What an analytical data warehouse is
A data warehouse organises information from different sources to support queries and analysis. It can retain history and indicator-oriented structures. It differs from the operational system where daily actions are recorded. It is not simply a folder of exports: it needs a model enabling data to be related and their meaning understood.
What it can bring together in OSH
A project can combine exposures, actions, training, workplaces and other necessary variables within the authorised scope. Purposes and responsibilities are defined before bringing them together. OSH data analytics requires comparable denominators and periods; adding records from different systems does not guarantee an interpretable indicator.
Definitions and master data
Workplaces, jobs and people must be linked with consistent identifiers. How structural changes are handled and which version corresponds to each period are also decided. If a workplace changes its name, analysis should not automatically interpret it as a new entity. Model documentation helps explain why two reports display different figures.
Updates and quality
Loading needs to control errors, duplicates and pending data. Users should know the update date and period covered. A dashboard updated nightly does not necessarily represent the situation of the last minute. Validation and reconciliation rules are established against sources, preventing an attractive presentation from concealing an incomplete load.
Comparison example
In a hypothetical example, two workplaces record training with different codes for the same course. The analytical warehouse maintains the mapping and retains source data to review discrepancies. The report distinguishes trained people from enrolments and shows the period used. This preparation enables comparison without confusing repeated courses with worker coverage.
Access and scope
Centralising information can increase risk if health data are copied unnecessarily. Permissions and levels of detail are defined for each purpose, together with retention and traceability. A data warehouse neither removes source systems’ responsibilities nor decides which preventive measures are appropriate; it provides a basis that must be interpreted with technical judgement.
