AWS Business Intelligence Blog
Ping Identity is a provider of identity and access management solutions that lead to frictionless and secure digital experiences. Ping’s services and embedded analytics dashboards make it easy for organizations to manage and control how their users access digital resources and apps. In this post, we discuss how Ping Identity uses Amazon QuickSight to embed analytics into the PingOne Cloud Platform to enhance customer experience and gain a better understanding of customer behavior with data-driven insights.
EasySend is a no-code platform that makes it easy for businesses to transition from long, plain-text forms to rich, interactive digital experiences. We work with over 100 enterprises, including world-leading carriers, agencies, brokerages, MGAs/MGUs, TPAs, and insurance software integrators. Using EasySend, the data collection process becomes more intuitive and enjoyable for end users — and, crucially, the data itself is easier for the business to manage, verify, and track by automatically syncing to customers’ core systems. In this post, we discuss how EasySend embeds analytics into their platform to enhance customer value through data and insights at their fingertips using Amazon QuickSight.
The Amazon QuickSight Community was launched in February 2022 to serve as a one-stop-shop where Business Intelligence (BI) authors and developers from across the globe can access learning content, ask and answer questions, stay up-to-date, network, and learn together about Amazon QuickSight. In this post, we’ll walk through the resources offered in the QuickSight Community, highlighting the newly launched Developer Corner for application developers, which showcases QuickSight’s embedded analytics capabilities across all facets of the customer experience.
How Amazon QuickSight empowered Treez to build an industry-leading analytics product at a lower cost
Treez is a leading provider of enterprise point of sale (POS) software for the regulated cannabis retail industry, serving thousands of customers. It’s a young industry with a large proportion of small businesses operating within it, and they work in a complex environment of regulatory scrutiny and change. In this post, we discuss how we built an embedded business intelligence (BI) dashboard system in our POS offering for our customers and their own internal staff using Amazon QuickSight.
Starburst is an AWS Data and Analytics Competency Partner with the Amazon Graviton and EC2 Spot Ready product designations. Starburst Galaxy is a fully-managed data platform, powered by Trino, that combines the scalability of a data lake, the functionality of a data warehouse, and the reach of data virtualization in a single platform. Galaxy offers several business intelligence (BI) and visualization tool integrations, including Amazon QuickSight. This post shows you how to connect QuickSight to Starburst Galaxy for powerful visualizations across all your cloud data sources.
Improve pivot table space utilization using the hide collapsed columns and default column width features in Amazon QuickSight
Recently, we launched a range of new features for Tables and Pivot Tables in QuickSight centered around interactivity and performance, allowing you to alter field visibility, load tables faster, and build consistency across different interactions. Building on this momentum, we are excited to unveil two additional capabilities, this time with a focus on improving readability, presentation, and space optimization.
Readers want to understand data quickly when viewing reports in Amazon QuickSight. To achieve that, authors need more capability to control the format and design of pivot tables. That is why QuickSight has launched more visual display options for pivot tables, giving you more control over the positioning of totals as well as contextual information for subtotals. These capabilities are part of a continued focus of giving QuickSight users more flexibility when designing dashboards and paginated reports. Additionally, the recent launch of the hierarchy layout for pivot tables includes a layout option for pivot tables that uses indentations to differentiate items from different fields, resulting in a more readable table.
This post helps cloud architects, cloud engineers, and developers build embedded analytics architectures on the AWS Cloud using AWS purpose-built data analytics services and Amazon QuickSight by providing various architectural patterns for building personalized dashboards based on user role or job function. We also discuss best practices and key considerations that showcase the power of embedded analytics, and share additional resources for getting started with building embedded analytics on AWS.
Generative BI dashboard authoring capabilities now available in preview for Amazon QuickSight Q customers
Amazon QuickSight customers can now try generative business intelligence (BI) capabilities in preview to build visuals, build calculations, and refine visuals by using the natural language interface of Amazon QuickSight Q. These new capabilities are the first wave of generative BI capabilities announced at the 2023 AWS New York Summit, and build on the early AI innovation of the natural language query capability of Q, which has enabled business users to ask questions of their data without having to write SQL queries or learn a BI tool since 2020. Generative BI in QuickSight is powered by Amazon Bedrock large language models (LLMs), which securely retain data within the AWS environment.
Amazon QuickSight is a scalable, serverless, embeddable, machine learning (ML)-powered business intelligence (BI) service built for the cloud. You can now export content to Excel workbooks by selecting multiple tables and pivot table visuals from any sheet of a dashboard on the QuickSight console via schedules or programmatically via a set of new Snapshot Export APIs. This post outlines these new functionalities and guides you through their implementation.