How Natural-Language Analytics with Amazon QuickSight Q Reduced Analyst Dependency by 71%
Our client, a global digital information provider, partnered with our team to improve data accessibility and self-service analytics across their...
At a Glance
Our client, a global digital information provider, partnered with our team to improve data accessibility and self-service analytics across their organization. With growing demand from content, marketing, and analytics teams, the company needed a simplified way for non-technical users to access insights directly from their Amazon Redshift data warehouse without relying on SQL or BI dashboards.
Challenge
Limited analytics accessibility for non-technical business teams
Although the organization had a strong Redshift data warehouse, business teams still relied heavily on analysts for day-to-day reporting. Limited SQL skills restricted true self-service analytics, slowing down routine insight requests across marketing, content, and analytics functions. The absence of a semantic layer also meant the system couldn't interpret business-specific terms, KPIs, or domain language.
To overcome these challenges, the organization needed an AI-driven, natural-language analytics solution that improved accessibility without rebuilding dashboards while ensuring accuracy, scalability, and alignment with real business needs.
Solution
Implementing Amazon Quick Sight Q with a business-aware semantic layer
Our team delivered a structured proof-of-concept to operationalize natural-language analytics on Redshift.
- Implemented Natural-Language Querying on Redshift: Enabled Amazon QuickSight Q to interpret business questions and generate insights without SQL, dashboards, or BI training.
- Designed and Configured a Domain-Specific Semantic Layer: Reviewed Redshift schemas, curated datasets, standardized terminology, and mapped business KPIs to support accurate interpretation.
- Validated Real Business Scenarios Through Workshops: Engaged marketing, content, and analytics teams to gather real user questions and iteratively tune Q for higher accuracy.
Benefits
Driving 71% lower Analyst dependency and faster insights
The engagement resulted in measurable improvements across accessibility, decision-making, and analytics scalability.
- Democratized Data Access: Non-technical users now retrieve insights independently through natural-language questions, contributing to a 69% increase in self-service analytics adoption.
- Higher Decision Velocity: Faster responses enable better campaign planning, content evaluation, and product improvements.
- Scalable Analytics Foundation: Natural-language analytics significantly decreased reliance on technical teams, leading to a 71% reduction in analyst dependency while enabling scalable analytics access.
Ready to make analytics accessible across your organization?
As an AWS Premier Tier partner, zeb helps enterprises enable self-service insights through natural language analytics, semantic modeling, and scalable BI modernization. Our approach focuses on building intuitive analytics environments that empower users to explore data independently, improve decision-making speed, and support evolving business needs.
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