AWS Business Intelligence Blog

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 […]

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.

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.

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.

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 September, we have an exciting lineup of sessions to learn all about how Amazon QuickSight can be used for generative insights and security use cases. See how Amazon Q integrates with Amazon Connect and QuickSight. Dive deep understanding how to use row and column level security (RLS and CLS) and how you can do so for private data for public sector customers. Learn from our partners at Ironside how you can migrate to Amazon QuickSight from other legacy business intelligence (BI) tools.

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.