Build data foundations your teams can trust and act on
We help organisations create practical data governance, data strategy and information management foundations that improve reporting, reduce risk and prepare data for analytics, Microsoft Fabric, AI, Copilot and business decision-making.
Good data governance makes data easier to use, not harder to access
Most organisations do not have a data problem because they lack information. They have a data problem because ownership is unclear, definitions vary across teams, quality is inconsistent and sensitive data is difficult to manage with confidence.
Synenza helps organisations establish practical data governance and data strategy that supports analytics, reporting, Microsoft Fabric, AI readiness, Microsoft Copilot, compliance and operational decision-making. The focus is to create clear ownership, trusted definitions, secure access and a roadmap that teams can actually follow.
Built for the work — not for the deck.
Data strategy and roadmap
Define a clear data strategy aligned to business priorities, reporting needs, analytics goals, AI readiness and long-term platform direction.
Data governance operating model
Establish practical governance roles, responsibilities, decision processes, ownership models and stewardship practices across business and technology teams.
Data quality and trusted definitions
Identify data quality issues, inconsistent definitions, duplicate records, ownership gaps and reporting conflicts that reduce confidence in business information.
Microsoft Purview and information management
Use Microsoft Purview, sensitivity labels, classification, lineage, catalogue and information protection patterns to improve visibility, control and compliance.
Data access, privacy and protection
Review access controls, sensitive data exposure, sharing risks, retention needs and privacy requirements so data can be used safely across the organisation.
AI-ready data foundations
Prepare enterprise data for Microsoft Copilot, AI agents, knowledge assistants, analytics and automation by improving structure, ownership, governance and accessibility.
A measured, honest path from idea to production.
Understand
Review business priorities, reporting challenges, data sources, ownership, governance maturity and current pain points across teams and systems.
Assess
Evaluate data quality, definitions, access, sensitivity, governance controls, metadata, reporting alignment and platform readiness.
Design
Define the governance model, data strategy, ownership structure, priority domains, standards, controls and improvement roadmap.
Enable
Support rollout with practical guidance, stakeholder alignment, governance templates, data quality actions and continuous improvement recommendations.
Common business scenarios.
AI and Microsoft Copilot readiness
Prepare organisational data for AI, Microsoft Copilot, custom copilots and knowledge assistants by improving access, permissions, classification, quality and governance.
Reporting and analytics trust
Create consistent definitions, trusted data sources and quality controls so teams can rely on dashboards, reports and business intelligence outputs.
Compliance and information protection
Improve visibility and control over sensitive data, retention requirements, sharing risks, access permissions and information protection obligations.
What good looks like.
- A clear data strategy aligned to business priorities, analytics goals and AI readiness.
- Defined data ownership, stewardship roles and governance responsibilities.
- Improved confidence in reports, dashboards, metrics and business definitions.
- Practical recommendations for Microsoft Purview, data classification, sensitivity labels and information protection.
- A better foundation for Microsoft Fabric, Power BI, Microsoft Copilot, AI agents and knowledge assistants.
- A phased roadmap for improving data quality, governance, access and long-term information management.
The questions clients ask first.
What is data governance?
Why is data governance important for AI and Microsoft Copilot?
What does a data strategy include?
Can Synenza help with Microsoft Purview?
Do we need perfect data before starting analytics or AI projects?
Can data governance be practical for smaller organisations?
Let's scope a first conversation.
Tell us what you're trying to do. We'll come back with a point of view, not a sales pitch.