# 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...

_2026-04-20 · Technology & Digital Services · Data & Analytics_

## 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.

- **71%** — Reduction in analyst dependency
- **64%** — Faster time to insights
- **69%** — Increase in self-service analytics adoption

## 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.

Let's build an intuitive and future-ready analytics experience for your teams.