CVS Health Transforms Data Access and Forecasting with Genie Agents
With the implementation of the Genie Agent, CVS Health empowers users to quickly obtain insights on-demand, transforming the way data is accessed and utilized in retail decision-making.
Key Facts
- CVS Health reduced ad hoc request turnaround from 1-3 days to minutes, enhancing operational efficiency.
- Transition to a pull model with Genie Agents boosts data accessibility, improving decision-making speed.
- Improved forecast accuracy from time-series models indicates a competitive edge in retail demand planning.
- Data governance in Unity Catalog ensures quality insights, minimizing risks associated with poor data usage.
- Genie Agents democratize data analysis, empowering more employees and potentially reducing reliance on data scientists.
Summary
Summary
CVS Health faced challenges in efficiently answering demand and labor forecasting questions from business partners. By implementing a retail-specific Genie Agent using Azure Databricks, they transformed their data inquiry process, significantly reducing response times. The result was a faster, more reliable way to derive insights, enhancing trust and collaboration across teams.
Background
CVS Health, a major player in the healthcare and retail pharmacy industry, needed to forecast demand and labor for its numerous stores. Prior to deploying the Genie Agent, the data science team was inundated with requests from business partners seeking explanations for various sales performance issues. Each inquiry required extensive manual analysis, leading to delays and inefficiencies.
Challenge
The primary challenge was the lengthy turnaround time for addressing business partners' questions regarding sales forecasts. Requests could take one to three days to resolve, as data scientists had to analyze data store by store, testing hypotheses and generating reports. This bottleneck hindered timely decision-making and strained resources.
Solution
CVS Health developed a retail-specific Genie Agent that operates on governed data within Unity Catalog, leveraging Azure Databricks. This solution allows users to ask questions in natural language, either from a dashboard visualization or directly through the Genie Agent. The system provides summarized answers by investigating across multiple data sources, transitioning from a push model of report generation to a pull model where users can directly query the data.
Results
The implementation of the Genie Agent drastically reduced the turnaround time for ad hoc requests from one to three days to just a few minutes. In a demonstration using synthetic data, Genie completed a comprehensive investigation into sales performance in mere minutes. This efficiency has allowed data scientists to focus more on developing time-series models, contributing to improved forecast accuracy and fostering greater trust among stakeholders.
Key Insights
The deployment of the Genie Agent highlights the importance of data governance as a foundational step before implementing AI solutions. By ensuring that data is well-structured and contextually relevant, organizations can empower users to perform high-quality analysis independently, thereby enhancing overall productivity and collaboration.
Customer Testimonial
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Key Concepts
Definitions
- Genie Agent
- A retail-specific tool developed by CVS Health that investigates data sources to provide summarized answers to business questions.
- Unity Catalog
- A data governance solution that allows for the registration and management of data tables, ensuring data quality and context.
- ad hoc requests
- Spontaneous inquiries made by users for specific data insights, often requiring quick turnaround.
- data governance
- The management of data availability, usability, integrity, and security within an organization.
- self-service analytics
- An approach that allows users to access and analyze data independently without relying on data scientists.
Use Cases
- →Investigating sales performance
- →Comparing store metrics
- →Enhancing forecast accuracy
- →Providing insights from multiple data sources
- →Improving stakeholder communication
- →Streamlining data analysis processes
Frequently Asked Questions
What is the purpose of the Genie Agent?
The Genie Agent is designed to investigate data sources and provide explanations for forecast and sales performance. It allows users to ask questions in natural language, making data insights more accessible.
How has CVS Health improved its data analysis process?
CVS Health has shifted from a push model to a pull model, enabling business users to directly query the Genie Agent. This change has significantly reduced the turnaround time for ad hoc requests.
What role does Unity Catalog play in CVS Health's data strategy?
Unity Catalog serves as a data governance framework that registers tables and validates AI-generated descriptions. This ensures that the data used by the Genie Agent is accurate and contextually relevant.
How does the Genie Agent enhance user experience?
The Genie Agent enhances user experience by allowing users to ask questions directly from dashboards and keeping context across follow-ups. This reduces the need for complex dashboards and improves accessibility to insights.
What benefits has CVS Health seen from using Genie Agents?
CVS Health has experienced faster explanations for data inquiries, improved forecast accuracy, and increased trust from stakeholders. Data scientists can now focus more on developing models rather than handling repetitive queries.