Data Quality & Governance

Every team has their own version of the numbers. None of them match.

We implement data testing, validated metric definitions, proactive alerting, ownership structures, and governance frameworks that make your data reliable and keep it that way across the entire organization.

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Data Quality & Governance

Data quality problems compound. One wrong formula in a source system becomes a wrong metric in a dashboard, which becomes a wrong decision in a board meeting. By the time someone notices, the root cause is buried under months of accumulated errors and workarounds.

100%

Test coverage on critical data models

3 weeks

Time to first trusted metric definitions

0 hrs/mo

Manual reconciliation after implementation

Use Cases

Engagements where this is the right work.

Conflicting reports across departments

Finance says revenue is one number, sales says another, and the CEO gets a third. The fix is not another dashboard. It is a shared data model with tested, validated definitions that are consistent across the entire organization.

Establishing a single source of truth

Your organization has multiple systems of record that contradict each other. We align them into a governed data architecture where every team works from the same definitions and the same numbers.

Data maturity for growth

Your data team spends more time firefighting quality issues than building new capabilities. We put the governance foundations in place so your team can move faster with less rework.

M&A data integration

You acquired a company and now have two conflicting definitions of revenue, customer, and churn. We build the unified data model that resolves conflicts and gives leadership a single view across entities.

Audit and compliance preparation

Regulators and auditors want to see where your numbers come from. We build lineage tracking, documentation, and validation that makes audit prep routine instead of a fire drill.

Our Process

A structured approach that delivers results at every stage.

01

Trace Metrics to Their Source

We start by identifying the 10-15 metrics your organization makes decisions on. We trace each one from the dashboard back to its source, document every transformation, and write automated tests that validate accuracy on every pipeline run. We map data lineage so your team understands dependencies and can anticipate the downstream impact of upstream changes. Then we build the governance framework: validated definitions, clear data ownership, proactive alerting, and an organizational structure that keeps everything consistent as your team and data grow.

Output: Lineage map from dashboard down to source-system column

02

Build Testing and Validation

Tested, documented data models in dbt or your transformation tool of choice. Automated quality monitoring that alerts your team before bad data reaches a report. A semantic layer with metric definitions your whole organization agrees on. Lineage tracking that shows exactly where every number comes from. A data ownership structure with clear accountability. And a team that can maintain all of it after we leave.

From our podcasts: Enhancing Developer Efficiency with SQLMesh, Unlocking the Power of Semantic BI with Hashboard, and People as Problem and Solution in Data Leadership with Nicole Radziwill.

Output: dbt tests, freshness checks, and CI gates that block bad data

03

Align Definitions Across the Organization

The hardest part of governance is not the technology. It is getting an organization to agree on definitions and align systems of record. We facilitate that alignment, working with stakeholders across departments to establish the canonical definitions for revenue, customer, churn, and whatever metrics drive your business. Once those definitions live in code, they are version-controlled, testable, and immune to the spreadsheet drift that created the problem in the first place.

Output: Metric dictionary with named owners and a review cadence

Technologies

We pick the right tool for the problem, not the other way around.

Results

Real outcomes from real engagements.

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Nonprofit / Research

Secure Data Pipeline for a Global Research Organization

5 production instances
Survey systems integrated
PII separated from analytical layer
Data security
Read the full story

Frequently Asked
Questions

What does data quality and governance look like in practice?
It means your data is accurate, complete, timely, and consistent, and that there are clear owners, definitions, and processes to keep it that way. In practice: automated tests on every pipeline run, alerts when something breaks, documented and validated definitions for every metric, lineage tracking from source to dashboard, and an ownership structure so someone is accountable for every critical dataset.
How do you implement data testing?
We use dbt tests, custom SQL validations, and tools like Great Expectations to build automated checks into your data pipelines. Tests run on every pipeline execution and catch issues like null values, duplicate records, referential integrity violations, and metric drift before bad data reaches your reports.
How do you handle governance without slowing teams down?
Governance that blocks people from doing their jobs gets ignored. We implement lightweight governance: clear ownership for every dataset, documented definitions, automated quality checks, proactive alerting when issues arise, and self-service access to governed datasets. The goal is to make good data easier to find and use, not harder.
How long does it take to see results?
You will typically have tested metric definitions and automated quality checks within 3-4 weeks. Full governance frameworks, including documentation, lineage, ownership structures, and access controls, typically take 2-6 months depending on the complexity of your organization and the number of systems involved.
How do you handle dependencies and data lineage?
We map every critical metric from the dashboard back to its source system, documenting every transformation along the way. This lineage tracking ensures that when something changes upstream, your team knows what downstream reports are affected. Conflicts are proactively identified and resolved so that evidence for decision-making stays consistent across the organization.
What if our data quality problems are in the source systems?
That is common. We build validation at the boundaries: checks that flag bad data as it enters the warehouse so your team can fix issues at the source rather than discovering them in a board deck. We also help establish data ownership, so each system of record has a clear accountable party and quality expectations are formalized.

Tired of numbers nobody trusts?

We’ll audit your data models and show you exactly where the inconsistencies are. No guessing required.

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Our Other Services

Or, see what else we do.

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Data Engineering

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Data Reliability & Performance

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Managed Data Team

Get a full data team without the hiring timeline. CorrDyn embeds the specific skill sets you need and owns outcomes, not just hours.

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Data Assessment & Roadmapping

Get a clear picture of your data maturity and a prioritized plan forward. CorrDyn assessments have standalone value before any build begins.

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