Governance model design
A governance function that operates inside your company
We run data governance and data quality as an ongoing service, not a one-time project.
We take ownership of the work an internal data governance team would normally handle. That includes designing your governance model, setting up catalogs and quality rules, enabling stewardship, and running day-to-day operations.
Governance that keeps working after setup.
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Catalogs and quality rules
Stewardship enablement
Day-to-day governance operations
A Practical Setup for Data Governance and Data Quality
We connect governance and data quality directly to your existing data stack so you can control how data is defined, checked, and used across teams.
We assign clear owners to datasets so every table, metric, and pipeline has someone responsible for it.
We align key business definitions so teams stop using different logic for the same metrics.
We set rules that detect missing, broken, or inconsistent data as it moves through your pipelines.
We map lineage so you can trace every report or dashboard back to its source.
Connect everything to your existing stack.
We integrate with your warehouse, lakehouse, and BI tools without replacing your current setup.
Data Governance, Data Quality, and Data Lineage for Enterprise Data Systems
Data Governance & Control
- Clear ownership for every dataset, table, and metric.
- Defined rules for how data is created, changed, and used.
- Central view of data definitions so teams stop working with different versions.
- Access control rules with full auditability for compliance and security.
Data Quality Management
- Automated validation, deduplication, and enrichment rules.
- Data quality checks embedded directly into pipelines.
- Continuous monitoring of data accuracy across systems.
- Early detection of issues before they appear in dashboards or reports.
Data Observability & Lineage
- Pipeline health monitoring and anomaly detection.
- Alerts for failures or unexpected changes in data patterns.
- End-to-end data lineage from source to report.
- Impact analysis to understand what breaks before changes go live.
Built for Real Data Environments
These systems are usually not isolated. They sit across warehouses, pipelines, BI tools, and multiple teams working with different definitions of the same data.
Our work focuses on bringing structure to that environment so data becomes consistent, traceable, and usable across the organization.
Registered supplier to the United Nations
PhD-led engineering team, headed by Sergei Shestakov
Delivery teams serving clients across the United States and Canada
Frequently Asked Questions
Data governance is the set of rules, ownership structures, and controls that define how data is created, accessed, and used across systems. It makes sure teams work with the same definitions and consistent data.
Data quality management works by applying validation rules, monitoring, and anomaly detection directly inside data pipelines. This helps detect and fix issues before they reach dashboards, reports, or analytics tools.
Data observability is the ability to monitor the health, movement, and behavior of data across your pipelines. It helps detect failures, anomalies, and unexpected changes in data early.
No. Data governance and quality layers are implemented on top of your existing warehouse, lakehouse, and BI tools. The goal is to structure and improve what you already have, not replace it.
It depends on the complexity of your data environment. In most cases, initial governance and quality structure can be introduced incrementally without disrupting existing systems.
We work with modern data stacks including cloud warehouses, lakehouses, ETL/ELT pipelines, and BI tools. The approach is tool-agnostic and focused on structuring data flows rather than replacing systems.
Start trusting the numbers.
Book a Governance Consultation → Book a governance consultation with MPP Insights. In 30 minutes, we review your current state, identify key governance and compliance gaps, and outline what works for your environment.