Fintel Analytics blog
Expert perspectives on data analytics, AI agents, internal tools, and business intelligence from the Fintel Analytics team.
29 of 206 posts
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 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.
Real-time data pipelines are transforming how businesses react to change. Learn how streaming analytics works and why it matters in 2026.
Real-time data pipelines are reshaping how businesses make decisions. Learn how streaming analytics works, where it delivers value, and how to get started.
Data observability is the silent backbone of trustworthy analytics. Discover how leading organisations are using it to eliminate costly data failures in 2026.
Manual compliance is costing fintechs more than they realise — in headcount, fines, and failed audits. Here is how to build the data infrastructure that fixes it.
Alternative data analytics gives fintech startups a real intelligence edge — but only if your data pipeline can actually ingest, clean, and model it at speed. Here's how to build it right.
Most fintechs collect event data but fail to operationalise it. Here's how to build an event-driven data architecture that gives you real-time intelligence — without the chaos.
Settlement timing analytics tell you exactly where your post-trade process breaks down — before regulators or counterparties do. Here is how to build the data stack that survives T+1.
Open banking API feeds generate enormous volumes of raw transaction data — but most fintech teams lack the analytics layer to turn it into decisions. Here's how to fix that.
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.
Training-serving skew is the silent killer of production ML. Learn how to build feature pipelines that actually hold up — and stop your models degrading the moment they ship.
Webhook events are failing silently — and your dashboards are lying to you because of it. Learn how to build reliable webhook ingestion analytics that protect revenue and power trusted metrics.
An internal data marketplace stops teams rebuilding the same pipelines and fighting over different numbers. Here is how to build one that actually works at startup scale.
Batch or CDC for Postgres to BigQuery? A practitioner's framework covering hard deletes, hidden merge costs and the pipeline failures that surface in production.
dbt full-refresh models in BigQuery silently balloon your cloud bill as data grows. Here's how to find the offenders, understand the real cost, and migrate safely to incremental.
Your APIs process every payment, every user action, every integration event — but most fintech teams never analyse that data. Here is how to fix that.
BigQuery table expiration silently deletes tables your dbt models depend on — often with no alerting. Here is how to find and fix it before production breaks.
Parsing nested JSON incorrectly in dbt BigQuery staging models is one of the most common — and most expensive — anti-patterns in modern analytics stacks. Here is how to find and fix it.
Undocumented dbt dependencies are silently breaking production dashboards at growth-stage companies. Here is how to audit your DAG and fix it before it costs you.
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.
Most fintechs have a ledger. Very few have analytics built on top of it. This guide shows exactly what breaks, and how to fix it.
Most fintechs run multi-gateway stacks but have no analytics layer to tell them whether their routing logic is working. Here's how to fix that.
Half of finance teams still take six or more days to close the books. Financial close analytics fixes the broken data pipelines causing that delay — here is how to build it.
Slow dbt models are costing you more than just time — they inflate cloud bills and delay decisions. Here's how to diagnose and fix the real causes.
BigQuery external tables look like a cheap shortcut in your dbt pipeline — until they silently inflate your query bill and break your freshness checks. Here is what actually happens in production.
dbt source fan-out silently duplicates BigQuery scans, breaks lineage, and strips your pipeline of test coverage. Here is how to find and fix it.
When a dbt model references the same BigQuery source table more than once, BigQuery may scan it multiple times per run — silently inflating your bill with every pipeline execution.
Misusing STRUCT and ARRAY types in dbt BigQuery models silently inflates your bill and slows your pipelines. Here is how to find and fix it.
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