The registry behind every module: one row, one owner, one history.
Data Manager holds the datasets the suite runs on – registries, references, and transaction tables – with import validation, row-level permissions, and an audit trail per change.
Every operational suite eventually meets the same question: who changed this row, and on whose authority. Data Manager answers it by treating the dataset as the unit – with columns, filters, permissions, and audit – instead of scattering tables across modules.
Imports are validated before they touch the data, which is the difference between a bad spreadsheet and a bad week.
- RowAudit trail per change, with actor and time
- ValidImport checked before it writes
- ScopePermissions per dataset and per team
- ExportGoverned export with a log entry
A dataset is a definition, not just a table.
Each dataset declares its columns, filters, search fields, and which module owns it. Views are read-only where they should be, and bulk actions are enabled by explicit decision rather than by default – which is exactly what an audit reviewer wants to hear.
- Column definitions with type, width, and searchability
- Saved filters per team
- Read-only views for audit and CDR tables
- Row selection and bulk actions controlled per dataset
Validate first, write second.
Imports are staged: required columns, formats, duplicates, and reference lookups are checked before any row is written. The report names the failing row numbers, so fixing the file is a task instead of a search.
- Required column and format validation
- Duplicate and reference checks before insert
- Row-level error report with line numbers
- Re-runnable import without doubling the data
Who changed what, and when.
Changes are recorded with the actor, the before and after value, and the time. Exports are logged too, because in a regulated environment the question is often about reading, not writing.
- Per-column change history with actor
- Export log with filters used
- Retention aligned with your policy
- Audit view readable without SQL access
What Data Manager covers
Datasets
Column definitions, filters, search, ownership.
Import
Staged validation with a line-level error report.
Export
Governed export with log entries.
Audit
Per-column change history with actor and time.
Permissions
Dataset and team scoping, read-only where required.
Read-only views
No checkboxes or bulk actions on sensitive tables.
Questions before importing production data
Can we import from Excel?
Yes, through the staged import with validation before write.
Who can see a dataset?
Visibility follows dataset and team permissions, including read-only roles.
Is deleted data recoverable?
Deletion is soft where the module requires history; the audit trail keeps the record either way.
Does it replace our DWH?
No. It governs the operational data the suite runs on, not analytical modelling.
See this app running on your own data.
We install the suite on your server or in our cloud, then walk your team through the exact flow you described.