Digital reporting: data management - analysis techniques - visualization

Prerequisites and requirements for the use of AI, business intelligence and big data in the company

• IT landscape, architecture and infrastructure in the age of AI.
• Organization and automation of reporting processes.
• Development of BI, AI and big data skills.

Design variants of data supply for corporate management

• Selection of suitable data sources and their integration.
• Intelligent extraction, processing and consolidation of raw information.
• Quality assurance and automatic quality assessment using AI methods.
• Modern architectural approaches: Enterprise data warehouse, data lakes, data mart architectures and hybrid solutions.
• Use of metadata with centralized, decentralized and federated AI approaches.
• Optimization of ETL/ELT processes through AI-driven automation.
• Practical exercises on AI-supported data cleansing, pattern recognition and predictive analytics.

Design variants of AI-supported data analysis and visualization

• Mapping and control of relevant KPIs using intelligent analysis methods.
• AI-based dashboards for dynamic and adaptive data visualization.
• Standard reporting, ad-hoc analyses and OLAP analyses with integrated AI.
• Visual analytics and explorative, algorithmic data analysis with AI.
• Identification and analysis of trends and development patterns using AI methods.