When AI Met BI: How Amazon QuickSight is Making Data More Accessible
Amazon Q Business, first introduced at the Amazon Web Services (AWS) re:Invent in 2023, has evolved over the past 12 months to become a comprehensive AI assistant that can answer questions, summarise content, generate visuals, and automate tasks – all based on an organisation’s data. QuickSight is Amazon’s completely managed, cloud-native business intelligence (BI) service that is revolutionising the way an organisation interacts with their data and empowers teams at all levels to adopt a data-driven culture. What if your business could unlock the full potential of its data, transforming it into insights that drive smarter decisions at the speed of thought? That’s exactly what Amazon QuickSight delivers. Its features include machine learning–powered insights, natural language queries through QuickSight Q, and predictive analytics that can democratise data and allow users, whether technical or non-technical, to access and act on the data. Tracy Daugherty, general manager of Amazon QuickSight, has been the driving force behind this evolution and has guided the platform through modern data analytics challenges. QuickSight has never been better for scale. With support for multi-tenant deployments for enterprise needs, the offering now easily integrates with other AWS services, including S3, Redshift, and Athena. At AWS re:Invent 2024, AIM interviewed Daugherty and had a detailed discussion about the platform’s journey and its vision for the future. Reflecting on the platform’s early days, Tracy said, “When I joined seven years ago, Amazon QuickSight was architecture-rich but feature-poor.” Despite its potential cloud-native architecture, Amazon QuickSight faced stiff competition from established vendors such as Microsoft Power BI and Tableau. The Journey and Challenges Tracy and his colleagues identified an opportunity to differentiate Amazon QuickSight by emphasising accessibility. “Amazon QuickSight was built to be more than a dashboard tool,” he explained. “It’s about empowering everyone in the organisation with insights, whether you’re a business executive, developer, or frontline worker.” This vision transformed Amazon QuickSight from a basic reporting tool to a full-fledged, self-service BI platform that could support use cases ranging from dashboards and reporting to embedded analytics. “Our purpose was always to democratise data access,” Tracy said. “We wanted to build a tool that would suit everybody – from technical analysts to those who have no technical background.” One of QuickSight’s most significant developments was the launch of Amazon Q, a generative AI-powered assistant that introduced natural language processing (NLP) capabilities to the BI market. “One of the biggest challenges users face is knowing what to ask…Q now understands context, suggests follow-up questions, and provides multiple visual answers for better exploration,” Tracy pointed out. The Ideal Assistant for Businesses Tracy described Amazon Q Business as a breakthrough tool for managing data at scale, with its ability to connect seamlessly to over 40 enterprise data sources, including Microsoft 365, Amazon S3, Google Drive, Salesforce CRM, and Asana. The AI-powered assistant can synthesise data from various sources and provide users with actionable insights via natural language enquiries. “Amazon Q Business brings AI directly into the hands of business users to answer critical questions, automate key tasks, and generate visuals with ease,” Tracy said, explaining how this allows teams to interact with data intuitively. The productivity gains from Amazon Q have been substantial for businesses. According to Tracy, preliminary tests indicate that Amazon Q will increase staff productivity by as much as 80%, specifically through the automated extraction of insights. However, he noted that the actual success measure is more than just the metrics; it is how successfully it is accepted and used across organisations. The integration of generative AI to QuickSight was further amplified in its capability through the launch of scenarios analysis capability in Amazon Q in QuickSight. “It’s a decision-making assistant,” Tracy explained. “Users can simulate outcomes, and Amazon Q returns actionable insights and recommendations in real time.” ‘AI Won’t Replace Analysts’ “AI isn’t here to replace analysts. It’s here to make their work more strategic by automating repetitive tasks and uncovering insights they might otherwise miss,” Tracy said. As Amazon Q in QuickSight pushes the boundaries of what BI tools can do, it faces stiff competition from established players in the industry. Tracy believes Amazon Q in QuickSight’s native integration with AWS gives it a significant advantage. “The integration with AWS services like data lakes, warehouses, and machine learning tools creates a secure, unified ecosystem for enterprises,” Tracy explained. This seamless connectivity not only enhances QuickSight’s functionality but also ensures that it remains a secure platform for business users, with stringent security measures in place to protect sensitive data. A standout feature in Amazon QuickSight’s security capabilities is the Random Cut Forest (RCF) algorithm, which excels in real-time anomaly detection. “Unlike traditional machine learning algorithms, RCF is optimised for real-time anomaly detection, making it invaluable for fraud prevention and operational monitoring,” Tracy said. This focus on security underscores Amazon QuickSight’s commitment to safeguarding customer data while continuing to innovate with AI features. User-Centric Approach Becomes Popular The popularity of Amazon Q in QuickSight can also be attached to its user-centric approach. “Most business intelligence technologies are built for specialists. We built Amazon Q in QuickSight to be self-service, where the intent is that every employee in the organisation can gain insights without requiring a broad degree in data science,” Tracy pointed out. This shift from being a model that was data analyst-centric to making all those employees have their power has helped Amazon QuickSight acquire widespread adoption, considering hundreds of thousands of users depend on it daily. Tracy imagines a world in which BI tools are woven into the fabric of all business processes. “Analytics should feel intuitive. It’s not just about visualising data – it’s about turning those visuals into actionable narratives that drive better outcomes.” Tracy has valuable advice for aspiring BI professionals. “Focus on mastering AI-driven tools and developing a strong foundation in data storytelling. The ability to turn data into actionable narratives is what sets the best apart,” he said. Amazon Q in QuickSight changes how businesses engage with data by including features such as scenarios and generative AI, making analytics a vital driver of strategic decision-making. “The future of BI is about making data accessible, actionable, and transformative for businesses of all sizes,” Tracy concluded.
“Most business intelligence technologies are built for specialists. We built Amazon Q in QuickSight to be self-service, where the intent is that every employee in the organisation can gain insights without requiring a broad degree in data science,” Tracy Daugherty said.
At AWS re:Invent 2024, AIM interviewed Daugherty and had a detailed discussion about the platform's journey and its vision for the future.
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["AI", "machine learning", "ML", "NLP", "data science", "analytics", "generative AI", "Aim", "predictive analytics", "anomaly detection"]
Anshika is the Senior Content Strategist at AIM Research. She holds a keen interest in technology and related policy-making and its impact on society. She can be reached at anshika.mathews@analyticsindiamag.com.