Use Case — Self-Service Analytics
The most expensive line in most data teams' backlog isn't a complex ML project — it's "can you pull a number for me?" Business users need data. Engineers write SQL. Everyone waits. A well-architected semantic layer and natural language query capability breaks that cycle permanently — putting governed, trusted data directly in the hands of the people who need it to make decisions.
This is a pattern we know well from production deployments. At a 12-million-user consumer platform, we built an enterprise semantic layer on AtScale that enabled non-technical teams across marketing, product, finance, and executive leadership to self-serve trusted data through Power BI, Looker, and Excel — without a single SQL query or engineering ticket. The BI backlog collapsed. The data team redirected to higher-value work.
The same architecture, applied with today's AI-powered natural language query tools, goes further still — letting users ask questions in plain English and get accurate, governed answers with full lineage back to the source data.
The Problem We Solve
Most organizations have plenty of data. What they lack is a governed, consistent layer that sits between that data and the tools business users work in every day. Without it, every question triggers an engineering request. Business users get inconsistent answers because different teams pull the same metric in different ways. The data team becomes a reporting factory — unable to focus on analytics that actually moves the business forward.
What We Build
A governed, multi-cloud semantic layer (AtScale, Azure Analysis Services SSAS Tabular, or dbt Semantic Layer) that defines your business metrics once and serves them consistently to every BI tool in your stack — Power BI, Looker, Tableau, ThoughtSpot, and Excel.
AI-powered query interfaces — ThoughtSpot, Power BI Copilot, or custom LLM implementations — that let business users ask questions in plain English and receive accurate, governed answers with citations back to source data. Built on top of a rigorous semantic model so answers are trustworthy.
A defined lifecycle for business metrics — from definition to certification to deprecation — so that "revenue," "churn," and "active users" mean the same thing to every team, in every report, every time. No more metric debates in Monday morning reviews.
Migration from third-party analytics tools (Google Analytics, vendor dashboards) to an integrated first-party data approach — establishing a single source of truth that's privacy-compliant, more accurate, and fully under your control.
We've built internal data literacy programs (including "SlickData University" for a major consumer platform) that enable employees across the organization to use data effectively — turning the semantic layer investment into cultural adoption, not just technical infrastructure.
Data quality monitoring and observability (Monte Carlo, Azure Data Quality) that catches anomalies, freshness issues, and schema drift before business users encounter them — so the data they're querying is always accurate and current.
Proven at Scale
At Slickdeals — a 12-million-user consumer platform — we designed and deployed an enterprise semantic layer on AtScale, connected to Snowflake and Databricks, serving Power BI, Looker, and Excel as certified analytics endpoints. Non-technical teams across marketing, product, finance, and executive leadership queried trusted data directly — no SQL, no engineering tickets, no waiting for a dashboard to be built.
The BI backlog was eliminated. The data engineering team redirected capacity from ad hoc reporting to building the ML and analytics systems that actually moved the business forward. The platform also replaced third-party analytics tools (Google Analytics and vendor dashboards) with an integrated first-party data model that gave the company more accurate, privacy-compliant insights directly from customer behavior.
The semantic layer work became recognized thought leadership — we delivered keynote addresses at the Semantic Layer Summit in 2022 and 2023, published a guest column in Retail IT Insights on the architecture patterns, and contributed to the AtScale blog on deployment strategy. The playbook is battle-tested at scale.
Let's Talk
Tell us about your current data stack, your BI backlog, and where the metric debates happen. We'll show you what a governed semantic layer changes.