Alcon. Transitioning from Dashboards to Dialogue with Agentic Analytics

Alcon is the global leader in eye care products. Alcon sought a solution that would provide a unified data modeling and access framework. Their vision was to use conventional dashboards augmented by Agentic AI to enable self-service analytics and drive actionable insights

The Challenge: Discrepancy, Delay, and Digital Risk

Alcon, like many large enterprises, faced challenges stemming from pervasive dashboards and complex data structures:

• Metric Inconsistency: The same KPIs and metrics could have different definitions and calculation methodologies across various teams, leading to discrepancies and confusion.
• Generative AI Risk: Due to complex data schemas with many databases, tables, and fields, implementing GenAI posed a high risk of error and hallucination. Alcon also needed to maintain strict compliance with regulations like HIPAA and GDPR, requiring robust security
• Slow Time-to-Value: Users constantly requested questions that existing dashboards could not answer. Submitting new development requests, even using an Agile process, resulted in slow intake, development, and deployment cycles

The Solution: Cube's Unified Semantic Layer and Agentic Analytics Platform

The selection of Cube Core as their semantic layer provider was based on four core capabilities that aligned directly with Alcon’s requirements:

• Access Control: Cube enforces centrally managed data security and role-based access necessary for a highly regulated environment
• Centralized Data Model: Cube facilitates the centralized definition and management of business logic and KPIs
• Caching: Cube minimizes repetitive queries into the data lake/warehouses, performing aggregation to achieve faster performance
• APIs: Cube’s APIs allow seamless integration with downstream BI tools and, critically, with the Cube Agentic Analytics platform

By implementing Cube, Alcon defined multiple logical data representations—Cubes (for accounts, products, orders, etc.) and Views (logical combinations that expose data fields)—to centralize metric management and calculation definitions in one place

“Semantic SQL is a wonderful thing – it's really at the heart of the semantic layer. Without Cube’s semantic SQL, our data analysts might have to write 20 different queries for a single core business metric. With Cube, that metric is defined once in the data model, and every downstream tool uses that definition along with the associated calculation logic.”

— Dr. Jun Huang, Global Head of Data Science for Digital Health, Alcon

The Impact: Governance, Trust, and Accelerated Insights

Implementing Cube’s semantic layer and the Cube platform delivered immediate benefits, enabling semantic governance in action.

• Consistency and Trust: The unified definitions provided by Cube ensure that data consumers receive consistent results. When testing the new system, the GenAI chatbot's responses to KPI questions matched exactly what was displayed on the existing Tableau dashboard. This consistency ultimately built user trust
• Reduced Development Effort: The Agentic AI complements the dashboard, allowing users to ask questions beyond the dashboard's scope. This capability reduces the need to develop as many dashboards as before
• Improved Governance for AI: The well-constructed semantic layer brings the correct context to the AI agents, significantly reducing the risk of hallucination. Furthermore, Alcon uses the Agentic Analytics platform to set rules, ensuring the agent only provides responses using approved metrics, adding a necessary level of control