Five systems, five definitions of revenue, until one warehouse answers in plain English
The Challenge
Operationally complex organizations, from real estate portfolios and multi-clinic healthcare groups to manufacturers, restaurant chains and PE-backed software companies, keep their data in a different system for every function: ERP, CRM, EMR, marketing, billing, product and operations. The systems rarely share definitions, so even a simple question about revenue, margin or churn has several competing answers. Building an enterprise-wide view takes analysts days or weeks, and by the time it arrives the decision it was meant to inform has often been made.
The small BI team becomes the bottleneck. Ad hoc questions wait four to seven days in a queue, dashboards go unused because they never match the question being asked, and board questions go unanswered. Leadership cannot compare sites, spot leaks or see churn risk early, while sponsors push for an 8–10% cut in SG&A within 180 days. Traditional data programs take six to nine months, which is longer than the pressure allows.
Systems that do not talk to each other. Data sits in ERP, CRM, EMR, billing and operations tools with no shared definitions, so the same metric has several answers.
The enterprise view takes weeks. Analysts assemble cross-system reports by hand, and the answer arrives after the decision.
The BI team is a queue. Ad hoc questions wait four to seven days, dashboard adoption stays low and board questions go unanswered.
Savings exist but stay out of reach. Without a trusted view of freight, inventory, pricing or site performance, leadership cannot find the leaks that fund a cost-reduction target.
The Approach
The work starts with people, not tooling: leadership agrees on a handful of KPIs and what each one means. The data is then consolidated into a governed data warehouse with a semantic layer, built with AI-assisted data engineering that, in several cases, wrote the large majority of transformation code and documentation. Entity-resolution and business-key logic reconcile records across systems, and extraction is read-only so source systems are never disturbed. The result is a single source of truth on which everything else depends.
On top sits a natural-language query layer delivered where people already work: Slack, Teams, the web or an existing AI assistant. It generates governed SQL behind guardrails such as cost limits, PII redaction and role-based access, and returns tables, charts and short narratives with source lineage. Anomaly detection, churn and revenue prediction, and alerts that feed operational workflows, such as a missing pre-procedure record or a failed eligibility check, extend the same foundation through BI and analytics. Governance is treated as a product, with approved tools, access requests, usage dashboards and versioned context, and AI features are deliberately held back until the data foundation is proven.
Techniques: Entity resolution and business-key matching · Governed warehouse with semantic layer · Natural-language-to-SQL with guardrails · Anomaly detection and churn/revenue prediction · Alerts routed into operational workflows
AI role
AI turns plain-English questions into governed queries that return tables, charts and narrative with lineage, and proactively surfaces anomalies, trends and correlations that nobody asked about.
Automation scope: Enterprise-wide data unification and self-serve reporting across finance, operations and executive decision-making.
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