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New Amazon Q in QuickSight uses generative AI assistance for quicker, easier data insights (preview)

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Today, I’m happy to share that Amazon Q in QuickSight is available for preview. Now you can experience the Generative BI capabilities in Amazon QuickSight announced on July 26, as well as two additional capabilities for business users.

Turning insights into impact faster with Amazon Q in QuickSight
With this announcement, business users can now generate compelling sharable stories examining their data, see executive summaries of dashboards surfacing key insights from data in seconds, and confidently answer questions of data not answered by dashboards and reports with a reimagined Q&A experience.

Before we go deeper into each capability, here’s a quick summary:

  • Stories — This is a new and visually compelling way to present and share insights. Stories can automatically generated in minutes using natural language prompts, customized using point-and-click options, and shared securely with others.
  • Executive summaries — With this new capability, Amazon Q helps you to understand key highlights in your dashboard.
  • Data Q&A — This capability provides a new and easy-to-use natural-language Q&A experience to help you get answers for questions beyond what is available in existing dashboards and reports.​​

To get started, you need to enable Preview Q Generative Capabilities in Preview manager.

Once enabled, you’re ready to experience what Amazon Q in QuickSight brings for business users and business analysts building dashboards.

Stories automatically builds formatted narratives
Business users often need to share their findings of data with others to inform team decisions; this has historically involved taking data out of the business intelligence (BI) system. Stories are a new feature enabling business users to create beautifully formatted narratives that describe data, and include visuals, images, and text in document or slide format directly that can easily be shared with others within QuickSight.

Now, business users can use natural language to ask Amazon Q to build a story about their data by starting from the Amazon Q Build menu on an Amazon QuickSight dashboard. Amazon Q extracts data insights and statistics from selected visuals, then uses large language models (LLMs) to build a story in multiple parts, examining what the data may mean to the business and suggesting ideas to achieve specific goals.

For example, a sales manager can ask, “Build me a story about overall sales performance trends. Break down data by product and region. Suggest some strategies for improving sales.” Or, “Write a marketing strategy that uses regional sales trends to uncover opportunities that increase revenue.” Amazon Q will build a story exploring specific data insights, including strategies to grow sales.

Once built, business users get point-and-click tools augmented with artificial intelligence- (AI) driven rewriting capabilities to customize stories using a rich text editor to refine the message, add ideas, and highlight important details.

Stories can also be easily and securely shared with other QuickSight users by email.

Executive summaries deliver a quick snapshot of important information
Executive summaries are now available with a single click using the Amazon Q Build menu in Amazon QuickSight. Amazon QuickSight automatically determines interesting facts and statistics, then use LLMs to write about interesting trends.

This new capability saves time in examining detailed dashboards by providing an at-a-glance view of key insights described using natural language.

The executive summaries feature provides two advantages. First, it helps business users generate all the key insights without the need to browse through tens of visuals on the dashboard and understand changes from each. Secondly, it enables readers to find key insights based on information in the context of dashboards and reports with minimum effort.

New data Q&A experience
Once an interesting insight is discovered, business users frequently need to dig in to understand data more deeply than they can from existing dashboards and reports. Natural language query (NLQ) solutions designed to solve this problem frequently expect that users already know what fields may exist or how they should be combined to answer business questions. However, business users aren’t always experts in underlying data schemas, and their questions frequently come in more general terms, like “How were sales last week in NY?” Or, “What’s our top campaign?”

The new Q&A experience accessed within the dashboards and reports helps business users confidently answer questions about data. It includes AI-suggested questions and a profile of what data can be asked about and automatically generated multi-visual answers with narrative summaries explaining data context.

Furthermore, Amazon Q brings the ability to answer vague questions and offer alternatives for specific data. For example, customers can ask a vague question, such as “Top products,” and Amazon Q will provide an answer that breaks down products by sales and offers alternatives for products by customer count and products by profit. Amazon Q explains answer context in a narrative summarizing total sales, number of products, and picking out the sales for the top product.

Customers can search for specific data values and even a single word such as, for example, the product name “contactmatcher.” Amazon Q returns a complete set of data related to that product and provides a natural language breakdown explaining important insights like total units sold. Specific visuals from the answers can also be added to a pinboard for easy future access.

Watch the demo
To see these new capabilities in action, have a look at the demo.

Things to Know
Here are a few additional things that you need to know:

Join the preview
Amazon Q in QuickSight product page

Read more about Amazon Q

— Donnie

Donnie Prakoso

Donnie Prakoso

Donnie Prakoso is a software engineer, self-proclaimed barista, and Principal Developer Advocate at AWS. With more than 17 years of experience in the technology industry, from telecommunications, banking to startups. He is now focusing on helping the developers to understand varieties of technology to transform their ideas into execution. He loves coffee and any discussion of any topics from microservices to AI / ML.