Creating meaningful dashboards can be a time-consuming task, often requiring hours of manual setup even for seasoned BI professionals. However, with the advent of Amazon Quick, the landscape of dashboard creation is changing dramatically. Amazon Quick leverages advanced generative AI to produce comprehensive, multi-sheet dashboards from natural language prompts, transforming the process of turning one or more datasets into production-ready analysis in mere minutes.
This innovative approach is particularly beneficial for data analysts involved in creating recurring operations reports, program managers preparing for management reviews, or engineers exploring new datasets. By simply describing their needs, users can rely on Amazon Quick to generate multiple organized sheets with selected visuals, filtering controls for exploring different dimensions, and calculated fields like year-over-year growth and month-over-month comparisons. Importantly, users maintain control over the final output by reviewing and editing an interactive plan of the proposed dashboard structure before generation.
Creating an Analysis with Amazon Quick
In Amazon Quick, the Analysis feature serves as the design surface for creating and organizing visuals, filters, and calculated fields across multiple sheets. Once the analysis is ready, it can be published as a dashboard. This streamlined process, powered by generative AI, allows users to refine the analysis and publish it as a dashboard with just a single click.
How it Works
To generate an analysis, you start by selecting the data you want to analyze. In Amazon Quick, your data is stored in datasets that connect to sources such as Amazon Redshift, Amazon S3, or uploaded files. Once your dataset is ready, you describe what you want to see, review a plan, and generate the analysis.
Select Your Datasets
Open a dataset in Amazon Quick and choose Generate analysis. Alternatively, you can start from the Analytics page. Select 1-3 datasets for analysis. If your data spans multiple tables, such as orders in one dataset and products in another, you can select them together.
To add other datasets if necessary, choose Add data.
Describe Your Analysis
Write a natural language prompt describing the information you want in the analysis. Specify the business questions, the metrics of interest, and how you want the information organized on the sheets. For instance: “Create an operations dashboard showing order volume trends, revenue KPIs, delivery performance comparing estimated delivery dates to actual delivery dates, and product category breakdown by revenue and number of orders. Include calculated fields for total revenue, average order value, and month-over-month order growth.”
Amazon Quick Analyzes Your Data
Amazon Quick examines your dataset structure and column statistics, providing real-time progress updates during the operation: analyzing dataset columns, analyzing column statistics, and creating the analysis plan.
If you move away, use the Analytics → Generations to check the status and return to the progress screen.
Check and Modify the Plan
Amazon Quick presents the analysis plan in a two-pane view. The left pane displays your initial prompt and a summary of the selected datasets. The right pane shows the proposed structure: filter fields, sheets, and the visuals planned for each sheet. You can generate immediately or choose To modify to first refine the plan – adjusting sheet names, adding or removing visuals, or rearranging the layout.
Generate the Analysis
Choose Generate. Real-time progress updates show each component being created: calculated fields, filters, and each sheet sequentially.
Early access authors in operations, engineering, and data science have found this feature to save significant time, turning hours of manual setup into minutes of guided build.
During Early Access, an author who had never used AI analysis before tested the feature with his first dataset: “The results are impressive and there is no comparison in the time it takes for AI to perform analysis and create dashboards versus a human.” — Jeff Sondic, Pre-Construction Manager, GES Ops Construction, Amazon, Ontario Canada
The result is a native quick scan. It works with existing release workflows, model integration, CI/CD pipelines, and point-and-click editing within the analysis surface. You can refine each visual after generation. It is not a static image. This is live, interactive analysis connected to your data.
Publish and Share as a Dashboard
When satisfied with the analysis, choose Publish to create a dashboard. Share the dashboard with other users, integrate it into apps in minutes with features like 1-click embedding, or schedule emails to be sent. The published dashboard retains all sheets, visuals, filter controls, and calculated fields from the generated analysis. Recipients interact with the dashboard without accessing the underlying analytics.
Availability and Access
At launch, Generate Analysis is available to Enterprise/Author Pro subscription users. Authors receive promotional access through December 2026 as part of Amazon Quick Enterprise, provided their organization has not restricted access. It is available in various AWS Regions, including US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (London).
Conclusion
Generate Analysis in Amazon Quick creates comprehensive analyzes across multiple sheets from natural language prompts, reducing dashboard creation from hours to minutes. During early access, authors from operations, engineering, and data science reported reducing their dashboard creation time by 90% or more.
One author said: “As a new user, creating this dashboard would have taken at least a full day. It took 5 minutes.” —Prabhakant Rasal, SDE-III, PXT DLS Tech, Amazon, Dallas TX
The AI builds your starting point. You refine it and publish it as a dashboard. Dashboards code questions that your team needs to answer repeatedly. For follow-up explorations and ad-hoc questions that arise in between, try Dataset Q&A to query your data directly in natural language.
About the Authors
Sindhu Chandra
Sindhu Chandra is a senior technology product marketing manager at AWS, leading the go-to-market strategy for Amazon Quick. With over 15 years of experience at Amazon, Uber, and Google, she is passionate about making technology marketing accessible, inclusive, and based on real customer value. Outside of work, she enjoys playing with her dog and making coffee from different origins.
Rushabh Vora
Rushabh Vora is a Senior Product Manager for Amazon Quick at Amazon Web Services, where he leads the generative AI capabilities for data analysis and visualization. Rushabh strives to enable organizations to transform raw data sets into actionable insights through natural language, reducing the time it takes from data to decision from hours to minutes. He is passionate about making data mining and dashboarding accessible to all business users, regardless of technical expertise.
Salim Khan
Salim Khan is a Senior Global Generative AI Solutions Architect for Amazon Quick at AWS. He has over 16 years of experience implementing enterprise business intelligence solutions. At AWS, Salim works with customers around the world to design and implement AI-driven BI and generative AI capabilities on Amazon Quick. Prior to AWS, he worked as a BI consultant in verticals such as automotive, healthcare, entertainment, consumer, publishing, and financial services, providing business intelligence, data warehousing, data integration, and master data management solutions.
For more information, visit the official Amazon Quick blog here.
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