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Data & AI
Data Preparation & Modeling
Consulting services
Trusted AI and analytics start with data modeling
Data modeling transforms raw data into a structured, meaningful foundation for analysis. It defines how data is organized, connected and understood so your business can rely on consistent, accurate insights.
Data modeling for enterprise analytics ensures that reporting is scalable, performant and aligned to business definitions. By creating a shared language across teams, it reduces time spent reconciling data and enables efficient, confident decision-making at scale.
Data modeling for AI ensures outputs are trustworthy and grounded in business context. By defining business relationships, logic and meaning, it gives AI the structure it needs to interpret data correctly —not just access it. This reduces misinterpretation and is essential for delivering reliable, decision-ready insights.
For enterprise data:
- Establishes consistent definitions for metrics, KPIs and dimensions
- Improves performance and scalability of reporting and dashboards
- Reduces manual data preparation and duplication
- Enables governed self-service analytics across the business
For AI:
- Provides the business context AI needs to interpret data correctly
- Ensures consistent logic across AI, reports and dashboards
- Reduces hallucinations and misinterpretation of business metrics
- Increases trust in AI-generated insights and recommendations
How we can help
We build high performant data models that structure enterprise data and give AI the business context it needs to deliver trusted insights.
- Define and standardize key business metrics to ensure consistency across reporting and AI use cases.
- Design and optimize data architectures in Microsoft Fabric, integrating, transforming and modeling data across lakehouse and warehouse environments.
- Build scalable semantic models in Power BI and Fabric that support both enterprise reporting and AI-driven analysis.
- Build AI-ready business context using Microsoft Fabric semantic models, knowledge graphs and ontologies so AI can interpret your data in business terms.
- Improve performance and reliability by tuning models, optimizing queries and resolving bottlenecks across the data stack.
- Establish data context and governance through cataloging, documentation and metadata management to support both users and AI systems.
Spotlight
Will your data governance hold up to AI? Read key practices for ensuring trusted data for AI
Webinars, blogs, videos & whitepapers
Fabric enablement & training
Knowledge transfer is the best way to ensure adoption and ongoing self-sufficiency. We offer flexible ways to build capability so you and your team can master Microsoft Fabric and Power BI.
Our enablement approach includes training to build skills as well as options for ongoing support that can adapt as new challenges arise. We tailor every engagement to your goals, roles and experience levels.