Data governance, data management and the use of AI

Introduction to data and data-driven decision-making

• Overview of the importance of data in modern companies.
• Data-driven decision-making and the role of AI.
• Challenges in relation to data in the company.

Data governance - the foundation of the data strategy

• Concepts and principles of data governance.
• The role of data governance in the implementation of AI initiatives.
• Development of a rough data governance framework for your own company.

Data Management - Operational management of data

• Core components of data management: data collection, storage, processing and maintenance.
• Best practices and challenges in data management, especially with regard to AI applications.
• Data quality assessment and cleansing.
• How can data management increase the effectiveness of AI projects?

AI and its role in data governance and data management

• Overview of AI tools and methods to support data governance and data management.
• Use of AI tools for data cleansing and analysis.
• How AI is changing the requirements for data governance and management.

Business Processes - Effective design of business processes

• Introduction to the design and optimization of business processes in the context of data.
• Challenges in integrating AI into existing business processes.
• Analysis of a company that has successfully adapted its business processes to a data-driven strategy.
• Analysis and improvement of a business process taking into account data and AI requirements.

Data architecture - designing a robust data infrastructure

• Introduction to the principles of data architecture.
• The importance of a solid data architecture for the success of AI projects.
• Design of a simple data architecture.
• Challenges in the implementation of a data architecture.

AI-specific challenges and solutions in business processes and data architecture

• Specific requirements of AI for business processes and data architectures.
• Analysis of a company that has successfully integrated AI into its data architecture.
• Draft a plan for integrating AI into your company's business processes and data architecture.

Roadmap for the implementation of data-driven projects

• Step-by-step guide to developing a roadmap for data-driven projects, including AI implementations.
• Definition of the internal and external resources required.