The Data Challenge: Legacy OLAP Meets Modern Cloud Demands
For years, this home improvement leader had invested in self-service analytics. Business teams were empowered to build dashboards, analyze data, and act quickly, but only to the extent their systems allowed. Unfortunately, their legacy on-premises OLAP solution had become a bottleneck. As the company scaled and data complexity grew, it became increasingly clear that the existing architecture couldn’t deliver on the promise of agile, enterprise-wide analytics.
Key pain points included:
- Outdated cube technology that slowed down data delivery
- Inconsistent metrics across dashboards, reports, and departments
- Limited staff expertise in supporting legacy systems and manual data prep
- Slow development cycles that delayed time-to-insight
- Growing demand for Excel access to governed enterprise metrics
- Increased interest in AI and natural language query capabilities, without a reliable foundation to support them
Even after transitioning to BigQuery to modernize the data warehouse layer, the organization lacked the governance, consistency, and usability required to operationalize data across the enterprise.
“We were slow to market, and the legacy technology was hard to support. It wasn’t designed for cloud scale or modern analytics.”
— Sr. Director of Technology, Data & Analytics
The Turning Point: Introducing a Semantic Layer
The retailer chose AtScale to implement a universal semantic layer, an architecture designed to centralize metric definitions and streamline analytics access across tools and teams. With AtScale, governed metrics in BigQuery could be modeled once and reused everywhere, including in business-friendly tools like Excel.
The primary objective was to deliver fast, accurate, and consistent data access for users across merchandising, finance, operations, and beyond without requiring SQL knowledge or IT dependency.
“With AtScale, we reduced complexity, sped up development, and gave business users the data access they needed, no SQL required.”
— Sr. Director of Technology, Data & Analytics
AtScale’s capabilities aligned perfectly with the retailer’s enterprise-scale requirements:
- Cloud-native OLAP on BigQuery with no data movement
- Semantic modeling to define business metrics centrally
- Excel integration for frictionless business user adoption
- AI-ready architecture that supports natural language access via API
- Advanced query optimization and caching for sub-second response times
- Support for hundreds of attributes and dozens of business dimensions
Business Impact: Performance, Governance, and Agility
The results were immediate and measurable. The semantic layer became the backbone of analytics for nearly every line of business. Today, users across marketing, e-commerce, finance, and store operations rely on AtScale to power their insights, whether they’re in dashboards, Excel spreadsheets, or conversational interfaces.
Highlights of the Impact:
- 80% of queries now complete in under 1 second, delivering near-instant access to enterprise metrics
- A 20+ TB semantic cube supports decision-making across the enterprise
- Hundreds of Excel users access governed data daily, minimizing IT support load
- Significant reduction in development time and manual data prep
- Consistent KPIs and definitions across departments, tools, and use cases
- Foundation in place for AI and NLQ, enabling future-ready innovation
The company’s new analytics stack supports faster time to insight, improved accuracy, and greater adoption of data-driven decision-making. Perhaps most importantly, it allows business users to work with trusted data in the tools they already use, without sacrificing governance or performance.
Unlocking the Power of Natural Language Query
As artificial intelligence reshapes how enterprises interact with data, this home improvement leader is ahead of the curve. The company is actively embedding natural language query capabilities into its analytics ecosystem, enabling employees to ask questions about their data in everyday language and instantly get accurate, governed answers.
Rather than relying on brittle natural language-to-SQL translations, the company routes queries from large language models (LLMs) directly to the AtScale semantic layer via API. This architecture ensures that AI-generated responses are based on the same curated and governed metrics used by human analysts.
This approach increases trust in AI insights and enables compliance and audibility at scale, something critical in regulated and complex enterprise environments.
“We’ve tested many approaches. Natural language to our semantic layer is by far the most reliable and the best foundation for enterprise AI.”
— Sr. Director of Technology, Data & Analytics
Future-Proofing the Data Stack
What began as an effort to modernize legacy analytics infrastructure has evolved into a comprehensive data strategy. The organization’s implementation of AtScale is not just solving today’s reporting needs; it’s enabling tomorrow’s innovation.
By aligning AI tools, self-service platforms, and enterprise governance under a single semantic framework, the company has positioned itself to:
- Scale analytics across thousands of users
- Democratize access to insights
- Maintain metric consistency across all tools and channels
- Enable explainable and auditable AI
- Support real-time, intelligent decision-making
With AtScale, business and technical teams now speak the same language, literally and figuratively. Whether someone is building a pivot table in Excel or asking a chatbot for sales trends, they interact with the same trusted semantic layer.
Conclusion
This case study is a clear example of how leading enterprises can overcome the limitations of legacy systems and unlock their data’s full potential. AtScale’s semantic layer has delivered measurable impact across speed, scale, governance, and AI readiness for this home improvement giant.
The semantic layer now acts as the connective tissue between BigQuery and the enterprise’s most important use cases, from Excel-based financial modeling to AI-powered natural language querying. By centralizing business definitions and decoupling analytics from infrastructure, the company has built a data stack that is agile, secure, and ready for the future.
In a world where data is everywhere, the ability to define, trust, and consistently access it is a competitive advantage—and this retailer has found it in AtScale.
