Data Engineering & Analytics · 3 of 5
Implement comprehensive data governance frameworks for data quality, security, and compliance.
Why it matters
Implement comprehensive data governance frameworks for data quality, security, and compliance.
This sits inside our data engineering & analytics practice, and rarely arrives alone — most engagements combine it with two or three of its neighbours. The assessment decides which, and in what order.
What the practice is measured on
Unified Data Platform
Centralize all your data sources in one modern platform
Real-time Insights
Get instant access to business-critical analytics
Scalable Architecture
Handle petabytes of data with cloud-native solutions
Data Governance
Ensure data quality and compliance across the organization
Microsoft Fabric, Azure Synapse, Databricks, Delta Lake, Apache Spark
Azure Data Factory, Logic Apps, SSIS, Apache Airflow, Talend
Microsoft Purview, Data Catalog, Data Lineage, Data Quality, Compliance
You are hereThe tooling we actually build data governance on.
Power BI, Tableau, Looker, QlikView, D3.js
Azure Monitor, DataDog, Grafana, Prometheus, ELK Stack
A sequence you can plan around, with a decision point at the end of each phase rather than one big reveal at the end.
Evaluate current data landscape and identify opportunities
Design scalable data platform architecture
Connect and integrate all data sources
Build dashboards and analytical models
Implement data governance and monitoring
The constraints differ more than the technology does. Each sector page sets out what changes in that context.
Five commitments that hold on every engagement, not just the ones that go well.
Every engagement opens with an assessment that produces a prioritised backlog. Engineering starts against that, not against an assumption.
Existing systems keep running while we work. Delivery arrives in increments you can put in front of users rather than one release at the end.
Each phase has defined outputs and a defined cost, with a decision point at the end. You can stop between phases without stranding the work.
Architecture decisions are written down with their rationale, in your repositories, so the reasoning survives the people who made it.
Access control, auditability, and data residency are settled in the first architecture review rather than retrofitted before an audit.
What we are asked most often about data governance.
Almost never. We work incrementally around what you already run, extracting interfaces and migrating in phases so the existing system keeps serving users while the new one takes over piece by piece.
That is what the assessment establishes. We map your current architecture, data and constraints first, and if the approach will not hold in your environment we say so before anyone commits to a build.
Discovery is fixed-price and ends with a costed roadmap. Build phases are then priced per phase against defined outputs, so you are never approving an open-ended budget.
You do — code, infrastructure definitions, any trained models, and the documentation. All of it lands in your own repositories and cloud tenancy as we go.
Monitoring, alerting and agreed response targets are part of delivery. Where we also run the platform under managed services, we are the ones on the other end of the alert.
We map what you have, what it would take, and in what order — specific to data governance in your environment.
Book a Free Architecture ReviewWhat you get from the audit
Yours to keep whether or not you engage us.