AWS Business Intelligence Blog
Meet one of the top Amazon QuickSight Community Experts: David Wong
In this post, we are thrilled to feature David Wong, one of our top Amazon QuickSight Community Experts for 2024.
October 2024 Amazon QuickSight events
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!
Docebo increases analytics adoption five times by embedding Amazon QuickSight on their Docebo platform
This is a guest post written by Laurent Balagué from Docebo. Docebo, founded in 2005, is a global provider of learning management systems (LMS). Our platform is used in various industries by more than 3,800 customers, supported by more than 900 Docebo employees. From onboarding, compliance, and workforce training to customer education, partner enablement, and […]
Enhance data governance through column-level lineage in Amazon QuickSight
In this post, we explore how to create a simple serverless architecture using AWS Lambda, Amazon Athena, and QuickSight to establish column level lineage. Tracking column-level lineage provides a clear view of each column’s path through different parts of QuickSight, helping to optimize data processing, improve query performance, ensure accuracy, and meet regulatory requirements.
From Operational Efficiency to Strategic Innovation: The Impact of Amazon QuickSight on Wake
Wake, a leading ecommerce platform provider, transformed its business intelligence capabilities by migrating to Amazon QuickSight. In this post, learn about how QuickSight enables Wake to make more informed, agile, and cost-effective decisions.
Mindex uses Amazon Q in QuickSight to democratize analytics and drive student success in education
In this post, we’ll share how Mindex, a leading provider of enterprise software development and cloud services, partnered with QuickSight to enhance speed and experience and offer advanced analytics to more than 410 school districts through the SchoolTool Platform. We’ll explore how Mindex used QuickSight to build efficient dashboards and used Amazon Q in QuickSight, harnessing the power of generative artificial intelligence (AI) to make dashboard developers more efficient.
September 2024 Amazon QuickSight events
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!
Improve power utility operational efficiency using smart sensor data and Amazon QuickSight Part 3: Generative AI-assisted data storytelling and executive summary
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 scales Business Intelligence to over thirty thousand users using Amazon QuickSight
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.
Build a market basket analysis dashboard using nested filters in Amazon QuickSight
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.