Product overview · Online version
FenDQ
No-code data quality for investment managers — on the pipelines and databases you already run
FenDQ is Fencore’s focused data-quality product. It was built for buy-side market, reference, static, sustainability, alternative, and client data. It plugs into existing systems without forcing a new operational store. When FenDQ is used inside a DataHub workspace, Cori can help teams query documentation, configuration, metadata, data, and alerts, and build quality pipelines from natural language.
The job FenDQ does
Most investment firms already have pipelines. What they often lack is a clear, repeatable way to measure whether those pipelines are producing data that operations and investment teams can use. FenDQ is that layer: connect to the sources and databases you already have, define the checks that matter to the business, run them, and work the exceptions.
It is intentionally narrower than DataHub. FenDQ does not try to become the golden operational store, the recon engine for IBOR versus ABOR, or the analytical warehouse. It answers: is this dataset complete, timely, internally consistent, and aligned with the rules we said we care about — and if not, who owns the break?
Typical datasets include vendor market and reference feeds, internally maintained static data, sustainability and ESG files, alternative-data drops, and client or account data. The same product can sit on spreadsheets or on multi-system estates; the architectural claim is that you do not have to change the surrounding stack to start.
How it works in practice
- Connect to feeds and databases with the same style of universal connectors used across Fencore products (files, APIs, databases, and related channels).
- Configure validation and business rules in a no-code, drag-and-drop interface, including multi-step checks.
- Run data through the engine on a schedule or as part of an existing pipeline.
- Review quality statistics and issue lists that highlight impact, not only raw error counts.
- Remediate: correct, override under control, or send the issue back to a source owner.
- Keep an audit trail of what was checked, what failed, and what was done.
Detection is hybrid. Rule-based controls remain the backbone — they are explainable and testable, which regulated firms need. FenDQ also uses assisted detection to surface patterns and gaps that a static rule list may miss. Those insights are there to guide users, not to silently rewrite the golden record.
Visual quality statistics
See the health of sources and datasets without exporting to a desktop workbook first.
Prioritised issues
Work the queue by business impact rather than treating every failed check as equal.
Fast proof of concept
Because FenDQ does not require you to retire the current store, quality programmes can be proven on a slice of data before a wider rollout.
FenDQ versus DataHub
Choose FenDQ when the immediate problem is quality on top of systems you will keep. Choose DataHub when you also need an operational store, mastering into a golden record, packaged buy-side workflows, FenRecon, FenWarehouse, and Cori as the daily assistant. Many clients start with FenDQ and expand; others start with DataHub and use the same quality discipline as a module rather than a separate estate.
The full DataHub overview is at /products/datahub/full. That page is the right place for the operating loop (connect through serve) and for Cori’s complete capability list.
Governance and exceptions
- Granular access control so quality results and underlying data are only visible to the right roles
- Four-eye approval where the firm requires dual control on changes
- Automated lineage and audit trail so a failed check can be explained after the fact
- Intelligent exception management so the same break is not rediscovered in email every morning
FenDQ is meant to de-risk change programmes: you can measure quality before and after a proposed model or rule change, and you can run it beside existing pipelines without cutting them over on day one.
Cori, when FenDQ sits in DataHub
Cori is Fencore’s in-product assistant, delivered with DataHub. If FenDQ is running as part of that workspace, users can ask Cori to query product documentation, explain configuration, inspect metadata, query data they are allowed to see, summarise and find alerts, and build pipelines from natural language. Cori does not replace approval workflows. Details are on the DataHub full overview at /products/datahub/full.
Where it sits in the suite
| If you need | Use |
|---|---|
| Quality checks on current systems, without a new ODS | FenDQ |
| Golden operational store, mastering, reporting, Cori, warehouse | DataHub |
| Breaks between two independently maintained books or files | FenRecon |
| Analytical copy of mastered data for BI and AI | FenWarehouse |
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