AWS Business Intelligence Blog

Amazon QuickSight stands at the forefront of AWS business intelligence (BI) and data visualization offerings, enabling organizations to create and share interactive dashboards, perform one-time analyses, and glean actionable insights from their data. In today’s data-centric business environment, the ability to efficiently generate and distribute insightful reports across different segments or regions remains a critical challenge for many business. Addressing this challenge, we delve into the automation of report processing workflows. For our use case, a real estate customer wants to send state-specific weekly real estate reports for each state to their regional agents. In this post, we show you how to use QuickSight, combined with its Snapshot APIs and other AWS services, to automate this process.

Amazon QuickSight supports identity federation through Security Assertion Markup Language 2.0 (SAML 2.0) in both Standard and Enterprise editions. With federation, you can manage users using your enterprise identity provider (IdP) and pass them to QuickSight at login. IdPs include Microsoft Active Directory Federation Services, Ping One Federation Server, Okta, and more. This post provides steps and code samples to overcome these challenges in a scalable way. We demonstrate the solution using Okta, but you could use other IdPs as well. This is a proven solution and has been used and implemented by several QuickSight customers.

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 both in-person and virtual events across the globe to bring direct learning resources […]

Traeger Grills invented the Original Wood-Fired Grill over 30 years ago in Mt. Angel, Oregon. We make it easy for home cooks to create delicious wood-fire flavored meals, and have enhanced our grill products by selling a variety of merchandise, from wood pellets and accessories to meals, seasonings, and T-shirts. Traeger Grills’s Customer Experience team is responsible for order management, customer service, and technical support for customers and commercial partners like The Home Depot, ACE Hardware, and Amazon. In this post, we discuss how Traeger Grills’s Customer Experience team uses Amazon QuickSight for data insights, helping drive customer satisfaction score from approximately 72% to 93%, and improved our first call resolution number from about 40% to 75%.

With origins in India and a legacy of more than 50 years, Tata Consultancy Services (TCS) is a global consultancy specializing in a range of technical operations. We operate in more than 50 countries with a highly localized workforce of more than 600,000 employees, across cloud and cybersecurity to analytics and network capabilities. In this post, TCS shares how we built a modern business intelligence (BI) software for an oil and gas client with Amazon QuickSight.

You can use the advanced functionalities in Amazon QuickSight to analyze data at different dimensions and get granular, actionable insights from your data. QuickSight also enables you to achieve this without having to worry about the complexity with data preparation. With QuickSight level-aware calculations (LAC), users including business analysts, data scientists, and decision-makers can dynamically […]

This post provides guidance on deploying QuickSight in a multi-tenant environment, and the considerations around data isolation and deploying resources to tenants in a QuickSight application. Multi-tenancy within applications provides a mechanism to segment groups of users from one another. These groups could be users from different companies, different geographic regions, or different lines of business within an enterprise. Users within different tenants can’t see other users, data, and assets, while reducing the complexity of having a different infrastructure for each set of users.

This is a guest post authored by Rishi Ranjan from Common Securitization Solutions. A joint venture of Fannie Mae and Freddie Mac, Common Securitization Solutions (CSS) administers a portfolio of $6.4 trillion worth of mortgage-backed securities. They use huge quantities of data to facilitate investment decisions. In this post, CSS shares how Amazon QuickSight helped […]

Amazon QuickSight is a business intelligence (BI) solution that any organization can leverage to share data and insights to anyone in the organization. As a serverless BI tool, it offers a comprehensive set of advanced analytics features, and one core benefit is that pricing is consumption based. That being said, Quicksight can be leveraged for different use cases and each variant might require a different method to track and report on the cost of running QuickSight. In this post, we explore how you can use AWS CUR and AWS tags to monitor how specific users are using QuickSight. We also discuss how these tags can help organizations implement cloud cost controls by providing the data needed to support custom chargeback reporting.

In this post, we show how to use AWS CloudFormation to deploy all the necessary resources to automate the retry of the ingestion of a failed dataset refresh. This can help speed up the time to have the data available to the users by either completing the refresh successfully or providing more information on the cause of the failure to the dataset owner. Additionally, QuickSight assets can be monitored using Amazon CloudWatch metrics. QuickSight developers and administrators can use these metrics to observe and respond to the availability and performance of their QuickSight ecosystem in near-real time.