Professional Data Management and Business Intelligence

Introduction:

The "Professional Data Management and Business Intelligence" course is designed to equip participants with the skills and knowledge required to manage data effectively and leverage business intelligence tools. This course covers the latest technologies and best practices in data management, focusing on enhancing decision-making and operational efficiency.

Objectives: By the end of this course, participants will be able to:

• Understand the fundamentals of data management and business intelligence.
• Develop strategies for data management and business intelligence planning.
• Utilize new technologies and automation to enhance data management.
• Implement risk management and quality management practices in data handling.
• Analyze and interpret big data for strategic decision-making.

Target Audience:

• Data managers
• Business intelligence analysts
• IT professionals
• Facility operations managers
• Anyone involved in data management and analysis

Course Schedule

Day 1: Introduction to Data Management and Business Intelligence

1. Overview of data management and business intelligence
2. Importance of data in modern business operations
3. Key components of data management systems
4. Role of business intelligence in decision-making
5. Case studies on successful data management implementations

Day 2: Data Management and Business Intelligence Planning

1. Developing data management strategies specific to facility operations
2. Business intelligence planning for enhanced performance
3. Identifying key performance indicators and metrics
4. Integration of data management with business processes
5. Practical exercises in data management planning

Day 3: New Technologies and Automation in Data Management

1. Introduction to new technologies in data management
2. Automation tools and techniques for data handling
3. Leveraging AI and machine learning in data management
4. Case studies on technology-driven data management
5. Practical exercises in using automation tools

Day 4: Risk and Quality Management in Data Management

1. Identifying and managing data-related risks
2. Implementing quality management practices in data handling
3. Ensuring data accuracy and consistency
4. Compliance with data protection regulations
5. Practical exercises in risk and quality management

Day 5: Big Data, Ethics, and Security in Data Management

1. Introduction to big data and its applications
2. Data integration and warehousing techniques
3. Ensuring data security and ethical handling
4. Case studies on big data analytics
5. Final course review and Q&A