Data strategy: setting direction instead of just collecting data
At the strategic level, data strategy answers the question:
What do we use data for and where should our company develop with regard to data?
A data strategy is the holistic framework for handling data within the company designed for the medium to long term and closely interlinked with corporate, IT and AI strategy. Typical components include:
- Vision & mission: what contribution should data make to value creation?
Example: “We make all significant management decisions on a fact-based basis.”
- Strategic objectives (target picture): how will we recognise in 3–5 years that we have become “data mature”?
- Strategic measures: which levers do we apply, for example data platform, roles, policies, AI guardrails?
- Roadmap: in what order do we proceed, for example pilot areas, scaling, organisation, technology?
Data Governance: the rules of the game and responsibilities
At the methodological level, Data Governance ensures that the strategy does not remain in a vacuum. It defines roles, rules and processes by which data are governed similar to the way corporate governance governs the company as a whole.
Key questions of Data Governance:
- Who is responsible for what, for example Data Owner, Data Stewards, Chief Data Officer (CDO), Data Governance Board?
- According to which principles and standards do we handle data, for example definitions, quality requirements, classifications?
- Which processes apply, for example approval of new data uses, handling of data quality issues, approval of AI use cases?
- How is compliance with these rules reviewed, for example through KPI-based monitoring?
The Three Lines of Defence model also helps here to clearly structure the different roles and responsibilities:
- 1st Line: specialist departments and IT use and generate data, operational implementation.
- 2nd Line: functions such as Data Governance, data protection, information security and compliance define the framework, provide guidance, for example through policies or playbooks, and monitor compliance, audits.
- 3rd Line: internal audit independently reviews effectiveness.
In short: Setting and leading the strategic direction means steering; steering requires prioritisation; prioritisation requires structure and Data Governance provides exactly this structure.
Data Management: implementation in day-to-day operations
At the operational level lies Data Management. This is where the requirements from data strategy and Data Governance are translated into concrete activities, systems and processes.
Definition:
Data Management is the planning, organisation, implementation and control of processes and measures aimed at efficiently managing data throughout their entire lifecycle and using them to create added value.
In practice, this means that data are collected, integrated, stored, secured, used, analysed and deleted again when they are no longer needed. Specialist departments, IT, data engineers, analysts, BI teams and AI teams typically work together cross-functionally here.
Data Capabilities: what good Data Management requires
For Data Management to be effective, certain capability areas are needed, known as Data Capabilities. In practice, the following areas, among others, have proven useful:
- Data Governance: definition of roles, processes and rules.
- Data Regulation & Compliance, including data ethics: ensuring compliance with legal requirements, such as the GDPR, Data Act and AI Act.
- Data Architecture: structure of the data landscape, platforms, interfaces.
- Data Modeling & Design: modelling of central data structures.
- Data Storage & Operations: storage and operation of databases and platforms.
- Data / Information Security & Risk: protection against loss, misuse and attacks.
- Data Integration & Interoperability: combining different data sources.
- Document & Content Management: management of unstructured information.
- Reference & Master Data Management: maintenance of central master data as the “single source of truth”.
- Data Warehousing & Business Intelligence: creation of reports, dashboards and analyses.
- Metadata Management: management of data about data, for example through data catalogues.
- Data Quality: ensuring correct, complete and up-to-date data.
- Data Products & Monetisation: development of data-based products and value creation.
- Data Culture & Data Literacy: building data competence and a culture of data use.
This is precisely where the importance of change management becomes apparent: employees need to understand why ways of working are changing, what advantages data-based decisions have and how they can use new tools sensibly. Training, clear communication and visible successes, for example fewer manual Excel analyses, faster decisions and greater transparency, are decisive.
Conclusion: three terms, one goal
Data strategy sets the direction,
Data Governance defines the rules of the game,
Data Management ensures implementation in everyday work.
Companies that clearly separate these three levels, while also coordinating them well, create the foundation for an organisation that does not merely collect data, but truly uses it strategically: to reduce costs, save time and increase revenue.
Everything else, from concrete approaches such as Maturity Assessments and roadmaps to Data Operating Models, Data Mesh or AI Governance, builds precisely on these foundations. And that is exactly where the exciting part of the journey begins.
Better decisions. Less gut feeling. With ISiCO to your data strategy.
This is how we support you with your data strategy:
Maturity Assessment
- Maturity measurement, Data Capability Map and gap analysis as part of a workshop.
- Derivation of measures and definition of milestones.
- Creation of a roadmap.
- Increasing data quality, for example for AI, and reducing barriers to innovation.
- Enablement of AI use cases.
Governance
- Preparation of guidance and policies.
- Definition of roles and responsibilities.
- Stopping diffusion of responsibility.
- Training and awareness programmes.
Regulatory Mapping
- Holistic view of the interfaces between the GDPR, AI Act and Data Act.
- Establishment of an AI inventory based on the ROPA / record of processing activities.
- Conducting DPIAs and fundamental rights impact assessments.
- Leveraging synergies and implementing compliance requirements in a practice-oriented way.