Arocom IT Solutions Pvt. Ltd.

Transforming QuickBase Data into Conversational AI Analytics using Amazon Q and Quicksight

Client

One of the premier electrical contractors in the United States utilizes QuickBase applications for core business operations, managing structured data across multiple departments, including employee records, project management, and operational metrics stored in various QuickBase tables.

Challenge

The client faced significant barriers in accessing and analyzing their valuable QuickBase data, which was locked in application silos and required technical expertise for reporting and insights generation. Business users without SQL knowledge or technical training were unable to independently query their data, creating bottlenecks in decision-making processes and dependencies on technical teams for routine reporting tasks. The organization needed a solution that would democratize data access while ensuring automated daily updates, maintaining data integrity across multiple QuickBase applications, and providing natural language querying capabilities without manual intervention.

Key Results

  • Reducedtime-to-insightby 85% by enabling direct conversational queries to business data instead of traditional technical reporting methods
  • Eliminatedtechnical barriers to data access for all employees.
  • Automatedtheentire data pipeline, saving 15 hours per week previously spent on manual reporting
  • Increaseddata-drivendecision making (compared to experience or gut feeling based decision making) by 40% through wider accessibility to insights via a conversational interface
  • Reduceddependencyon technical teams by 80% as business users gained independent access to analyze QuickBase data. Allowing the technical team to focus on new innovations.
  • Improveddatafreshness with guaranteed daily updates, ensuring all queries use current information
  • Deliveredinsightsin 4 different formats (text summaries, interactive dashboards, structured tables, and statistical analysis) based on query context.
  • Successfullyprocessedboth simple fact-finding queries and complex multi-dimensional analysis requests through the same interface

Solution

We implemented a comprehensive QuickBase to Amazon Q Business integration solution that transformed siloed application data into an intuitive conversational analytics platform. The team developed sophisticated two AWS Glue ETL job , one for extracting data from QuickBase via API with support for full, incremental, and date-based selective data loads, and the other job for maintaining data integrity through intelligent merge operations and upsert functionality.

The solution established a structured Amazon S3 data lake architecture with separate zones for raw and processed data, utilizing a config.json configuration file to control which QuickBase tables and fields were made available to Amazon Q Business. The team configured Amazon QuickSight to create consolidated datasets from individual QuickBase tables through primary and foreign key relationships, enabling comprehensive data modeling across multiple applications.

Critical to the solution’s success was the enhancement of field metadata with business-friendly names, detailed descriptions, and synonyms for optimal natural language processing. The team established a precise automated scheduling system with carefully timed intervals.

Amazon Q Business was configured to deliver multi-format AI-driven insights through natural language queries, enabling users to receive text summaries, dynamic visualizations, structured tables, and statistical analyses from conversational prompts, transforming technical data access into an accessible conversational analytics experience.

Technologies Used

  • AWSGlue ETL (Extract, Transform, Load)
  • AmazonS3
  • AmazonQuickSight
  • AmazonQ Business
  • QuickbaseAPI
  • CloudWatch(for monitoring and scheduling)

Summary for Website Card

We transformed Quickbase data into an AI-powered conversational analytics platform using Amazon Q Business, enabling business users to gain instant insights through natural language queries and reducing time-to-insight by 85%. We also maintained data accuracy through an automated ETL pipeline with daily synchronized updates.

Workflow Diagram