Curated data models and role-based dashboards that give leadership a single source of truth and a launchpad for AI.
Business intelligence and analytics consulting that connects day-to-day operations with strategic decisions.
Modern BI enablement that builds curated data models, reliable pipelines and compelling role-based dashboards aligned to your decisions, and ready to support AI. We design for governance from day one so the BI stays trustworthy as the data and the questions evolve.
The most widely adopted BI platforms in enterprise are Microsoft Power BI (best for organizations already on Microsoft 365 and Azure), Tableau (strong visualization and self-service analytics), and Looker (strong for organizations on Google Cloud with complex data models). The right choice depends on your existing cloud infrastructure, IT capability and budget. More important than the tool choice is the data model and governance underneath it: a well-governed semantic layer on any of these platforms outperforms a poorly governed implementation of the theoretically optimal tool.
A real-time enterprise performance dashboard is built on four components: a data integration layer (pulling from operational platforms, ERP, and other source systems on a defined cadence, daily or real-time where possible); a curated data model (resolving naming and definition conflicts between systems so a shared metric has one agreed definition across all platforms); a semantic layer (exposing consistent metrics to all BI tools and users so everyone queries the same logic); and a dashboard layer (role-specific views for frontline, operational and executive audiences). The cadence of refresh is driven by use: executive dashboards need daily; operational dashboards benefit from real-time where systems support it.
The most valuable enterprise KPIs fall across four dimensions. Financial: margin at completion, cost variance, revenue recognized versus forecast, cash flow against plan. Operational: planned versus actual milestone completion, cycle time by process, throughput, and process efficiency ratios. Risk: incident rate, compliance adherence rate, SLA breach frequency. Quality and performance: request volume and cycle time, approval rates, defect and error rate, supplier performance score. CFOs specifically benefit from a unified cash flow view and a cost-at-completion forecast that refreshes automatically from ERP and operational data.
Consolidation follows three steps. First, a data integration layer: API connectors pull data from your operational platforms, ERP and other source systems on a defined cadence into a central data platform (typically a cloud data lakehouse). Second, an entity resolution layer: a data model or matching logic resolves the fact that a record, cost code or entity is named differently in each source system, creating a single unified reference. Third, a semantic layer: consistent metric definitions are defined once and exposed to all BI tools so every dashboard uses the same calculation. Without the entity resolution step, consolidation dashboards produce numbers that different teams dispute because their source differs.
Yes, in two specific ways. First, AI can improve the accuracy of cost-at-completion forecasting by incorporating signals that traditional analysis misses: historical cost variance patterns by process type, partner risk scores from prior engagements, and activity velocity signals that are leading indicators of future cost growth. Second, AI can automate the data preparation and exception identification that occupies most of an analyst's time: automatically flagging areas where actual performance has diverged significantly from forecast, so the team focuses on the outliers rather than reviewing every line item.
A BI implementation for a mid-size enterprise (multiple active business units, three to five source systems, two to four user roles) typically takes eight to sixteen weeks from kickoff to initial dashboards live in production. Iterative delivery is standard: a first working dashboard covering the highest-priority KPIs is typically available in four to six weeks, with additional roles and metrics added in subsequent sprints. Governance and adoption work continues for two to three months after the technical build to ensure dashboards are trusted and used consistently.
Business intelligence consulting firms bring three capabilities that general IT consultants cannot match: deep domain knowledge of the KPIs that matter in your industry and how they are calculated; pre-built integration patterns for major enterprise platforms that eliminate months of bespoke data engineering; and change management experience with leadership teams who need insight and trust, not raw data dumps. The result is a BI platform that earns trust because it reflects how your business actually measures performance.
Microsoft business intelligence consulting for an enterprise centers on Power BI as the visualization layer, with Azure Synapse or Azure Data Factory handling data integration and transformation. For Microsoft 365-aligned organizations, this means: connecting your operational systems to Azure via their REST APIs; building a semantic model in Power BI that resolves cross-system naming conflicts; deploying role-level security so individual contributors see their scope while executives see the portfolio; and embedding Power BI reports inside Teams and SharePoint for daily use. We also connect Microsoft Copilot capabilities to the semantic layer so executives can query the BI platform in natural language.