Reporting and Data Visualization Services

We work with Power BI, Tableau, Looker, Qlik and others, and recommend based on your context. If you already use Microsoft 365 and Azure, Power BI and Fabric are often a natural fit. Looker suits teams that want a code-based semantic layer on BigQuery. Tableau remains strong for visual exploration. We consider existing licenses, skills, data platform, embedding needs and total cost, and we are equally comfortable improving the tool you already have.

A semantic layer is a governed model that sits between your data platform and reports. It defines tables, relationships, hierarchies, security and calculated measures such as revenue or churn once, in one place. Every report built on it uses the same logic, so numbers agree across dashboards and analysts. It also improves performance and makes self-service safer, because users build on certified definitions instead of writing their own calculations.

A focused dashboard suite, for example executive and sales reporting on a clean data source, can be delivered in four to eight weeks. If the semantic model and data pipelines need to be built first, plan for two to four months for the first release. We deliver iteratively, showing prototypes within the first weeks, so stakeholders can shape the design long before final sign-off.

We implement row-level security so users see only their region, entity, customers or team, and object-level security where certain columns or measures must be hidden. Security is defined in the semantic model, not in individual reports, which makes it consistent and auditable. We integrate with your identity provider, such as Microsoft Entra ID, and apply least-privilege principles for workspace and dataset access.

Yes. We start with a performance and governance review that identifies the root causes, typically inefficient data models, complex calculations, unnecessary columns, missing aggregations or refresh bottlenecks. We then prioritize fixes by user impact. Many improvements, such as remodeling a dataset into a star schema or optimizing key measures, deliver significant speed gains without changing how the report looks to users.

We combine clear structure with enablement. That means defined workspace tiers for personal, team and certified content, deployment pipelines for production reports, a certification process for shared datasets and usage monitoring to spot duplication. Alongside these guardrails we train analysts, publish design and modeling guidelines, and support a community of practice, so people know how to build good reports rather than simply being restricted.