Each phase works on its own, or as part of the full path.
The oversight that runs through every phase.
Data Governance · Cross-cutting oversight across all phases
A data infrastructure stays reliable because someone keeps checking it, not by chance. Data Quality & Governance runs through all four phases: it verifies data is correct from the design stage, stays complete and up to date, gets validated before use, and that someone notices immediately if something breaks.
It's an ongoing check, the reason why, months later, you can still trust the numbers you're looking at.
How we do it
- 01Quality by design: tracking and schema accuracy, consent and GDPR, deduplication built in at collection time.
- 02Completeness & freshness: reconciliation between source and BigQuery, latency checks, alerts on schema changes.
- 03Validation & testing: validation rules, attribution and deduplication checks at every transformation.
- 04Observability & alerting: anomaly detection, data downtime measurement, a reliability indicator on dashboards and AI tools.
The tools we use
Frequently asked questions
Is Data Governance a separate service or included in the other phases?
It's a cross-cutting layer: it runs through all four phases (Source, Datawarehouse, Data Preparation, Activation) instead of being a separate project. It checks that each one is doing its job correctly.
How do we know if something breaks in tracking or the data?
Through observability: anomaly detection, measuring how long data goes missing (data downtime), and an alert when a source's schema changes without warning.
What's this 'trust score' you mention?
A reliability indicator visible directly on dashboards and AI tools: at a glance you know whether the number you're looking at is trustworthy today, instead of having to guess.
Do we need this from day one, or can we add it later?
It's better from day one (quality by design costs less than fixing it later), but it can also be layered onto an existing infrastructure, after checking what's missing first.
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