AWS boosts Q in QuickSight with AI-powered scenario analysis
Driven by customer feedback, the BI platform now enables nontechnical and expert users alike to model data and perform deep analytics using generative AI-fueled natural language.
New scenario analysis capabilities in Amazon Q in QuickSight aim to enable users to go beyond basic data exploration and do deep analysis using natural language.
AWS first introduced limited natural language processing (NLP) capabilities in QuickSight, the tech giant's analytics platform, in 2021. Just over two years later, AWS unveiled Amazon Q, a generative AI-powered chatbot integrated into multiple AWS platforms -- including QuickSight -- that enables freeform natural language interactions.
At the time, Q in QuickSight allowed users to ask basic questions of their data and receive responses.
The scenarios capability, made generally available on Tuesday, enables deeper data analysis, letting nontechnical and expert users build and analyze data models and compare different scenarios by asking exploratory "what if" questions.
Given that it adds new types of analysis, the scenarios capability is significant for QuickSight users, according to Mike Leone, an analyst at Enterprise Strategy Group, now part of Omdia.
The underlying value of the scenarios capability is that it moves QuickSight from being a powerful reporting tool to a dynamic, interactive decision-making platform.
Mike LeoneAnalyst, Enterprise Strategy Group
"The underlying value of the scenarios capability is that it moves QuickSight from being a powerful reporting tool to a dynamic, interactive decision-making platform," he said.
Before adding scenario analysis, Q in QuickSight served its purpose as a question-and-answer tool, Leone continued.
"But it was still primarily focused on … providing insights from existing data," he said. "This new capability can enable users to perform more advanced tasks."
First launched in November 2016, QuickSight was viewed as a basic BI platform with a low price that fits with other AWS tools -- its main appeals. Adding NLP capabilities altered that perception, and since then AWS has continued augmenting QuickSight to make it more competitive.
New capabilities
While many enterprises long wanted widespread use of analytics to inform decisions, the complexity of BI platforms limited their use for decades to a small percentage of experts. As recently as 2022, it was found that only about one-quarter of all employees within organizations used BI tools as part of their work.
OpenAI's November 2022 launch of ChatGPT changed that. By combining proprietary data with generative AI models, organizations for the first time could develop applications that enabled true natural language interactions with data that reduced barriers to widespread BI use.
Vendors responded en masse by developing tools that enabled customers to build generative AI chatbots and assistants using proprietary data with large language models.
Now, many are adding depth to those assistants and chatbots. For example, Strategy -- formerly MicroStrategy -- added personalization capabilities to its bot last month so users can receive outputs tailored to their specific needs while Tableau is reimagining its platform with autonomous insight delivery as one of its core features.
Q in QuickSight's added depth centers around deep analysis, using agentic AI to enable users to securely load data into QuickSight from spreadsheets and dashboards, model data and perform scenario analysis with a few clicks.
Q in QuickSight's scenarios capability could help differentiate the platform's generative AI capabilities from its competitors, given many analytics vendors do not include forecasting in their platforms, according to Doug Henschen, an analyst at Constellation Research.
"Scenario analysis capabilities are an important add for QuickSight and a differentiator versus some -- though not all -- analytics offerings," Henschen said.
IBM, Oracle and SAP are among those providing scenario planning tools within analytics platforms. However, AWS might be the first to provide them through a natural language interface, Henschen added.
"Scenario analysis capabilities are common in planning products, but not so much in business intelligence and analytics products," he said.
Regarding making scenario planning a priority, Jose Kunnackal John, director of QuickSight, said customers requesting deeper analytical capabilities was the main motivator.
Still, even with the addition of potentially unique scenario planning capabilities, QuickSight is not the most advanced analytics platform, according to Leone. Instead, it is one of many offering similar capabilities, each with slight differences that make them appealing to certain customers.
"While Q in QuickSight brings some seriously powerful capabilities to the table -- especially with the new scenarios feature -- it's fair to say the competitive landscape is right there with them," Leone said.
Henschen similarly noted that while QuickSight has steadily improved, it is one of many similarly advanced BI platforms only subtly differentiated from one another.
For example, Tableau's strengths include sophisticated visualizations and a generative AI-powered insight generator, while Microsoft Power BI is appealing because of its low cost, inclusion in Microsoft 365 bundles and generative AI copilots, according to Henschen.
"QuickSight has been gaining ground steadily for years on the strength of its being price-competitive and native to AWS," he said. "With the addition of Q to QuickSight, adoption has accelerated over the last year. This new scenario analysis feature raises the bar."
Looking ahead
With scenario analysis now part of Q in QuickSight, adding more capabilities that make QuickSight accessible to non-technical users in addition to trained experts is paramount, according to Kunnackal John.
Henschen, meanwhile, suggested that AWS develop something akin to Tableau Pulse, a generative AI-powered tool that surfaces insights by monitoring key metrics. Zoho Analytics has already delivered something similar, and other vendors should follow suit because humans can't constantly monitor metrics and surface all insights.
"With analytics alone, you're reliant on the business context being in people's heads, but you can't always rely on that," Henschen said.
Leone likewise cited proactive insight generation as a way for AWS to further improve QuickSight. For example, a feature that provides users with actionable recommendations based on real-time data could be added.
"There is a need to focus on proactive insights," he said. "Right now, Q in QuickSight is largely reactive, responding to user queries. But what if it could anticipate user needs?"
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than 25 years of experience. He covers analytics and data management.
