Data governance
Transparency instead of a blind flight through data silos
Understanding how your data really flows
In many organizations, the data landscape has grown historically. Pipelines are stable, reports provide figures, and yet it is difficult to clearly identify dependencies. Field origins are unclear, KPI logics are stuck in the code, and changes in one place have unexpected effects elsewhere.
This leads less to technical problems than to operational uncertainty. What is often missing is a common, robust understanding of how data silos can be overcome, where data comes from, how it is changed and what it may be used for.
We support teams in establishing data lineage and governance as an integral part of their data architecture – whether based on existing structures or fundamentally redesigned, even while retaining existing tools.
Data lineage as part of the architecture
We understand data lineage not as a retrospective description, but as an architectural element. The aim is to make data flows visible from end to end – from the source through transformations to use in analytics, reporting or operational processes.
We consider both technical dependencies and functional meanings. Tables, fields and pipelines are enriched with context so that it becomes clear what role they play in the overall system. This creates a transparent basis for responding to regulatory requirements and Lineage also becomes the foundation for operation, further development and collaboration between teams.
Governance that doesn't slow users down
Governance only works if it fits into everyday working life. That is why we rely on tailor-made rules, clear responsibilities and a close link to existing workflows.
The focus is on:
- Clearly defined data ownership
- Comprehensible quality and release rules
- Governance where data is generated and used
- Collaboration
The goal is not control for the sake of control, but reliability and maintainability.
Traceable analytics instead of black box KPIs
At the latest in reporting, it becomes clear whether transparency really exists. If key figures can no longer be explained or have to be reinterpreted with every query, trust is lost. Both in specialist departments and within the data teams.
By linking lineage, metadata and technical definitions, we ensure that KPIs remain comprehensible. Origin, calculation logic and dependencies are visible, which reduces reconciliations and makes analyses more stable.
Open instead of tool-driven
We work independently of platforms and manufacturers. For us, governance does not come from an additional tool, but from clean architecture, metadata and responsibilities.
We rely on open source and hybrid environments in particular:
Integration instead of parallel worlds
Expandability instead of lock-in
Governance as a capability,
not as a product
For teams with responsibility for data
Our approach is aimed at data engineers, analytics engineers, BI teams and platform managers who not only want to operate functioning pipelines, but also consciously design their data landscape. Wherever traceability, maintainability and clean further development are important, Data Lineage unfolds its full potential in combination with governance their added value.
Implement data governance with a data catalog now
DataHub is an open source platform for modern metadata management and data governance. It enables the tracking of data lineage, the profiling of data sets and the definition of data contracts contracts.