Power BI
Executive and operational reporting with performance, accessibility, and a layout designed around decisions rather than available charts.
Data and analytics
Reporting people actually act on.
We build the layer between raw data and decisions: modelled, documented, governed semantic models in Power BI and Microsoft Fabric, with executive and operational reporting designed around the questions your teams are really asking.
The problem
What usually brings people here
If more than one of these is familiar, they are probably connected. We look for the underlying cause before proposing a build.
- Two reports, two answersThe same measure is calculated differently in different reports, so meetings become an argument about the numbers rather than the decision.
- Dashboards nobody opensReports were built around available fields instead of business questions, so they look complete and change nothing.
- Manual data preparationSomeone spends the first two days of every month exporting, joining, and correcting spreadsheets before reporting can start.
- Data that is not ready for AIAI initiatives stall because the underlying data lacks definitions, ownership, quality rules, and consistent identifiers.
Capabilities
What we build
Microsoft Fabric
Lakehouse and warehouse design, pipelines, and workspace structure that give analytics a durable foundation.
Semantic models
Certified, documented models with agreed measure definitions, row-level security, and predictable refresh behaviour.
Data integration and quality
Reliable ingestion from business systems with validation, reconciliation, and visible data freshness.
Governance and adoption
Workspace strategy, certification, access model, and the enablement work that decides whether reporting is used at all.
AI-ready foundations
Definitions, ownership, lineage, and quality rules that make your data usable by copilots and agents, not just by report authors.
Delivery approach
How the work runs
A repeatable path from an unclear problem to a system someone owns. The lifecycle decisions happen early, where they are cheap.
Discover
Identify the decisions the business needs to make, the questions behind them, and the systems that hold the underlying data.
Design
Agree the semantic model, measure definitions, security model, refresh strategy, and the report structure that supports those decisions.
Build
Implement pipelines, model, and reports with source control, validation against source systems, and performance testing on real volumes.
Evolve
Monitor usage and refresh health, retire unused content, and extend the model as new questions appear.
Technology ecosystem
Product names are used descriptively. The right combination is decided during design, not assumed at the start.
- Power BI
- Microsoft Fabric
- Dataverse
- Azure Data Factory
- Azure SQL
- Power Query
- DAX
Outcomes
What changes afterwards
One agreed set of numbers
Shared definitions in a certified model end the reconciliation debate before the meeting starts.
Time returned to the team
Automated ingestion and refresh replace the manual preparation cycle that consumed the start of every period.
Reporting that gets used
Reports designed around real decisions attract genuine usage rather than a launch spike.
A foundation AI can build on
Documented, governed data is what makes later copilot and agent work practical instead of speculative.
Related work
Data & Power BI • Azure
Executive Intelligence Hub
Regional services group
A certified semantic model and executive reporting layer that ended the reconciliation debate, replacing conflicting spreadsheets with one agreed set of measures.
View case study
Power Platform • Automation
Lifecycle Operations Platform
Multi-site operations business
A connected Power Platform solution replacing spreadsheet and inbox handoffs with a single request, approval, and document lifecycle that operations leaders can see end to end.
View case study
Related insights
Data & Analytics
What "AI-ready data" actually means
Most stalled AI projects are not blocked by the model. They are blocked because nobody can say authoritatively what the underlying data means.
Ahmed Salih4 min read
Questions
Frequently asked
Power BI alone is often sufficient when your data volumes are moderate and the sources are already reasonably clean and accessible. Fabric becomes worthwhile when you need a shared data foundation across several teams, larger volumes, more complex transformation, or a single governed home for data that currently lives in scattered exports. The decision should follow the data estate, not the product roadmap.
By moving the definition out of individual reports and into a shared semantic model. When 'active customer' or 'net revenue' is defined once, documented, and certified, every report inherits the same logic. It is as much a governance change as a technical one — someone has to own each definition and approve changes to it.
Usually yes. A typical assessment looks at model design, measure duplication, refresh performance, workspace and access structure, and actual usage. That normally produces a short list of high-value fixes — consolidating duplicate models, correcting relationships, fixing refresh, retiring unused reports — well before anything needs rebuilding from scratch.
It means data an AI system can use without inventing its own interpretation: agreed definitions, a clear owner per domain, consistent identifiers across systems, documented lineage, and quality rules that are checked rather than assumed. Most stalled AI projects are not blocked by the model — they are blocked because nobody can say authoritatively what the underlying data means.
Let's talk
Let's talk about Data & Analytics.
Bring the problem rather than a specification. A short conversation is usually enough for us to tell you what we would do first — and whether we are the right people to do it.
Prefer email? info@aqlyst.ai