Data Management & Governance

A strong data strategy only creates value when it is supported by effective data management and governance. As data volumes, reporting needs and analytics and AI use cases grow, organizations need clear structures around data ownership, data quality, definitions, access and lifecycle management. TriFinance helps organizations create trusted, governed and business-ready data by aligning people, processes and technology. This enables reliable reporting, scalable self-service analytics, stronger Power BI and Microsoft Fabric environments, and a solid foundation for future AI initiatives.

A strong data and analytics architecture provides clarity on where data originates, how it flows through the organization and which systems serve as the single source of truth. This reduces fragmentation, improves data quality and increases trust in reporting.

TriFinance helps organizations design future-proof architectures that connect source systems, data platforms, semantic models and reporting solutions. Whether built on Microsoft Fabric, Databricks or Azure, the goal is to create a structured path from raw data to governed, business-ready data products.

Our approach is based on the Medallion Architecture: Bronze for raw source data, Silver for cleansed and standardized datasets, and Gold for curated data products used in reporting, analytics and AI. This layered model improves quality, governance and scalability.

We align architecture design with business priorities by assessing reporting needs, KPIs, source systems and future analytics requirements. The result is a sustainable data foundation that supports reporting, performance management, ESG, planning and AI initiatives.

Typical architecture topics 

• Define authoritative source systems and data ownership for key business domains such as finance, customers, products, employees and transactions.
• Use the Medallion Architecture as a future-proof data design principle, structuring data into Bronze, Silver and Gold layers to improve quality, traceability and business readiness from source data to reporting, analytics and AI.
• Design the central data platform by clarifying which logic belongs in raw, validated and curated data layers, and which logic should be handled in semantic models or reporting tools.
• Integrate Microsoft Fabric, Databricks, Power BI, EPM systems and business applications into one coherent architecture for reporting, planning, analytics and AI.
• Create reusable business data products for finance, sales, operations, HR, ESG and performance management to reduce duplication and increase consistency.
• Align data architecture with governance requirements such as ownership, documentation, lineage, access management, data quality and AI-readiness.

Data models give business meaning to data by structuring facts, dimensions, relationships and KPIs in a consistent way. They form the foundation for reliable reporting, analytics and decision-making.

TriFinance designs data models with a strong business focus, supporting finance, sales, operations, HR, planning and ESG reporting. We ensure consistent KPI definitions, shared dimensions and scalable semantic models to avoid duplicated logic and conflicting insights.

In Power BI and Microsoft Fabric, the semantic model is a critical layer that brings together business logic and reporting definitions. A well-designed model enables trusted reporting, self-service analytics and AI-driven insights.

Our approach is based on proven modelling principles based on the Kimball dimensional modelling techniques (star schema designs and conformed dimensions). Combined with strong governance, documentation and lifecycle management, this creates data models that are scalable, understandable and easy to maintain.

What this enables 

• Consistent reporting definitions across teams, reports and dashboards. 
• Reusable and performant semantic models that reduce duplication and improve maintainability. 
• Clear relationships between operational data, financial data and management KPIs. 
• A strong single source of truth for Power BI, planning, forecasting and performance management. 
• A strong foundation for AI use cases by making relationships, definitions and measures explicit, improving the usability of data for predictive analytics and AI-enabled reporting.

Data governance defines how data is managed, owned, protected, documented and used across the organization. It brings structure to decision rights, responsibilities, policies, processes and controls. In practice, governance answers essential questions: who owns this data, which definition is correct, who can access it, how quality is monitored, how changes are approved and how users know which report or dataset they can trust. 

TriFinance approaches governance as an enabling framework, not as bureaucracy. The goal is to create the right balance between user empowerment and necessary control. Self-service analytics should remain possible, but it needs guardrails around data ownership, certified content, access management, lifecycle processes, documentation and quality monitoring. This balance is critical in Power BI and Microsoft Fabric environments, where business users can create value quickly but where unmanaged growth can also create report sprawl and inconsistent definitions. 

Power BI Governance Dashboard

TriFinance has developed a standard Power BI Governance Dashboard to help organizations quickly gain control over their Power BI environment. The dashboard brings together insights on usage, access rights, licenses, lineage, artifacts and refresh performance, giving business and IT teams a clear view on how Power BI is used across the organization. It supports practical governance actions such as identifying unused reports, monitoring adoption, reviewing access rights, optimizing licenses and detecting refresh issues. This provides a concrete starting point for improving Power BI governance and creating a more transparent, structured and trusted analytics environment.

BI governance framework

Our BI governance framework is built around four connected dimensions: People and Organization, Processes, Systems and Data. It defines who owns data, how analytics assets are created and managed, which systems and tools support the environment, and how data quality, definitions and security are controlled. This integrated approach helps organizations avoid uncontrolled report growth, improve transparency, strengthen trust in KPIs and create a sustainable foundation for Power BI, Microsoft Fabric, self-service analytics and AI-ready data. Our approach is pragmatic and progressive: define the minimum governance model that achieves the required objectives, then mature it based on adoption, usage, risk and business value. 

Business-driven data catalog with dScribe

As part of our governance approach, TriFinance partners with dScribe, a Belgian data catalog solution that helps business users find, understand and trust reports, KPIs and data assets. Many organizations already have technical cataloging initiatives in place, but these are often not sufficiently adopted by the business. dScribe addresses this gap by bringing documentation, business definitions and KPI context closer to the end user, including directly inside the Power BI reporting experience. This makes it easier to document reports, govern KPI definitions, assign ownership and create a shared business glossary. For TriFinance, dScribe is a strong fit because it supports governance from a business perspective, helping customers improve data literacy, reporting adoption and trust in the numbers used for decision-making.

Governance domains we typically address 

• Data ownership, stewardship, roles and responsibilities. 
• Data definitions, KPI governance, business glossary and metadata management. 
• Data access, security, privacy, compliance and lifecycle management. 
• Power BI governance, workspace structure, deployment practices, certified content and report lifecycle processes. 
• Monitoring of Power BI usage, access rights, lineage, licenses, artifacts and refresh performance. 
• Data catalog, business glossary and metadata management for better discovery and trust. 
• User enablement, adoption, community building, training and continuous improvement. 

High-quality data is accurate, complete, consistent and reliable. Poor data quality reduces trust in reporting, creates inefficiencies and limits effective decision-making. As data volumes grow, organizations need a structured approach to continuously monitor and improve data quality.

TriFinance helps organizations define critical data elements, establish quality rules and KPIs, and embed ownership within a clear governance framework. This enables data quality issues to be identified, assigned and resolved in a controlled manner.

Our pragmatic data quality model focuses on key dimensions such as completeness, uniqueness, timeliness, validity, accuracy and consistency. Through Power BI dashboards, organizations gain clear visibility into data quality issues and priority areas for improvement. This transforms data quality from a technical challenge into a business-driven process that strengthens trust in reporting, analytics and AI-ready data.

Typical focus areas 

• Definition of critical data elements, quality rules and data quality KPIs. 
• Definition of data quality dimensions such as completeness, uniqueness, timeliness, validity, accuracy and consistency for selected master data domains and critical data elements.
• Development of Fabric data model and dashboards to monitor data quality checks and make data quality issues visible to business and data owners. 
• Use of data quality dashboards as an operational governance instrument to support follow-up, ownership and continuous improvement

TriFinance combines finance, reporting and data expertise to turn data management and governance into tangible business value. We understand both the business requirements behind reporting, FP&A, consolidation, ESG and operational analytics, and the technology needed to support them.

By bridging the gap between business and IT, we help define KPIs, ownership models and reporting needs, while translating these into data models, governance processes, Power BI solutions and platform architectures. Our consultants can advise, design, implement, train and support, ensuring practical and sustainable adoption.

We take a pragmatic approach to governance, focused on improving transparency, reducing duplication and increasing trust in data. This creates a strong foundation for scaling Power BI, Microsoft Fabric, self-service analytics and AI, while maintaining clear ownership and data quality.
The impact for clients 

• More trusted reporting through clear ownership, definitions and data quality controls. 
• Less duplication of reports, datasets and business logic across the analytics landscape. 
• Better visibility on Power BI usage, access, lineage, licenses and refresh performance. 
• A stronger operating model for data ownership, governance decisions and user enablement. 
• A governed foundation for Microsoft Fabric, Power BI, performance management and AI-ready data.