AWS Business Intelligence Blog

Revolutionizing business intelligence: Amazon Q in QuickSight introduces powerful new capabilities

Today, we’re excited to announce the general availability of groundbreaking capabilities in Amazon Q for QuickSight. These enhancements are set to revolutionize how business users access and interpret data insights, making informed decision-making faster and more accessible than ever before.

Amazon QuickSight is a unified BI service built for the cloud that provides modern interactive dashboards, natural language querying, pixel-perfect reports, machine learning (ML) insights, and embedded analytics at scale. Amazon Q transforms how work gets done in your organization, empowering users to start asking questions of data using natural language. Amazon Q in QuickSight uses the same QuickSight datasets you use for your dashboards and reports so your data is governed and secured.

Amazon Q Business is a generative AI assistant that helps your team work smarter. It can answer questions, provide summaries, generate content, and securely complete tasks based on the information in your enterprise systems. It serves as a centralized knowledge base that can be connected to various organizational resources such as document libraries, internal websites, and business applications. Amazon Q Business uses natural language processing (NLP) to understand and respond to user queries, providing relevant information from across the connected sources.

By integrating Amazon Q Business with QuickSight, you can now use this vast repository of organizational knowledge alongside your structured data analytics. This integration allows for more comprehensive and context-rich insights, because it combines quantitative data from QuickSight with qualitative information from various business documents and applications.

The challenge: Bridging the gap between structured and unstructured data

Organizations face a critical challenge in effectively combining insights from structured and unstructured data sources. Although structured data (like sales figures and financial metrics) in databases, data lakes, and data warehouses can be analyzed using traditional BI tools, information relevant to inform decisions from unstructured data (such as emails, documents, and social media posts) in document management systems, websites, and applications hasn’t traditionally been accessible from within a BI context. This disconnect between the two data types has led to several key issues, including incomplete analysis, siloed information, time-consuming manual processes, and missed opportunities to make well-informed decisions.

Combining structured and unstructured data hasn’t traditionally been automated because systems designed to process data have required information to be in only one format. Large language models (LLMs) bring a new way of merging and interpreting information, allowing for new customer experiences. Addressing this challenge effectively can dramatically improve your organization’s ability to use existing data assets, leading to more comprehensive BI and strategic advantages in an increasingly competitive market.

The disconnect between these two data types has led to several key issues:

  • Disjointed insights from data across different formats – Organizations face challenges in extracting valuable insights from their data, which is scattered across different data formats—from structured data sources like databases, data lakes, and data warehouses, as well as unstructured sources like documents libraries, webpages, internal repositories, call summaries, and more
  • Incomplete answers that miss important context – Answers might be incomplete or miss important context because they fail to include critical business context from unstructured sources distributed across an organization’s files, websites, and applications
  • Time consuming and error prone – Manually collating and combining information across many sources to surface additional context is both time consuming and error prone

Addressing these challenges requires a solution that can seamlessly integrate structured and unstructured data analysis, making insights from both sources readily available and interpretable for business users at all levels. This is where the new capabilities of Amazon Q in QuickSight come into play, offering a revolutionary approach to bridging this long-standing gap in business intelligence.

QuickSight insights augmented by Amazon Q Business

This powerful integration seamlessly connects QuickSight with Amazon Q Business, opening up access to a vast array of unstructured data sources. These include document libraries, organizational websites, and various business applications. Amazon Q Business currently supports over 40 built-in connectors to popular enterprise applications, document repositories, and knowledge management systems.

The integration uses Amazon Q Business to automatically discover and summarize relevant information from these unstructured sources. This capability enriches the insights provided in data Q&A answers and generated data stories, offering users a more comprehensive view of their business landscape.

By seamlessly integrating structured and unstructured data sources, Amazon Q in QuickSight provides a holistic view of business performance. This eliminates the need for manual data correlation, saving time and providing a more complete picture for decision-makers.

The following screenshot shows an example of unstructured insights from Amazon Q Business with relevant Source citations.

Enhanced contextual insights in Q&A and data stories

The narratives generated in data Q&A answers and during the creation of data stories now include summaries of findings from unstructured sources. These summaries come with links to the source material, providing users with straightforward access to deep business context.

When users ask questions or build data stories in QuickSight, they receive a rich, contextual analysis that draws from the entire spectrum of their organization’s information resources, not just structured data.

Unlike conventional natural language query services that rely solely on structured data, this solution incorporates vital business context from unstructured sources. This results in more complete, nuanced answers, addressing a long-standing limitation in BI tools.

The following screenshot shows an example of how within data stories you can upload local documents and use unstructured documents from Amazon Q Business.

Benefits for business users

The introduction of these capabilities brings a host of benefits to business users:

  • Faster, more informed decision-making – By providing a comprehensive view of both structured and unstructured data insights, Amazon Q in QuickSight enables quicker and more accurate decision-making
  • Improved accessibility – With Q&A now automatically enabled for all dashboards, users across the organization can query their data without technical barriers
  • Enhanced context – The integration of insights from business documents, applications, and websites provides richer context to data analysis, leading to more nuanced understanding and strategy development
  • Time savings – By automating the process of finding and relating insights from various sources, users can focus more on analysis and less on data gathering and collation
  • Increased accuracy – The reduction in manual data handling minimizes the risk of errors, enabling more reliable insights and decisions

Conclusion

These new Amazon Q in QuickSight capabilities mark a significant milestone in the evolution of BI. By bridging the gap between structured and unstructured data, we’re empowering organizations to make faster, more informed decisions based on a complete view of their business landscape.

For existing QuickSight customers, these new features are now available to explore and implement. For those considering a move to a more powerful and intuitive BI solution, there’s never been a better time to discover how Amazon Q in QuickSight can transform your data analysis and decision-making processes. We invite you to experience these new capabilities and see firsthand how they can streamline your access to data insights, enhance your understanding of business performance, and ultimately drive your organization’s success. Learn more at Integrate unstructured data into Amazon QuickSight using Amazon Q Business.

You can additionally learn about the new Amazon Q Business to QuickSight integration, offering seamless access to structured data sources and advanced visualizations at your fingertips.

Resources

Check out the following useful resources:

About the Author

Rahul Easwar is a Senior Product Manager with Amazon QuickSight, bringing over 15 years of experience in implementing and leading global analytics programs across various industry verticals. His expertise has been instrumental in shaping the future of business intelligence within the AWS ecosystem. As the product leader for Amazon QuickSight Pixel-Perfect Reporting, Easwar spearheaded the successful launch of this groundbreaking feature in 2022. Currently, Easwar is focused on pushing the boundaries of AI-powered analytics with Amazon Q in QuickSight. This cutting-edge initiative aims to democratize data insights by using natural language processing and machine learning to make data exploration more intuitive and accessible to all users, regardless of their technical expertise.