The Problem: Fragmented Data and Conflicting Metrics Across Retail Systems
As Bluemercury expanded, its data ecosystem became increasingly complex. Different business functions relied on separate tools and data sources:
- Finance pulled reports from ERP systems
- Marketing used CRM platforms
- Store operations relied on point-of-sale (POS) data
Each team used its own logic to define core business metrics like net sales, gross revenue, and margin. These inconsistencies caused significant problems across the business.
Challenges Bluemercury Faced:
- Inconsistent business metrics leading to conflicting reports in executive meetings
- No unified data source for decision-making across departments
- Lack of data governance and unclear access controls
- Limited self-service capabilities, creating reporting bottlenecks
- AI and LLM readiness issues, due to unaligned training data
Without a standardized data layer, teams spent hours reconciling numbers instead of taking action. Analysts were burdened with ad hoc requests, and leadership lacked confidence in the presented data.
“Every business function had their own language when it came to reading the data. That made consistent measurement and trust nearly impossible.”
– Praful Deshpande, Managing Director of Data and Technology, Bluemercury
The Solution: Implementing a Governed Semantic Layer with AtScale
To overcome these data challenges, Bluemercury partnered with AtScale to implement a centralized semantic layer, a governed, business-aligned data model that delivers a single source of truth across all reporting tools and functions.
Goals of the Semantic Layer Initiative:
- Standardize metric definitions across finance, marketing, and store ops
- Improve data governance and access control
- Enable governed self-service analytics using Power BI and Tableau
- Ensure AI/LLM models are trained on consistent, accurate data
- Reduce data silos and support enterprise-wide decision-making
“The semantic layer became our system of record for truth. It wasn’t just a technical solution, it was a business transformation.”
Implementation Approach: A Phased, Business-Driven Rollout
With its semantic layer initiative, Bluemercury followed a focused strategy to drive adoption and long-term success.
- Engage Subject Matter Experts (SMEs) Early
Cross-functional SMEs were brought in to define and align on high-priority metrics, beginning with sales and margin, two areas with the most inconsistencies. - Start with a Narrow Use Case
The team focused on three critical KPIs: net sales, gross sales, and margin. Aligning these metrics across departments created a foundation of trust in the data. - Establish Governance and Ownership
Each metric was assigned an owner. Role-based access controls were implemented to ensure consistent definitions and reduce unauthorized changes. - Integrate with Power BI and Tableau
AtScale’s semantic layer was connected to Bluemercury’s BI tools, enabling business users to consume governed data directly within their preferred reporting platforms. - Track Adoption and Engagement
The team used Tableau’s built-in usage analytics to monitor:- Report views
- Login frequency
- Dashboard creation using governed metrics
These insights helped Bluemercury measure success and increase organizational buy-in.
Results: Trusted Data, Higher Adoption, and Accelerated AI Readiness
Bluemercury’s implementation of AtScale’s semantic layer delivered measurable benefits across data trust, self-service analytics, and AI enablement:
80% of Enterprise Data Now Integrated
The semantic layer currently includes 80% of Bluemercury’s business data, with full coverage expected within six months.
Eliminated Conflicting Metrics
All teams now use a single definition for core metrics like sales and margin, eliminating discrepancies between finance, marketing, and store reports.
Governed Self-Service Across BI Tools
Users access consistent data via Power BI and Tableau without relying on analysts, drastically reducing turnaround time for insights.
AI-Ready Data Foundation
Bluemercury can train large language models (LLMs) and deploy generative AI (GenAI) capabilities using a consistent, governed data layer.
“Once people understood that the semantic layer gives them a trusted version of the truth and that they didn’t have to manually reconcile reports anymore, the adoption just skyrocketed,”
– Praful Deshpande, Managing Director of Data and Technology, Bluemercury
Semantic Layer as a Living Product
Bluemercury doesn’t treat its semantic layer as a one-time implementation. It’s a dynamic product, continually evolving as new data sources, metrics, and use cases are added to the platform.
The semantic layer now serves as a key pillar in Bluemercury’s modern data strategy, supporting business agility, compliance, and innovation.
“Semantic layers aren’t just architectural upgrades, they’re business enablers. Without it, your data team is stuck managing chaos instead of delivering insights.”
– Praful Deshpande, Managing Director of Data and Technology, Bluemercury
Conclusion: A Blueprint for Modern Retail Analytics
Bluemercury’s success story highlights the growing importance of a semantic layer in retail data transformation. By partnering with AtScale, the company eliminated data silos, streamlined reporting, and unlocked trusted analytics at scale.
Today, the AtScale semantic layer powers cross-functional collaboration, supports AI innovation, and ensures that governed, consistent, and real-time insights back every decision for Bluemercury.
Ready to scale your data trust and unlock governed self-service analytics? Explore how AtScale can help.
