Digital reporting: data management - analysis techniques - visualization
Efficient automation and intelligent data visualization
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Machine Learning Engineer
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.
In your online learning environment, you will find useful information, downloads and extra services for this training course once you have registered.
Through numerous case studies and exercises, you'll learn in a practical way,
- how to build a stable and consistent data foundation for your reporting,
- how to systematically develop data models and key performance indicator systems,
- how to design and automate reporting processes efficiently,
- how to create informative and easy-to-understand reports and dashboards,
- how you realistically assess the use of AI and apply it strategically where it adds value.
Optionally, you can take an online exam and, depending on your score, receive a certificate in addition to your certificate of completion.
Practice-oriented lecture, case studies and exercises on the PC, discussion and optional e-exam.
After successfully completing the training , you can take an optional e-exam to obtain an additional certificate in addition to your confirmation of participation. The e-exam is an online-based exam on your PC and lasts 60 minutes. You can take the exam in your own familiar environment at a time of your choosing. The exam is based on single or multiple choice questions. Once you have completed the exam, you will immediately be shown whether you have passed or failed. Once you have successfully passed the e-exam, you will receive a certificate based on your exam result.
controllers to establish a BICC (Business Intelligence Competence Center) or Analytics CC, business intelligence analysts, financial analysts, data scientists, subject matter experts and executives, as well as those responsible for reporting and digital controlling processes.
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