data & ai
Data & AI focuses on creating a robust, scalable, and secure data infrastructure that lays the foundation for intelligent business decisions. From customised cloud data platforms to efficient data migration management, everything is focused on turning data into actionable insights. A special focus is on Data Architecture, which includes data security and agile data models. Likewise, the benefits of self-service BI for organisational development will be highlighted. In artificial intelligence, the focus is on integrating technical and business models that enable the use of AI in various business areas. The requirements of analytics, and traditional and generative AI determine the architecture and lifecycle management of current and future data landscapes.
Cloud Data Platforms
- Data Platform / Data Lake / Lakehouse: Tailored data organization concepts and logical data architectures
- Technical concepts and business models: Building data-oriented cloud platforms, including best practices
- Efficient building of enterprise-wide data architectures using leading cloud solutions such as Snowflake, Databricks, or Azure
Cloud Data Migration
- Modeling, automation, lifecycle management: data preservation and transformation structures tailored to your process chains
Data Architecture
- Data Platform / Data Lake / Lakehouse: An architecture for performance and data security
- Modeling, Automation, Lifecycle Management: Modeling as an element of data architecture
Self-Service BI
- Self-Service BI Concepts and Processes: Expectation management, usage concepts, and organisational development
Artificial Intelligence
- Technical concepts and domain-oriented models: including AI aspects could be relevant here, even if it is not explicitly mentioned
- Generative AI: enhancing the digital experience, data architecture to support AI, connecting leading LLMs (OpenAI, Llama, etc.)
Model Development
- Data Analysis & Preprocessing: use of modern algorithms for data preparation and analysis as a basis for robust models
- Model Development: Machine learning and statistical methods application for precise predictions and insights
- Automated workflows: Integration of pipelines for continuous model improvement and rapid implementation
- Lifecycle management: monitoring model performance and automated updates to maintain accuracy
- Scalable architectures: Use of cloud and on-premise solutions for maximum flexibility and scalability
