Data Review Best Practices for Modern Data Teams

What Is Data Review?

Data review is the practice of systematically validating data model changes before merging them into production. Unlike code review, which examines logic, data review examines the actual output — the rows, columns, and values that downstream consumers depend on.

Modern data teams working with dbt (data build tool) face a core challenge: a single model change can affect every downstream dependency in the DAG — from intermediate models to dashboards and ML features. Data review provides visibility into this blast radius before changes ship.

Why Data Review Matters

Traditional data quality approaches rely on post-deployment monitoring — catching issues after they’ve already affected production dashboards and reports. Data review shifts this left:

• Pre-merge validation: Compare branch output against production baseline
• Impact analysis: Understand which downstream models are affected
• Automated diffing: Detect schema changes, row count shifts, and value distribution changes
• Human judgment: Flag changes that are technically valid but semantically wrong

Core Components of a Data Review Process

1. Impact Analysis

Before reviewing data, understand the scope of change. Impact analysis maps which models are modified and traces their downstream dependencies. This tells reviewers where to focus attention.

Key metrics for impact analysis:

Metric	What It Measures	Why It Matters
Modified models	Direct code changes	Primary review targets
Downstream models	Transitive dependencies	Blast radius of the change
Affected exposures	Dashboards, ML features	Business impact visibility
Row count delta	Production vs. branch	Data volume changes

2. Automated Checks

Automate the repetitive parts of data review:

• Schema diff: Detect added, removed, or renamed columns
• Row count comparison: Flag unexpected increases or decreases
• Value distribution: Compare histograms of key columns
• Primary key validation: Ensure uniqueness constraints hold

3. PR-Level Reporting

Integrate data review results into your pull request workflow. A data review summary posted as a PR comment gives reviewers context without switching tools.

Implementing Data Review with Recce

Recce automates data review for dbt projects. The typical workflow:

1. Developer opens a PR with model changes
2. CI runs dbt build on the PR branch
3. Recce compares branch output against the production baseline
4. Recce posts a diff report as a PR comment
5. Reviewers approve or request changes based on data impact

Integration with dbt CI/CD

Recce plugs into existing dbt CI pipelines. After dbt build completes, Recce runs its comparison checks and reports results. No changes to your dbt project structure are required.

Best Practices

1. Review data, not just code: A syntactically correct model can produce wrong results. Always check the output.
2. Scope reviews to impact radius: Don’t review every model — focus on modified models and their direct downstream dependencies.
3. Automate the baseline: Use CI to maintain a production baseline that Recce compares against automatically.
4. Set blocking thresholds: Define what constitutes a blocking data change (e.g., >10% row count change) and enforce it in CI.
5. Document expected changes: When a PR intentionally changes data output, annotate the expected changes in the PR description.