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Amazon Web Services (AWS) last week launched QuickSight Q — a new capability within its existing cloud-based business intelligence tools system, Amazon Quicksight — that allows users to query their data using natural language.
Enterprise users can type questions about their business data in natural language and receive answers and visualizations in seconds, according to AWS’ announcement last week.
Despite AWS’ leadership in overall cloud service offerings, Amazon has remained a niche player in business intelligence tools (BI), according to research firm Gartner. Other Amazon QuickSight competitors have launched similar AI-powered business intelligence features. Earlier this year, for example, Tableau launched a new feature, Ask Data, that similarly “lets users answer business questions with natural language, autocorrection, and synonym recognition.” Analysts curate use cases by querying data in the context of their business needs.
To compete with tools like Tableau, QuickSight Q aims to lower the barrier to entry. It does not depend on prebuilt dashboards and reports, so users aren’t limited to asking only a specific set of questions. It uses machine learning (natural language processing, schema understanding, and semantic parsing for SQL code generation) to automatically understand meaning and relationships between business data — freeing BI teams from the time-consuming task of updating calculations, visuals, reports, and dashboards each time a user has a new question.
Widespread adoption of QuickSight Q will almost certainly put pressure on competitors to build more user-friendly data analytics and visualization tools. QuickSight Q will also help Amazon Web Services maintain a competitive advantage over Microsoft Azure and Google Cloud, which offer similar business intelligence services: Microsoft Power BI and Looker. Amazon Web Service’s investment in business intelligence analytics follows larger trends in the global big data and business analytics market, which is forecast to grow from $168.8 billion in 2018 to $274.2 billion by 2022, according to Statistica.
QuickSight Q is betting its natural language processing technology will empower users with little background in business intelligence to generate actionable insights based on data. The new capability offers auto-complete suggestions for key phrases and business terms and performs spell checking and acronym/synonym matching. Thanks to these features, users don’t need to remember exact business terms or worry about spelling unfamiliar words correctly. When the insights or visualization miss the mark, users can revise their questions using an editor that helps QuickSight Q’s models improve.
Customers don’t need to pay for a subscription or sign a contract, and prices are determined by the number of users or number of queries.
The QuickSight Q launch comes at a pivotal time in the data analytics industry. The pandemic has accelerated a number of important trends, including the expectation that AI serve more practical use cases. Enterprises are also starting to recognize the importance of data-driven decision-making, as well as the need to help employees without robust analytics backgrounds make decisions based on relevant data.
From one perspective, QuickSight Q is part of a larger movement to democratize data literacy. Many business intelligence tools require teams to spend months onboarding, preparing data in advance, and creating models that are tied to the company’s business goals. QuickSight Q tackles this problem by leveraging AI models to create dashboards quickly and ostensibly without sacrificing accuracy. The dashboards interpret and describe what the data is doing in plain language so someone without a data background could theoretically understand and act on any relevant insights.
The new capability will also make it easier for users to make decisions based on real-time analytics. Enterprises are sitting on troves of data, but inconsistent data quality and lack of data governance means companies are tracking and analyzing outdated data. With QuickSight Q, users can generate visualizations and insights based on real-time trends. As an example, retailers might use QuickSight Q to adjust prices on specific items based on real-time shopping trends.
QuickSight Q has use cases in a wide range of industries, underscoring the need for improved data analytics across a number of sectors. AWS chief evangelist Jeff Barr wrote in a blog post that the technology is “aided by models that have been trained to recognize vocabulary and concepts drawn from multiple domains,” including sales, marketing, retail, HR, advertising, financial services, and health care. Current customers include the NFL, Forwood, and PeopleScout.
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