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Data Governance and Data Quality

Trust in the numbers, engineered: ownership and definitions settled, quality rules automated in the pipeline, monitoring that catches drift before executives do, sized for your organization rather than imported from a bank.

What You Get

OwnedEvery critical dataset has a name on it
RuledQuality checks in the pipeline, automated
MonitoredDrift caught before the dashboard
PracticalGovernance sized to fit, not smother
What's Included

Data Governance and Data Quality: the full scope

Governance operating model
Ownership, stewardship, and decision rights, minimum viable and written down.
Critical data definitions
The measures and dimensions that matter, defined and agreed.
Quality rule implementation
Completeness, validity, and reconciliation checks automated in Fabric pipelines.
Monitoring and alerting
Quality dashboards and alerts wired to the people who can act.
Purview integration
Catalog, lineage, and sensitivity classification where the estate warrants it.
Issue management loop
A path from detected problem to accountable fix.
How We Work

Structured delivery, every engagement

The same disciplined framework behind our enterprise migrations: documented, validated, and led by senior expertise from start to finish.

1

Assess

Inventory every report, data source, and dependency. Usage analysis identifies what actually matters.

2

Rationalize

Retire duplicates and unused reports. Typically 30 to 50 percent never need migrating.

3

Modernize

Rebuild on Power BI and Fabric with governed semantic models and validated outputs.

4

Govern

Workspaces, row-level security, certified datasets, and documented standards.

5

Enable

Training and handover so your team owns the platform, not a vendor.

Talk to a specialist

A free 30-minute discovery call: bring your questions about data governance and data quality, leave with a clear next step and an honest read on effort and cost.

Book a Discovery Call →
Common Questions

Data Governance and Data Quality FAQ

Where do quality checks actually run?

In the pipeline: validation at ingestion, reconciliation between layers, and threshold alerts in Fabric before bad data reaches a semantic model. The dashboard is where problems become visible, not where they should be found first.

How heavy does governance need to be?

Lighter than the frameworks suggest: clear ownership of critical datasets, agreed definitions, automated rules, and an escalation path covers most organizations. We size it to yours and resist ceremony.

Do we need Purview?

At estate scale, yes: catalog, lineage, and classification pay for themselves. Smaller estates start with Fabric-native lineage and endorsement and grow into Purview when sprawl demands it.

How is success measured?

Fewer reconciliation escalations, faster issue detection-to-fix, and quality scores per critical dataset trending on a dashboard leadership can read.

Related Services

More ways we can help

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Tell us what you are working with. A senior specialist will respond within one business day.

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Location
Vancouver, BC. Serving BC, Alberta, and Washington.
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