworks and Strategies — 6 classes
› 2.1 Understanding Data Quality: Key Concepts and Importance
› 2.2 Identifying Data Quality Dimensions and Indicators
› 2.3 Designing a Data Quality Framework: Essential Components
› 2.4 Implementing Data Quality Strategies: Best Practices
› 2.5 Monitoring and Measuring Data Quality: Tools and Techniques
› 2.6 Enhancing Data Quality: Continuous Improvement Approaches
• 3 Data Profiling and Cleansing Techniques — 6 classes
› 3.1 Understanding Data Profiling Basics
› 3.2 Identifying Data Patterns and Anomalies
› 3.3 Exploring Data Cleansing Techniques
› 3.4 Applying Data Transformation Methods
› 3.5 Ensuring Data Integrity and Consistency
› 3.6 Implementing Effective Data Quality Solutions
• 4 Data Quality Metrics and Improvement Plans — 6 classes
› 4.1 Understanding Data Quality Metrics: An Overview
› 4.2 Identifying Key Metrics: Ensuring Data Reliability
› 4.3 Measuring Data Accuracy: Techniques and Tools
› 4.4 Evaluating Completeness and Consistency in Data Sets
› 4.5 Developing Effective Data Quality Improvement Plans
› 4.6 Implementing and Monitoring Data Quality Interventions
• 5 Leveraging Technology and Tools for Data Quality Management — 6 classes
› 5.1 Understanding Data Quality Tools and Technologies
› 5.2 Exploring Data Profiling Techniques
› 5.3 Implementing Data Cleansing Processes
› 5.4 Automating Data Quality Monitoring
› 5.5 Integrating Data Quality Tools with Business Intelligence Systems
› 5.6 Evaluating and Selecting Data Quality Solutions
4 Leadership in IT 5 chapters · 30 classes  65 marks
• 1 Foundations of Leadership in IT: Building Effective Teams — 6 classes
› 1.1 Understanding Leadership Styles in IT Teams
› 1.2 Recognizing and Developing Key IT Team Roles
› 1.3 Fostering Communication and Collaboration in IT Environments
› 1.4 Building Trust and Accountability within IT Teams
› 1.5 Leveraging Diversity and Inclusion for Team Innovation
› 1.6 Implementing Strategies for Effective Team Performance
• 2 Navigating Change: Leadership in Rapidly Evolving IT Environments — 6 classes
› 2.1 Understanding the Dynamics of Change in IT
› 2.2 Analyzing the Impact of Technological Advancements on Leadership
› 2.3 Adapting Leadership Styles for Agile IT Environments
› 2.4 Fostering a Culture of Innovation and Flexibility
› 2.5 Communicating Change Effectively in Technical Teams
› 2.6 Implementing Strategies for Sustainable Change Leadership
• 3 Data-Driven Decision Making: Enhancing Leadership with BI Tools — 6 classes
› 3.1 Understanding BI Tools in Leadership
› 3.2 Exploring Data-Driven Decision Making
› 3.3 Identifying Key Metrics for Decision Making
› 3.4 Leveraging Data Analysis for Strategic Leadership
› 3.5 Integrating Business Intelligence with Leadership Practices
› 3.6 Applying Data-Driven Insights to Enhance Leadership Outcomes
• 4 Ethical Leadership in Data-Intensive IT Projects — 6 classes
› 4.1 Understanding Ethical Challenges in Data Projects
› 4.2 Identifying Stakeholder Roles and Responsibilities
› 4.3 Exploring Frameworks for Ethical Decision-Making
› 4.4 Analyzing Case Studies of Ethical Leadership in IT
› 4.5 Implementing Ethical Practices in Data Governance
› 4.6 Cultivating a Culture of Ethical Leadership in IT Teams
• 5 Strategic Leadership: Driving Organizational Success Through Data Governance Initiatives — 6 classes
› 5.1 Understanding the Role of Strategic Leadership in IT
› 5.2 Identifying Key Components of Data Governance Initiatives
› 5.3 Exploring Strategic Leadership Skills for Data-Driven Success
› 5.4 Analyzing Effective Data Governance Frameworks
› 5.5 Integrating Leadership Practices to Enhance Data Governance
› 5.6 Applying Strategic Leadership to Drive Organizational Change
5 Emerging Technologies 5 chapters · 30 classes  65 marks
• 1 Foundational Concepts in Emerging Technologies — 6 classes
› 1.1 Understanding Emerging Technologies: An Introduction
› 1.2 Analyzing Key Characteristics of Emerging Technologies
› 1.3 Exploring Disruptive Innovations in Emerging Tech
› 1.4 Assessing the Impact of Emerging Technologies on Business
› 1.5 Evaluating Ethical Considerations in Emerging Technologies
› 1.6 Applying Emerging Technologies to Business Intelligence Strategies
• 2 Technological Innovations Shaping Data Governance — 6 classes
› 2.1 Exploring Key Technological Innovations in Data Governance
› 2.2 Understanding Blockchain's Role in Data Integrity
› 2.3 Utilizing Artificial Intelligence for Improved Data Analysis
› 2.4 Assessing the Impact of Cloud Computing on Data Management
› 2.5 Leveraging the Internet of Things for Enhanced Data Collection
› 2.6 Implementing Machine Learning in Business Intelligence Processes
• 3 Artificial Intelligence in Business Intelligence — 6 classes
› 3.1 Understanding the Role of AI in Business Intelligence
› 3.2 Exploring AI Techniques for Data Analysis
› 3.3 Integrating AI with BI Tools
› 3.4 Enhancing Data Visualization with AI
› 3.5 Leveraging AI for Predictive Analytics in BI
› 3.6 Implementing AI-Driven Decision Support Systems
• 4 Ethical and Legal Considerations of Emerging Technologies — 6 classes
› 4.1 Understanding Ethical Frameworks for Emerging Technologies
› 4.2 Exploring Legal Challenges in New Tech Development
› 4.3 Analyzing Privacy Concerns in Emerging Technologies
› 4.4 Evaluating the Impact of AI on Ethical Decision-Making
› 4.5 Assessing Regulatory Approaches to Digital Innovations
› 4.6 Applying Ethical Guidelines to Tech Implementation
• 5 Case Studies and Future Directions in Emerging Technologies — 6 classes
› 5.1 Exploring Breakthrough Technologies in Business
› 5.2 Analyzing Real-World Case Studies of AI Integration
› 5.3 Identifying Key Challenges in Blockchain Adoption
› 5.4 Assessing the Impact of IoT on Business Operations
› 5.5 Predicting Future Trends in Emerging Technologies
› 5.6 Developing Strategic Roadmaps for Technology Evolution
6 Organisational Change Management 5 chapters · 30 classes  65 marks
• 1 Understanding Organisational Change in Data-Driven Environments — 6 classes
› 1.1 Exploring the Need for Change in Data-Driven Organisations
› 1.2 Identifying Key Drivers of Organisational Change in Data Environments
› 1.3 Analyzing Resistance to Change in Data Governance
› 1.4 Mapping Stakeholder Roles in Data-Driven Change Processes
› 1.5 Designing Effective Communication Strategies for Change
› 1.6 Implementing and Monitoring Change Initiatives in Data Governance
• 2 Assessing Organisational Readiness for Data Initiatives — 6 classes
› 2.1 Understanding Organisational Culture for Data Initiatives
› 2.2 Evaluating Current Data Management Practices
› 2.3 Identifying Key Stakeholders and Their Roles
› 2.4 Analyzing Capability Gaps in the Workforce
› 2.5 Assessing Technological Infrastructure Readiness
› 2.6 Developing a Readiness Action Plan
• 3 Strategic Planning for Data Governance Change — 6 classes
› 3.1 Understanding the Need for Strategic Planning in Data Governance
› 3.2 Identifying Key Stakeholders in Data Governance Initiatives
› 3.3 Analyzing Current Data Governance Frameworks
› 3.4 Setting Objectives and Goals for Data Governance Change
› 3.5 Developing a Strategic Roadmap for Data Governance Implementation
› 3.6 Evaluating and Adapting the Data Governance Strategy
• 4 Implementing Change: Best Practices and Tools — 6 classes
› 4.1 Understanding Organisational Change Principles
› 4.2 Assessing Change Readiness in Your Organisation
› 4.3 Designing an Effective Change Management Plan
› 4.4 Communicating Change Effectively Across the Organisation
› 4.5 Managing Stakeholder Engagement and Resistance
› 4.6 Evaluating Change Outcomes Using Key Metrics
• 5 Evaluating and Sustaining Change in Data Governance — 6 classes
› 5.1 Understanding the Principles of Evaluating Change
› 5.2 Identifying Key Metrics for Data Governance Assessment
› 5.3 Analyzing Stakeholder Feedback and Engagement
› 5.4 Applying Continuous Improvement Strategies in Data Governance
› 5.5 Ensuring Long-term Sustainability of Change Initiatives
› 5.6 Leveraging Technology for Sustaining Data Governance Changes
LAPT — 85 Great Portland Street, W1W 7LT, London, United Kingdom
info@lapt.org  •  +44 7513 283044  •  lapt.org		 UKPRN 10067351  •  Company 4984798
Curriculum current at the date of issue.