Data Governance & Quality
Trust is a property you engineer, not a policy you publish.
Automated quality testing, column-level lineage, and access controls that keep data trustworthy as it scales — so governance is enforced by the pipeline rather than by a document nobody reads.
Governance That Runs, Not Governance That Is Filed
Most data governance fails the same way. A policy document is written, circulated, approved, and then never consulted again, while the actual behaviour of the data is determined entirely by what the pipelines do.
We treat governance as an engineering concern. Quality rules are automated tests that run on every load. Ownership is metadata attached to the dataset, not a name in a spreadsheet. Access control is enforced in the warehouse. If a rule is not executable, it is a preference rather than a control.
This matters most under audit. When a regulator asks who accessed this dataset and how this figure was derived, the answer should come from lineage and logs in minutes — not from three people reconstructing it from memory over a fortnight.
Service Inclusions
Automated Quality Testing
Uniqueness, referential integrity, range, and business-rule assertions executed on every pipeline run.
Column-Level Lineage
Trace any field from dashboard back to source, including every transformation applied along the way.
Access Control & Audit
Role-based access with row and column-level controls, and query logging that satisfies audit requests.
Data Catalogue
Searchable catalogue with definitions, owners, and freshness — so people can find data without asking around.
PII Classification
Sensitive fields identified and tagged, with masking and retention policies enforced automatically.
Quality Alerting
Failures routed to the dataset owner with enough context to act, rather than to a shared inbox nobody watches.
A Process Built for Clarity
No black boxes. No surprise invoices. Every project at Mornis Global follows a disciplined four-phase process designed to reduce risk and maximise value at every stage.
Quality Baseline
Profile critical datasets and measure current quality. This establishes an honest starting point.
Ownership Mapping
Named steward assigned per critical dataset, with escalation paths agreed.
Test Implementation
Quality rules encoded as automated tests, prioritised by how much damage bad data would cause.
Lineage & Catalogue
Column-level lineage captured and a searchable catalogue populated with definitions and owners.
Access & Classification
PII tagged, masking applied, and role-based access implemented in the warehouse.
Operating Model
Review cadence, change process, and steward responsibilities documented so it persists after handover.
The Tech Stack
We select technologies based on performance, scalability, and long-term maintainability, not trends.
Great Expectations
Specialized implementation of Great Expectations in the Data Quality space.
dbt tests
Specialized implementation of dbt tests in the Testing space.
OpenMetadata
Specialized implementation of OpenMetadata in the Catalogue space.
Monte Carlo
Specialized implementation of Monte Carlo in the Observability space.
Snowflake
Specialized implementation of Snowflake in the Warehouse space.
Immuta
Specialized implementation of Immuta in the Access Control space.
Real-World Impact
FinSecure
The Challenge
“Audit requests for data access history and figure derivation took weeks to answer, assembled manually from logs and analyst recollection.”
The Solution
We implemented automated quality testing across critical datasets, captured column-level lineage end to end, tagged PII with enforced masking, and stood up role-based access with full query logging.
Key Performance Indicators
Common Inquiries
Everything you need to know about our specialized services.
Could You Answer an Audit Tomorrow?
Tell us which datasets carry the most risk. We will show you what automated quality and lineage would change.