Executive dashboards fail when the data underneath them is a pile of one-off pipelines. This practice designs the platform those reports can trust.
Organizations modernizing fragmented pipelines and scaling governed enterprise analytics.
Microsoft Fabric is often the right platform when Power BI, the lakehouse, and governance need to live in one estate. It is not automatic. We design the reference architecture, ingestion, and quality controls around the business questions first, including a path for teams that cannot move every workload at once.
These are directional outcomes. They describe the point of the work, not a guaranteed metric.
Specialist work at Carlo Solutions follows the same four steps as the rest of the practice.
We baseline architecture, governance gaps, and reporting friction against business priorities.
We design a target state with clear standards for reliability and ownership.
We deploy reusable frameworks and implementation patterns that shorten delivery.
We transfer capability through role-based coaching, governance routines, and operating playbooks.
Look at Fabric when Power BI, storage, and transformation are split across tools that nobody fully owns, or when a lakehouse would remove repeated copies of the same data. Stay put when the current warehouse is reliable, cost is understood, and the pain is really the semantic model or the reports. Platform change is justified by reliability and ownership, not by a product announcement.
We start with the decisions that are blocked, then trace the sources, copies, and manual steps behind them. The first architecture is usually a short list of certified domains, not a plan to land every table in the company. Ingestion, transformation, and quality checks are designed for those domains so Power BI has a stable place to read from.
We work with the team you have. Carlo Solutions is a specialist practice, not a staff-augmentation bench. The engagement is there to set architecture, patterns, and controls your engineers and analysts can run. Enablement is part of the work so the platform does not depend on an outside firm staying forever.
A reference architecture names where raw data lands, how it is transformed, which tables are certified for reporting, and how Power BI semantic models should read them. It also covers security boundaries, lineage, and the data quality checks that decide whether a pipeline is fit to refresh. The diagram is only useful if those rules are specific enough to build from.
Security follows the business boundary: who can see a domain, and where row-level or object-level rules belong. Lineage is there so a broken executive number can be traced to a pipeline, not debated in a meeting. Data quality checks sit on the certified tables that feed semantic models, with a clear owner when a check fails.
A good design states the platform choice and the exit points. Medallion layers, certified tables, and semantic models should have explicit contracts so a later change of engine is possible. We will recommend Fabric when it fits the Microsoft estate you already run. We will not pretend every workload belongs there on day one.
Expect a platform that can grow without a new pipeline style for every request, more reliable data for the reports that matter, and less operational risk when someone changes a source. We do not promise a universal percentage improvement. The result shows up as fewer conflicting numbers and a refresh path the team can explain.
Tell us which decisions are stuck, what Power BI or Fabric estate you already have, and who will own the result internally.