How we Enabled AI-Powered Natural-Language Analytics Using Amazon QuickSight Q
A telecommunications organization ran a QuickSight Q proof of value to bring natural-language business intelligence to its telecom data, letting teams ask questions in plain English without SQL.
At a Glance
A leading telecommunications organization partnered with us to explore how natural-language business intelligence could simplify access to telecom insights. Although the company managed well-structured datasets, teams still relied heavily on analysts for routine reporting. The engagement focused on proving the value of Amazon QuickSight Q through a structured proof of value (POV), demonstrating whether plain-English querying could truly accelerate decision-making and reduce technical friction.
Challenge
Making telecom insights accessible to non-technical users
Despite having rich telecom datasets, business teams struggled to access insights independently. Reporting cycles were slow because most questions required SQL or BI team involvement, which created bottlenecks for marketing, customer operations, and product analytics. The absence of a semantic layer also meant users lacked clarity on KPIs, business definitions, and common telecom terms. The organization needed a more intuitive way to explore data, one that did not require technical skills, dashboard navigation, or rebuilding existing analytics assets. The POV aimed to validate whether QuickSight Q could reliably interpret natural-language queries and transform how insights were consumed across the business.
Solution
A structured QuickSight Q POV enabling natural-language BI
We delivered a comprehensive implementation designed to configure, optimize, and validate QuickSight Q for real-world telecom scenarios.
- Delivered Natural-Language BI on Existing Telecom Data: Configured QuickSight Q to allow business users to ask analytical questions in plain English, removing the need for SQL or dashboard dependence.
- Designed Semantic & Data Modeling for Q Accuracy: Analyzed datasets, defined business-friendly metrics, and optimized Q Topics around recurring telecom analytical patterns.
- Validated Real Use Cases Through Iterative Testing: Performed structured test cycles using genuine business questions, documented results and limitations, and enabled internal teams through knowledge-transfer sessions.
Benefits
Faster insights, simplified access, and a roadmap for AI expansion
The POV created measurable improvements in how the organization interacted with telecom data.
- Democratized Access to Insights: Users across departments can now retrieve answers instantly, avoiding manual reports and technical dependencies.
- Faster Decision-Making Across Telecom Metrics: Time-to-insight significantly improved, enabling quicker planning, troubleshooting, and operational response.
- Foundation for Future AI-Driven Analytics: The engagement outlined clear next steps for scaling analytics with cost forecasting, anomaly detection, and Bedrock-powered custom models.
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