How We Delivered 62% Faster Insights and Simplified Analytics with Amazon QuickSight Q
Our client, a digital customer engagement platform, aimed to improve data accessibility and self-service analytics for non-technical business teams....
At a Glance
Our client, a digital customer engagement platform, aimed to improve data accessibility and self-service analytics for non-technical business teams. Although their MySQL environment stored extensive loyalty program data, users in marketing, operations, and customer success relied heavily on analysts for routine reporting and insight generation. To streamline decision-making and reduce BI dependency, the organization initiated a proof of value (POV) project to activate Amazon QuickSight Q's natural-language query capabilities.
Challenge
Data and analytics complexities restricting self-service insights
Despite having a robust MySQL database, the organization struggled with true self-service analytics. Non-technical teams lacked SQL expertise, leading to slow turnaround times for routine insight requests. There was also no semantic structure to help an analytics tool understand business definitions, loyalty program KPIs, or frequently used domain language.
The objective was to introduce an accessible, AI-driven analytics experience without rebuilding dashboards, while ensuring accuracy, scalability, and business relevance.
Solution
QuickSight Q implementation with semantic modeling
Our team executed a structured proof-of-value project to activate natural-language insights into the existing loyalty data.
- Enabled Natural-Language Querying on MySQL: Configured Amazon QuickSight Q to process plain-language business questions and deliver insights without SQL or dashboard navigation.
- Evaluated and Modeled the MySQL Schema: Assessed schema compatibility, curated datasets, and designed semantic layers that mapped business terms, KPIs, and loyalty metrics for accurate interpretation.
- Validated High-Value Business Scenarios: Conducted stakeholder workshops to collect real user questions and iteratively tune Q for improved accuracy across marketing, operations, and customer success functions.
Benefits
Accelerated self-service analytics adoption by 70% with reduced analyst dependency
The POV successfully demonstrated measurable value for the client's analytics workflow.
- Expanded Data Access: Business users without technical expertise can retrieve loyalty insights using natural-language queries, resulting in a 70% rise in self-service analytics adoption.
- Accelerating Insight Generation and Decision-Making: Direct access to customer trends, loyalty activity, and operational metrics enabled 62% faster insight generation, supporting quicker marketing optimization and engagement decisions.
- Foundation for Scalable Enterprise Adoption: The pilot provides a reusable blueprint for expanding natural-language analytics to additional datasets and business domains.
Ready to enable natural-language analytics for your business users?
zeb, an AWS Premier Tier partner, supports organizations in creating intuitive analytics experiences through natural-language BI, semantic data modeling, and scalable analytics modernization. This enables business teams to explore data independently while reducing reliance on technical specialists.
By aligning analytics platforms with business terminology and everyday workflows, teams can access insights more quickly and use data more effectively across their operations.
Let's build an accessible and scalable analytics environment that helps your teams turn data into informed decisions.
Ready to transform
your enterprise?
Let's build something that lasts. Our team is ready to talk.