Data Governance, Quality and Compliance: A 2026 Guide
Data governance, quality and compliance are no longer optional. This guide breaks down practical frameworks and real-world strategies for business leaders and data teams in 2026.
Fintel Analytics blog
Expert perspectives on data analytics, AI agents, internal tools, and business intelligence from the Fintel Analytics team.
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Data governance, quality and compliance are no longer optional. This guide breaks down practical frameworks and real-world strategies for business leaders and data teams in 2026.
Late-arriving data silently corrupts dbt incremental models in BigQuery — producing wrong metrics that nobody catches until a decision has already been made. Here is how to fix it.
Silent data failures are the most expensive kind — your pipeline runs green while bad data corrupts every dashboard downstream. Here's how to build the analytics layer that catches them first.
Most startups don't find out their data is broken until a stakeholder catches it in a board deck. A proper dbt testing strategy changes that — here's how to build one.
Most startups don't realise their data pipeline is broken until a board meeting goes sideways. Here's how to build reliable data infrastructure from day one — before it costs you.
Data observability is the silent backbone of trustworthy analytics. Discover how leading organisations are using it to eliminate costly data failures in 2026.
Why finance and ops report different daily totals for the same transactions, and how to fix timezone errors in BigQuery and dbt with one governed reporting date.
A practitioner's guide to moving business-critical spreadsheet logic into tested dbt models. Learn what to migrate first and how to prove the numbers match.
Untested dbt models are silent liabilities. Learn how to find every model with no test coverage in BigQuery using runnable SQL and dbt commands — in under five minutes.
Every dbt source without a freshness config is a silent risk. Here's how to find every unchecked source in under five minutes using BigQuery INFORMATION_SCHEMA — and how to fix it.
Silent NULLs in dbt and BigQuery pass every test, build successfully, and corrupt your metrics for weeks before anyone notices. Here is how to find them.
Fan-out joins silently inflate dbt metrics in BigQuery — revenue looks higher, churn looks lower, and nobody notices until a stakeholder asks why the numbers don't add up.
dbt's default on_schema_change='ignore' silently drops new source columns from incremental models. Here's how to detect and fix it before your dashboards lie to you.