Data Governance Blueprint
Visualizes roles, processes, and responsibilities along the data lifecycle.
Data Governance: The foundation for transparency and control
Without clear rules, responsibilities, and transparency, your data landscape remains underutilized – and risks increase. With a strong Data Governance framework, you ensure data quality, compliance, and trust.
This is how we make Data Governance tangible, scalable, and effective – across all areas of your organization
Our strength lies in combining strategic consulting, technological expertise, and hands-on implementation. We support companies from the initial analysis to the sustainable anchoring of governance structures – with a clear focus on business value and user adoption.
Our methodology:
- Modular & scalable: From quick assessments to company-wide governance initiatives.
- Change-oriented: Governance only works when people are on board – we involve stakeholders early and foster data literacy.
Our tools and assets:
Data Lineage Map
Shows how data flows through systems and processes – for maximum transparency and audit security.
Data Catalog Starter Kit
A tool-agnostic template for introducing a company-wide data catalog.
Governance KPI Dashboard
Measurable success factors for governance initiatives – from data quality to user adoption.
Next Steps : Get started now: Data Governance Kick-Off Workshop
In this workshop, we work with you to determine what form of Data Governance your company truly needs – not as a rigid rulebook, but as a foundation for trust, reusability, and real value creation. Together, we develop a target vision and define the first steps to implement Governance effectively and pragmatically within your organization.
Has your company already established governance structures?
Whether you’re just getting started, looking to professionalize your structures, or aiming for the next level of maturity – we have the right format for you.
With our Governance KPI Dashboard, you can sustainably manage and measure your governance initiatives — ideal for organizations seeking to operationalize and scale their governance.
Our Governance Framework & Role Model helps you establish governance structures that last. It’s designed for companies facing increasing data complexity and growing compliance requirements.
Without Data Governance, you lose control and competitiveness
Companies that fail to establish effective Data Governance risk operational inefficiencies and financial disadvantages. Without clear responsibilities and transparent data flows, data quality declines — directly affecting reporting, forecasting, and strategic decision-making. Projects in Business Intelligence, AI, or automation often fail because the underlying data is unreliable.
Even more critical is the loss of competitiveness: companies without governance cannot react quickly and data-driven to market changes. They lack transparency over customer, production, and financial data — making it impossible to make informed investment decisions or develop new business models. While competitors with a Data Governance framework launch data-driven products, predictive services, or automated controls, others remain flying blind. Silos and missing data integration slow down processes, stifle innovation, and leave opportunities in AI and data-based services untapped.
Regulatory risks also increase. Without documented data flows and clear responsibilities, companies face fines, reputational damage, and a loss of trust among customers and partners.
Those who fail to act now risk losing not only control over their data — but also over their organization’s steerability, innovative strength, and long-term viability.
Ready to take the next step?
Your Contact
FAQs on Implementing Data Governance
Ideally, IT, the relevant business units, data protection, compliance, and management should all be involved. Governance only works when all relevant stakeholders take responsibility.
A typical project consists of four phases:
- Analysis of the data landscape
- Definition of roles, rules, and processes
- Selection and implementation of suitable tools (e.g., Data Catalog)
- Training, rollout, and continuous optimization
Data Governance defines who can do what with data and why — the rules, roles, and responsibilities.
Data Management describes how data is technically processed, stored, and maintained.
4. Is a Data Catalog absolutely necessary?
Answer:
No, a Data Catalog isn’t strictly required — but in practice, it’s almost always highly beneficial. We speak from experience.
A Data Catalog is not an end in itself, but an enabler of transparency, efficiency, and scalability in data management. Especially in organizations with many data sources, complex processes, or multiple stakeholders, a lack of central visibility quickly leads to confusion.
Here are some considerations for context:
When is a Data Catalog indispensable?
- Many data sources & systems: When data must be consolidated from ERP, CRM, BI, Data Lake, etc.
- Unclear responsibilities: When no one knows exactly who owns which data.
- Recurring questions: “What does this KPI mean?”, “Where does this value come from?”, “Who uses this data?”
- Regulatory requirements: GDPR, ISO 27001, BAIT, and similar frameworks often require documented data flows and responsibilities.
- Scaling analytics & AI: Without a documented data foundation, every new project becomes a blind flight.
When can you (still) get by without a Data Catalog?
- Small organizations with few data sources and clear responsibilities
- Early project phases where roles, processes, and data strategy are still being defined
- Proof-of-concepts where governance will be operationalized at a later stage
No, a Data Catalog isn’t strictly required — but in practice, it’s almost always highly beneficial. We speak from experience.
A Data Catalog is not an end in itself, but an enabler of transparency, efficiency, and scalability in data management. Especially in organizations with many data sources, complex processes, or multiple stakeholders, a lack of central visibility quickly leads to confusion.
Here are some considerations for context:
When is a Data Catalog indispensable?
- Many data sources & systems: When data must be consolidated from ERP, CRM, BI, Data Lake, etc.
- Unclear responsibilities: When no one knows exactly who owns which data.
- Recurring questions: “What does this KPI mean?”, “Where does this value come from?”, “Who uses this data?”
- Regulatory requirements: GDPR, ISO 27001, BAIT, and similar frameworks often require documented data flows and responsibilities.
- Scaling analytics & AI: Without a documented data foundation, every new project becomes a blind flight.
When can you (still) get by without a Data Catalog?
- Small organizations with few data sources and clear responsibilities
- Early project phases where roles, processes, and data strategy are still being defined
- Proof-of-concepts where governance will be operationalized at a later stage