Conversational Analytics with GenAI

Situation

A leading retail organization sought to accelerate decision-making by enabling business teams to access data and insights without relying on technical specialists. Marketing, measurement and strategy teams frequently depended on Data Scientists to answer business questions, creating delays that slowed campaign planning and operational decision-making.

The organization required a secure, cloud-native GenAI solution that could interpret natural language queries, analyze enterprise data and deliver accurate insights in real time.

Problem

• Business users relied heavily on Data Scientists for routine data requests and analysis
• Turnaround times for insights ranged from hours to weeks
• Limited self-service capabilities created operational bottlenecks
• Technical expertise was required to query enterprise datasets
• Manual reporting reduced business agility and slowed decision-making
• Data Science teams spent significant time addressing repetitive analytical requests

Solution

• Built a GenAI-powered conversational analytics platform on Google Cloud to enable natural language interaction with enterprise data
• Automated text-to-SQL generation, allowing users to retrieve insights without writing code
• Applied AI agents to interpret business questions, validate queries and generate accurate analytical responses
• Delivered automated visualizations and business-friendly summaries alongside raw data
• Integrated securely with enterprise data stored in BigQuery to provide scalable, governed analytics
• Enabled self-service analysis through an intuitive conversational interface, reducing dependency on technical teams

Key Components:

Enterprise Data Foundation: BigQuery served as the centralized analytical platform, enabling fast, secure querying of enterprise datasets at scale.

Generative AI Intelligence: Vertex AI and Model Garden powered the large language models responsible for understanding business questions, generating SQL queries and producing clear, contextual summaries.

Automated Workflow Orchestration: Cloud-based orchestration streamlined data retrieval, query validation and analytical workflows, ensuring reliable and efficient execution.

Scalable Data Management: Cloud Storage supported intermediate processing, model outputs and reusable analytical artifacts, enabling efficient data handling across the solution.

Business-Friendly Analytics: Automated reports, visualizations and conversational responses enabled stakeholders to perform complex analysis using simple English, significantly improving accessibility and decision-making.

Decision Science Framework: Mu Sigma’s structured approach to problem framing and decision architecture ensured the platform was built around business questions, not just query automation. By mapping analytical workflows to specific decision contexts across marketing, measurement and strategy teams, the solution delivered contextually accurate insights rather than generic data outputs

Together, these Google Cloud services transformed enterprise analytics into a conversational, AI-powered experience, enabling business users to access trusted insights faster while allowing Data Science teams to focus on higher-value strategic initiatives.

Impact

• More than 3,500 prompts successfully processed across business users
• Approximately 95% response accuracy achieved through AI-powered analytics
• 90% reduction in turnaround time for analytical requests
• Adopted across multiple business communities supporting marketing and measurement functions
• Enabled three new AI-powered analytical capabilities for business users
• Reduced dependency on manual reporting and technical data specialists