Power BI works fine in demos.
Production is where it falls apart.

We start with an audit of what exists. Some datasets and reports are worth refactoring; others are faster to rebuild correctly. We make that call report by report based on model quality and business criticality, not a blanket policy. The goal is a governed environment, not a clean-slate rewrite for its own sake. Your team keeps reports that work and we fix the ones that do not.

Pro licenses cover basic sharing within the organization. Premium Per User adds paginated reports, larger dataset sizes, and deployment pipelines at the user level. Fabric is Microsoft's broader data platform that includes Power BI alongside data engineering and data science workloads. Which tier fits you depends on dataset size, refresh frequency, audience size, and whether you need Fabric's lakehouse or pipeline capabilities. We assess that during the audit and give you a straight recommendation.

Usually both, and they need to be diagnosed separately. Slow import-mode reports often point to a poorly structured data model or expensive DAX measures. Slow DirectQuery reports usually point to the underlying database query performance or an inappropriate use of DirectQuery where import mode would serve better. We profile both layers - the Power BI model and the source query - before recommending a fix, because treating one without the other rarely solves the problem.

The structural answer is a certified dataset strategy using Power BI's endorsement feature combined with workspace governance that discourages or restricts ad hoc dataset creation. Teams build reports on top of certified datasets they do not own, rather than building their own from scratch. This requires both a technical setup and an organizational agreement about who owns which datasets. We help with both sides.

We work across the full stack - Dataflows Gen2, dataset refresh orchestration, and the report and semantic layer. If your organization is on Fabric, we also work with Fabric Lakehouses and the Fabric pipeline tooling as data sources for Power BI semantic models. The data preparation layer is often where the real problems originate, so we do not limit scope to the report surface.

Yes. Power BI Embedded uses a different licensing model - capacity-based rather than per-user - and requires a different authentication approach using service principal or master user credentials. Row-level security configuration is also more involved in an embedded context. We have built embedded implementations and can scope that work separately from an internal BI engagement if needed.

A focused audit and data model rebuild for a mid-market firm typically runs four to eight weeks depending on the number of datasets, data sources, and reports in scope. A full governance and deployment pipeline setup on top of that adds time. We scope engagements after the audit so you have a realistic timeline before committing, not a number we invent during the sales conversation.