Automating Domo Data Quality: Tests, Alerts & Data Contracts
Data quality is not a reporting issue.
It is a trust issue.
When leaders lose confidence in dashboards, adoption drops.
When numbers are questioned in meetings, momentum slows.
When data breaks silently, decisions become risky.
Organizations using Domo often assume dashboards equal reliability. In reality, data quality must be engineered deliberately.
This guide explains how to implement automated Domo data quality tests, alerts, and data contracts – so errors are caught before executives see them.
Table of Contents
Why Domo Data Quality Matters More Than You Think
Bad data does not fail loudly.
It fails quietly:
- Missing rows
- Null values
- Incorrect joins
- Schema changes
- Delayed refreshes
- Silent API failures
By the time someone notices, trust is already damaged.
Data quality in Domo should be:
- Proactive
- Automated
- Measurable
- Governed
Not reactive.
The 4 Pillars of Domo Data Quality
Effective data quality in Domo rests on four foundations:
1️⃣ Data Quality Tests in Domo (Foundation Layer)
Automated validation is the core of reliable analytics.
What Are Data Quality Tests?
Data quality tests validate that datasets meet defined expectations before being used in dashboards.
In Domo, this can be implemented using:
- ETL checks
- Validation tiles
- ETL checks
- Validation tiles
- Threshold conditions
- Scheduled validation flows
Types of Data Quality Tests You Should Implement
✔ Row Count Validation
Detect unexpected data drops or spikes.
Example:
- Yesterday’s orders = 12,000
- Today’s orders = 2
This is not a dashboard issue – it’s a pipeline issue.
✔ Row Count Validation
Detect unexpected data drops or spikes.
Example:
- Yesterday’s orders = 12,000
- Today’s orders = 2
This is not a dashboard issue – it’s a pipeline issue.
✔ Schema Change Detection
If a source system changes:
- Column names
- Data types
- Field order
Without checks, dashboards may silently misbehave.
✔ Schema Change Detection
If a source system changes:
- Column names
- Data types
- Field order
Without checks, dashboards may silently misbehave.
2️⃣ Automated Alerts in Domo (Monitoring Layer)
Testing without alerting is incomplete.
Domo allows alerts based on:
- Metric thresholds
- Scheduled checks
- Anomaly detection
- ETL outcomes
Best Practice:
Never rely on someone manually checking dashboards.
Instead:
- Configure automated alerts
- Notify dataset owners
- Route alerts to Slack or email
- Escalate repeated failures
Data reliability must be visible.
3️⃣ Data Contracts (Advanced & Underused)
This is where mature organizations differentiate.
What Is a Data Contract?
A data contract is a documented agreement between:
- Data producers (source systems)
- Data consumers (analytics teams)
It defines:
- Expected schema
- Field definitions
- Refresh frequency
- Acceptable thresholds
- Change notification process
Without contracts, every schema change becomes an emergency.
Why Data Contracts Matter in Domo
Domo is downstream of many systems:
- CRM
- ERP
- Marketing platforms
- APIs
- Data warehouses
If upstream teams change data structures without notice, dashboards break.
Data contracts prevent:
- Surprise schema drift
- KPI inconsistency
- Governance chaos
They convert reactive troubleshooting into controlled evolution.
4️⃣ Ownership & Governance (The Missing Layer)
Even perfect tests fail without ownership.
You need:
- Named dataset owners
- Clear escalation paths
- Data quality SLAs
- Version control discipline
- Documentation standards
Governance is not bureaucracy.
It is protection of trust.
Common Data Quality Failures in Domo
We consistently see:
- KPI logic duplicated in multiple ETLs
- Beast Modes masking underlying data errors
- No row count checks
- Alerts configured but ignored
- No data dictionary
- No version control
These issues compound silently until a leadership meeting exposes them.
Advanced Best Practices for Enterprise Domo Data Quality
For mature teams:
✔ Create a Dedicated Data Quality Layer
Separate validation flows from transformation flows.
✔ Build Reusable Test Frameworks
Standardize row count checks across datasets.
✔ Version-Control Your ETL Logic
Document changes and validate impact before deployment.
✔ Use Parallel Validation
Compare Domo outputs with source-of-truth systems periodically.
✔ Track Data Quality KPIs
Measure:
- Data freshness %
- Failure rate
- Alert resolution time
- Schema stability
What gets measured improves.
When to Seek Domo Data Quality Help
You likely need expert support if:
- Executives question numbers regularly
- Alerts are inconsistent
- ETL pipelines are difficult to audit
- You are scaling across departments
- Governance is informal
- You are preparing for compliance audits
Data reliability becomes exponentially harder as scale increases.
Before Trusting Your Dashboards – Run This Check
Download our Domo Data Quality Audit Checklist to:
- Identify hidden data risks
- Evaluate validation coverage
- Assess alert effectiveness
- Benchmark governance maturity
- Strengthen executive trust
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