Data Governance
Share data with clear ownership.
Who owns a metric definition, approves access or resolves a data issue? Unclear answers slow projects and weaken decisions. We work with you to establish practical rules for ownership, definitions, use and quality. Data governance becomes part of everyday collaboration and supports analytics, applications and AI.
Your options
Services that move your project forward
Select business goals and an initial data domain
We start with a specific problem and the data that goes with it. Teams involved, users, and decision-making paths determine the first scope.
The starting point has a clear benefit and a manageable scope.
Describe roles and decisions
We distinguish business ownership, ongoing maintenance, technical delivery and approvals. Data owners and data stewards receive specific tasks and defined decision paths.
People can apply their responsibilities to everyday decisions.
Make terms and data understandable
An agreed glossary connects important terms to definitions, sources and contacts. A data catalog is added where it helps people discover and maintain relevant information.
Teams can find suitable data and understand what it means.
Regulate usage and access
Requests, approvals, technical rights and regular inspections are linked together. The need for protection and permitted use are incorporated into clear rules.
Data access follows a documented, verifiable process.
Embed quality and change
Quality requirements, error handling and changes to data products are given responsibilities. Effects on users and dependent applications are taken into account at an early stage.
People know how to address issues and changes.
Test the approach and plan expansion
We test roles and procedures on realistic tasks. Experience, turnaround times and open decisions show which rules should be improved or transferred to other areas.
Governance develops through tested working practices.
Where to start
Data Governance Use cases
Three example situations show how we can help.
Data requests are stuck between multiple departments
We establish ownership and a clear process from a data request to delivery.
New analytics and AI projects use unclear data sets
Definitions, responsibility, and permissible use are clarified together for a prioritized data area.
A data catalog exists, but is not maintained
We connect maintenance tasks to named roles, relevant changes and a clear benefit for users.
From requirements to results
A clear process with agreed milestones
Understand the problem and existing ownership
We capture an appropriate data area, stakeholders, and existing rules.
Design roles and procedures
Terms, approvals, quality and decision-making processes are specified with those responsible.
Test real tasks
Data requests, changes and quality issues are used to test whether the procedures are clear and practical.
Plan maintenance and the next domains
Results, those responsible and a prioritized expansion are documented.
Your benefit
What you receive
- A coordinated governance model for the agreed data area.
- Specific roles, responsibilities and documented decision paths.
- A usable glossary and agreed procedures for access, quality and changes.
- Results of the pilot, maintenance tasks and a prioritized roadmap.
Ways to work with us
Choose a starting point that fits your needs. We agree the scope and required effort in a tailored proposal.
Governance Assessment
For orientation: current problems, responsibilities and a suitable first data area.
Request a quote: Governance AssessmentImplement a governance pilot
For practical collaboration: test roles, definitions and procedures on a real data product.
Request a quote: Implement a governance pilotExpand governance to other areas
For broader use: reusable rules, integrated tools and maintained ownership.
Request a quote: Expand governance to other areasSYNEDAT PLATFORM
Platform experience for your project
We use these selected tools in SYNEDAT PLATFORM or its delivery processes. We adapt suitable practices to your project and align their integration with your existing systems.
Deployment and platform automation
Kubernetes · Azure Kubernetes Service · Helm · Argo CD · Terraform
Versioned configuration and declarative deployment connect infrastructure and applications. GitOps makes proposed changes reviewable and the desired state explicit. Operational transitions and recovery procedures are still planned for the specific application.
Repeatable changes and clearer responsibility boundaries.
Observability and operations
Prometheus · Grafana · Alloy · Loki · Tempo
Metrics, logs and traces provide different views of applications and platforms. We organize data sources, dashboards and alert paths around specific operating questions. Retention, sensitive data and costs are considered when planning data collection.
Better incident diagnosis and informed operating decisions.
Data, search and messaging
MySQL · Redis · RabbitMQ · Solr · OpenSearch
Data storage, caching, messaging and search have different consistency, access and recovery requirements. We select and connect these components around your business processes. Ownership and maintenance are part of the integration.
Technical components that fit your data and application processes.
Questions before you get started
Is data governance primarily an IT project?
Technology supports implementation, but business ownership must come from the teams that understand and use the data. Definitions, permitted use and priorities require their decisions. We connect those decisions with IT capabilities and existing company policies.
Do we need a company-wide data catalog first?
A catalog can help, but it should be able to meet a specific need and be maintained. A limited data area with clear roles and a few important terms is often a suitable place to start. The tool decision follows the required tasks.
What is the difference between data owner and data steward?
A data owner typically takes responsibility for business decisions and priorities within a data domain. A data steward supports the ongoing maintenance of definitions, quality rules and procedures. The specific division of duties is adapted to your organization and available capacity.
How do we avoid additional bureaucracy?
Rules are geared to real decisions and recurring tasks. In the pilot, we check whether this makes requests and changes more understandable and easier to process. Unnecessary approvals and unclear responsibilities are specifically questioned.
Can an existing governance tool continue to be used?
Yes. We check existing catalogs, roles, metadata, and integrations against the required procedures. Tools such as Microsoft Purview can support appropriate functionality. However, configuration and product-related rights must match the actual organizational model.
How are governance and data protection related?
Governance organizes data ownership, use and traceability. For personal information, we involve the responsible privacy functions and follow existing requirements. Business and legal decisions are translated into technical and organizational procedures.
Does data governance also help with AI applications?
Clear ownership, origins, quality and permitted uses help prepare suitable data. An AI project also requires model- and application-specific assessments. We connect the data foundations with those additional requirements.
How do we recognize progress?
Useful indicators include named owners, maintained definitions, manageable quality issues and clear access workflows. Metrics follow the original problem, such as unresolved decisions or request turnaround times. The number of documents produced alone does not establish value.
Discuss your next step
Where do your data projects lack a clear decision today?
Describe a typical conflict or a faltering data request. We use this to develop a practical entry point into your data governance.
Data Governance
Your next step
Tell us what you need. We will route your enquiry to the right team and discuss the next steps with you.
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