Strategic data competence for decision makers

Developing a modern data strategy

• Design a data strategy framework: Define the vision, operating model, processes, architecture, and technologies.
• Use maturity models to set priorities.
• Promote a data culture and build data literacy in a targeted manner.
• Integrate AI meaningfully into your data strategy.

Data governance – Rules & Responsibilities

• Define clear roles, responsibilities, and basic standards for handling data.
• Establish governance as a framework condition for data quality and the integration of AI.

Data Management – Strategic data qualityyt

• Define KPIs, key dimensions, and establish effective monitoring.
• Implement a clear operating model.
• Standardize and harmonize data structures.
• Use AI to improve data quality.

Process strategy – Think end-to-end

• Optimize data quality through clear process design.
• Develop E2E process design across all levels (Levels 1–5).
• Define process roles and integrate governance.
• Use process harmonization as a strategic lever.

Data architecture strategy – the technical Rübackbone

• Plan a target architecture based on principles such as API-first, clean core, and domain-driven design.
• Design data flows, mappings, and models.
• Implement a platform strategy: Utilize data lakes, DWH, streaming, and AI.
• Define architecture roles and an effective operating model.

AI strategy – Scaling responsibly and effectively

• Use AI specifically for governance, data management, and process optimization.
• Develop guidelines to minimize risks and clarify responsibilities.
• Scale AI based on its maturity level.
• Put ethics, transparency, and validation at the heart of your AI strategy.

Roadmap & Implementation

• Prioritize measures: Focus on quick wins and structural levers.
• Establish a KPI framework to measure progress.
• Organize roles and resources effectively.
• Use a data and AI board as a management tool.
• Develop your individual 90-day strategic plan.