AWS Business Intelligence Blog

Amazon QuickSight powers data-driven organizations with unified business intelligence (BI) at hyperscale. With Amazon Q in QuickSight, business analysts and business users can use natural language to build, discover, and share meaningful insights in seconds, turning insights into impact faster. We host in-person and virtual events across the globe to bring direct learning resources to our customers. Explore our recurring QuickSight Learning Series, PartnerCast, and Immersion Day events, sign-up for ones that fit your interests, and share this post with others!

Amazon QuickSight is a serverless, fully managed business intelligence (BI) service that enables data-driven decision-making at scale. QuickSight meets diverse analytic needs with modern interactive dashboards. In Part 1 and Part 2 of this series, we showed how QuickSight can improve power utility operational efficiency and how to create, schedule, and share highly formatted multi-page reports based on different dashboard requirements. In this post, we focus on implementing generative artificial intelligence (AI) in QuickSight and how generative AI can help operators quickly analyze and identify circuit faults to improve power utility operational efficiency.

Amazon Logistics (AMZL) is the Amazon last-mile delivery network. The goal of AMZL is to provide customers with a seamless package delivery service across multiple geographies. AMZL plays a critical role in Amazon Transportation’s supply chain by using continuous improvement initiatives and creative thinking to ensure that millions of packages reach their destination as efficiently as possible. Amazon QuickSight reporting and BI infrastructure hosts more than 3,200 dashboards that are consumed by over 30,000 users across the company with average weekly active users of more than 17,500. Critical insights such as AMZL daily and weekly business reviews, operational metrics that help visualize corporate business outcomes and drivers, and operational insights for delivery station managers and AMZL leadership are all hosted in Amazon QuickSight, making it the one-stop shop for AMZL analytics and reporting. In this blog post, we share the challenges AMZL faced with their previous BI solution, their migration to QuickSight, and the best practices that helped the AMZL team to reduce costs and improve performance to help stakeholders make data-driven decisions.

Amazon QuickSight is a scalable, serverless, machine learning (ML)-powered business intelligence (BI) solution. As a fully managed service, QuickSight lets you create and publish interactive dashboards that can be accessed from any device and embedded into your applications, portals, and websites. Traditionally, building market basket analysis dashboards requires data engineering pipelines that can take weeks to implement, because these often depend on ETL (extract, transform, and load) jobs, complex SQL operations, and updates on the data pipeline. The nested filter capability in QuickSight simplifies this process with a no-code interface. In this post, we show you how to configure nested filters in a QuickSight dashboard and how they can aid in different business use cases within market basket analysis. We show how nested filters can provide more advanced filtering to help solve common challenges with market basket analysis dashboards in four different use cases.

With over 5350 branches, 8 international offices, and over 16,000 ATMs, Axis Bank is India’s third-largest private sector bank. In this post, we share how Axis Bank used Amazon QuickSight to achieve highly scalable modern business intelligence (BI). As part of our digital transformation journey, we started using AWS to modernize our applications to be cloud-centered. We’ve deployed several mission-critical applications on AWS, from onboarding journeys for Savings, Salary, Staff Account, central Know Your Customer (KYC) platform for video KYC, eKYC, to credit card servicing application.

Amazon QuickSight powers data-driven organizations with unified business intelligence (BI) at hyperscale. With Amazon Q in QuickSight, business analysts and business users can use natural language to build, discover, and share meaningful insights in seconds, turning insights into impact faster. We host in-person and virtual events across the globe to bring direct learning resources to our customers. Explore our recurring QuickSight Learning Series, PartnerCast, and Immersion Day events, sign-up for ones that fit your interests, and share this post with others!

In QuickSight, data is queried from datasets when visuals load within analyses, dashboards, reports, exports, in responses to questions asked in natural language to Amazon Q, or when threshold alerts are being evaluated. Direct queries are sent to the underlying data source every time a request is made. Using SPICE, a refreshable snapshot of the data is cached in QuickSight, and all queries are fulfilled using the latest snapshot in SPICE, no longer connecting to the underlying data source. In this post, we will explore the benefits and factors to consider when using SPICE and direct query mode. Afterwards, we will also discuss when and how to use which query mode most efficiently in different scenarios.

QuickSight users can now use natural language generation to create pixel-perfect reports – a new capability in Amazon Q for QuickSight that simplifies the way users create and distribute visually-rich, highly-formatted reports to their stakeholders. Pixel-perfect reports are crucial because they ensure that every detail of the report, from layout to formatting, is meticulously controlled and aesthetically pleasing. This not only creates a professional data presentation, it also improves comprehension and decision-making among stakeholders.In this post, we cover how to use generative BI capabilities to accelerate pixel-perfect report designing using Amazon Q in the QuickSight console and deliver it to QuickSight users. Pixel-perfect reports can also be delivered to non-QuickSight users in Excel, CSV, or PDF format using Snapshot Export APIs.

We are thrilled to announce that Amazon Web Services (AWS) has been recognized as a Challenger in the 2024 Gartner Magic Quadrant for Analytics and Business Intelligence (ABI) Platforms. This recognition marks the second consecutive year AWS has been named a Challenger, which we believe reflects our ongoing commitment to innovation and excellence in the ABI space. The Gartner Magic Quadrant evaluates 20 ABI companies based on their Ability to Execute, which measures a vendor’s capacity to deliver its products or services effectively, and Completeness of Vision, which assesses a vendor’s understanding of the market and its strategy for future growth, according to Gartner’s report, How Markets and Vendors Are Evaluated in Gartner Magic Quadrants.

Pixel-perfect reports in Amazon QuickSight enable you to create and share highly formatted, personalized reports containing business-critical data to hundreds of thousands of end-users without infrastructure setup or maintenance, upfront licensing, or long-term commitments. To save time copying the same visual multiple times to filter for different values and generate an invoice for all your customers, you can use repeating sections in pixel-perfect reports. With repeating sections, authors can build visuals in a report one time but have the system repeat for as many values of a dimension exist. In this post, we demonstrate how to use this new feature.