Day 1
Onboarded Valido in under 30 minutes. Connected Snowflake and configured 12 critical KPI tables.
Case Study
How Numeriq Cut Data Incidents by 81% with Valido
From rapid Series B growth to zero dashboard outages, see how a fintech leader rebuilt trust in their data stack.
Company
Numeriq
Industry
Fintech & Payments
Team Size
250 Employees
Data Stack
Snowflake, dbt, Fivetran
The Challenge
Silent failures in a rapidly scaling pipeline
When Numeriq entered Series B, their data volume doubled in three months. The team was focused on shipping new features, but they were losing confidence in their own analytics. They noticed a troubling trend: dashboards would occasionally show negative user counts or zero transaction volumes during peak hours—only to correct themselves five minutes later. These "silent failures" were eroding trust with product and finance stakeholders.
The existing monitoring tools were too generic. They flagged *every* pipeline as "yellow" or "red" due to minor latency, creating alert fatigue. The team needed a solution that could distinguish between a transient glitch and a real data integrity issue.
Why Valido
Evaluation process and key differentiators
Numeriq evaluated three major data observability vendors. While competitors offered robust monitoring, Valido stood out in three critical areas.
Deep Warehouse Integration
Unlike other tools that required ETLing data out to a separate platform, Valido's agent ran natively inside their Snowflake environment. This meant they could validate row counts and checksums directly against the source of truth.
AI-Driven Root Cause
When an anomaly was detected, Valido didn't just say "Row count is down." It analyzed the lineage graph and pinpointed the upstream source: a Fivetran sync that had stalled due to a schema change in the source database. This saved them hours of manual debugging.
Implementation
From onboarding to first anomaly in 48 hours
Day 3
Deployed "Daily Revenue" validation rule. Valido caught a null value in the transaction amount field immediately.
Week 2
Set up adaptive baselines. The system learned the normal traffic patterns and stopped flagging routine fluctuations.
A Culture Shift in Data Ops
Before Valido, the Data Engineering team spent their Friday afternoons reacting to fires. Now, they spend their mornings validating the health of the previous week. The shift has allowed them to move from reactive firefighting to proactive pipeline maintenance.
The Results
Quantified impact on trust and efficiency
81%
Reduction in data incidents
14 hrs
Saved per week on manual debugging
0
SLA breaches in 6 months post-launch
100%
Confidence in dashboard accuracy
"Valido acts as a safety net that allows us to move fast without breaking things."
"Before Valido, every anomaly required a manual investigation by a senior engineer. Now, the root cause is surfaced in seconds. It has completely changed how our team operates."
Sarah Jenkins
Head of Data Engineering
"The ROI was immediate. We stopped losing stakeholders to bad data."
"Finance was constantly questioning our numbers. Since implementing Valido, our dashboards have been rock solid. It’s rare to find a tool that delivers on its promise so consistently."
Michael Ross
VP of Analytics
Tech Stack
Built on a reliable foundation
Snowflake
dbt
Fivetran
Valido
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A deep dive into how one finance team automated their data quality checks using AI-assisted rules.
Read the case studyReady to cut data incidents?
See how Valido can help your team build a data pipeline that you can actually trust. Book a demo today.