Digital reporting: data management - analysis techniques - visualization

The Basics of Successful Digital Reporting

• Development of BI, AI and big data skills.
• Requirements for modern corporate reporting.
• The interplay of data, processes, and visualization.
• The Importance of Data Quality and Consistency for Business Management.
• The Role of Standardization and Governance in Reporting.

Data Management and Data Architecture

• Selection, integration, and harmonization of various data sources.
• Development of a consistent data model (e.g., star schema, data warehouse).
• ETL/ELT processes for data preparation and delivery.
• Architectural approaches: data warehouse, data lake, data marts, and hybrid models.
• The importance of metadata and semantic layers.
• Hands-on exercises in data modeling and AI-assisted data exploration.

Data Quality and Control Logic

• Ensuring data quality and consistency.
• Definition and standardization of key performance indicators (KPIs).
• Handling data inconsistencies and data gaps.
• Establishing a reliable set of data as the basis for analysis.

Analytical Techniques in Reporting

• Standard reporting, ad hoc analyses, and OLAP.
• Time series analysis and deviation analysis.
• The fundamentals of advanced analytics and their role in the context of reporting.
• Classification of AI-based methods as a complementary analytical option.

Report Visualization and Design

• Basic principles of effective data visualization (e.g., IBCS-based).
• Designing clear, easy-to-understand, and action-oriented reports.
• Creating dashboards for different target audiences.
• Avoiding common mistakes in visualization (information design).
• Using data to tell stories that support management decisions.
• AI-powered dashboard creation.

Automation and Further Development of Reporting

• Automation of reporting processes.
• Self-service BI and decentralized data usage.
• Use of modern tools for reporting and analysis.
• Integration of AI to support automation and analysis (e.g., anomaly detection, text generation).
• Limitations and prerequisites for the effective use of AI in reporting.