Mars Enhances Workforce Analytics with Genie Agents and Unified Data
Through the implementation of Genie Agents, Mars is transforming its HR analytics landscape, allowing business users to swiftly access comprehensive employee data with natural language queries. This shift promises to elevate decision-making capabilities and drive operational efficiency.
Key Facts
- Mars' unified HR data model enhances decision-making speed, reducing analyst dependency by days.
- Genie Agents reveal cross-domain insights, breaking silos and improving organizational data access.
- Transition to Unity Catalog strengthens data governance, ensuring compliance while enabling analytics.
- Improved accuracy in Genie Agents boosts reliability, mitigating risks of data misinterpretation.
- Strategic focus on predictive insights indicates Mars' commitment to proactive workforce management.
Summary
Summary
Mars, a family-owned company known for brands like Snickers and M&M’s, faced challenges with siloed HR data that hindered analytics and decision-making. To address this, they implemented Genie Agents on Azure Databricks, creating a unified data model for workforce intelligence. As a result, business users can now access insights quickly, significantly reducing the time spent on data requests.
Background
Mars operates in the consumer goods industry and has expanded through acquisitions, including Kellanova, the maker of Pringles and Pop-Tarts. Prior to deploying Genie Agents, their people analytics team had integrated data from various HR sources into Azure Databricks but struggled with data unification. Dashboards were isolated by domain, and business translators often relied on data analysts, leading to delays in obtaining answers to their questions.
Challenge
The primary challenge was the lack of a unified view of associate data, which made it difficult for business users to explore and analyze HR information effectively. Mars aimed to create an associate 360—a comprehensive, governed view of every associate from hire to retire that could be accessed in natural language.
Solution
Mars transitioned its HR data to Unity Catalog for improved governance and security. Collaborating with Tiger Analytics, they developed a common data model that integrated various HR domains, including recruiting, performance, compensation, and learning. Genie Agents were built to enable business translators to ask questions directly, eliminating the need for lengthy analyst involvement. The system was designed to understand Mars-specific terminology and provide relevant insights.
Results
With Genie Agents, business translators can now obtain answers to complex HR questions almost instantly, instead of waiting days for data analysts. The common data model allows users to ask cross-domain questions and analyze results across different organizational levels. In a demonstration, Genie successfully interpreted vague prompts and provided detailed insights, showcasing its ability to deliver multi-year trends and answer follow-up questions.
Key Insights
The deployment of Genie Agents highlights the importance of unifying data across domains to enhance decision-making. By enabling natural language queries, organizations can empower business users to access insights directly, reducing reliance on data analysts and improving response times. Ensuring data governance through tools like Unity Catalog is crucial for maintaining security while facilitating analytics.
Customer Testimonial
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Key Concepts
Definitions
- Genie Agents
- AI-driven tools that allow business users to query HR data in natural language, providing quick insights without needing data analysts.
- Unity Catalog
- A governance tool that ensures data security and lineage, allowing users to access only the data they are permitted to see.
- common data model
- A unified framework that organizes data across various HR domains, facilitating comprehensive analysis and reporting.
- people analytics
- The use of data analysis techniques to understand and improve workforce performance and decision-making.
- natural language processing
- A branch of AI that enables computers to understand and respond to human language in a meaningful way.
Use Cases
- →Analyzing cross-domain HR data
- →Improving data access for business translators
- →Providing insights on employee attrition
- →Tailoring data access for different HR personas
- →Enhancing data accuracy and performance
- →Exploring predictive insights in partnership with legal
Frequently Asked Questions
What are Genie Agents?
Genie Agents are AI tools developed by Mars that enable business users to ask questions about HR data in natural language. They provide quick responses, significantly reducing the time previously required for data analysts to generate insights.
How does Unity Catalog enhance data governance?
Unity Catalog ensures that users can only access data they are authorized to see, which helps maintain data security and compliance. It also tracks data lineage, providing transparency about data sources and transformations.
What is the purpose of a common data model?
A common data model serves to unify disparate HR data sources into a single framework, allowing for comprehensive analysis across various domains. This model enhances the ability to generate insights and reports that are consistent and reliable.
How does Mars plan to improve Genie Agents?
Mars plans to enhance Genie Agents by improving visualizations and fine-tuning experiences tailored to specific HR and finance roles. They also aim to explore predictive and prescriptive analytics to provide deeper insights.
What benefits does natural language processing provide in HR analytics?
Natural language processing allows users to interact with HR data using everyday language, making it easier for non-technical users to extract insights. This capability streamlines the data analysis process and improves accessibility for business users.