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Yellowfin analytics platform update adds NLQ capabilities

Yellowfin 9.7, the vendor's latest platform update, includes Guided Natural Language Query, a tool that enables users to explore and analyze data without writing code.

New natural language query capabilities highlight the latest Yellowfin analytics platform update.

The vendor, founded in 2003 and based in Melbourne, Australia, unveiled version 9.7 of its platform on Dec. 4, which includes the general availability of Guided Natural Language Query (NLQ).

Additional Yellowfin 9.7 features and capabilities address data governance, application programming interfaces and the vendor's mobile app experience.

NLQ capabilities

With Guided NLQ, Yellowfin analytics customers are not only able to ask questions of their data using natural language rather than code, but they're also guided through thousands of modeled questions by the tool itself.

Examples, according to Yellowfin, include data queries such as, "Show sales by product categories and highlight outliers for this month." Yellowfin then responds by providing a visualization and report -- including analysis, in some cases -- within seconds.

NLQ tools, however, are not new for analytics vendors. ThoughtSpot, for example, designed its platform around natural language search. Sisense and Tibco are among the vendors that have added NLQ features over the past couple of years, and Oracle, Qlik and Tableau are among those with established NLQ capabilities.

As a result, adding Guided NLQ to the Yellowfin analytics platform is viewed as a catch-up move by analysts. But it's also viewed as one that will benefit users, enabling those who already work with data to more easily explore, analyze and gain the insights that lead to data-driven decision making.

"Guided NLQ isn't breaking any new ground in technology, but does offer a refreshingly practical approach aimed primarily at analysts and power users who know their way around BI and analytics platforms," said Doug Henschen, an analyst at Constellation Research.

The tool includes options to select Focus Areas and Filter Groups already available in Yellowfin View, which help ensure that data users get the information they're looking for, he continued.

Similarly, Donald Farmer, founder and principal of TreeHive Strategy, noted that Guided NLQ adds useful capabilities.

Yellowfin makes it work well, as with other features, because of the care they have put into this design.
Donald FarmerFounder and principal, TreeHive Strategy

"Yellowfin has taken a productive approach by guiding the user through that query construction while still retaining the flow of natural language," he said. "They are not the first to build semi-natural language queries this way, but Yellowfin makes it work well, as with other features, because of the care they have put into this design."

Farmer added that one way Yellowfin makes Guided NLQ work well is by incorporating the tool throughout its analytics platform.

"I think they have done good work integrating this approach all through the Yellowfin platform such as reports and stories, not just adding to dashboards," he said.

A sample Yellowfin dashboard
A sample dashboard from analytics vendor Yellowfin displays an organization's tequila sales.

NLQ's potential

While Henschen noted that Guided NLQ may be best-suited for experienced data analysts and power users within organizations, NLQ in general is seen as a capability that has the potential to extend the use of analytics to a broad base of users.

Depending on the source, it's estimated that fewer than one-third of employees within most organizations use analytics in their jobs, and that number has held steady for years.

Technologies such as NLQ and automated data storytelling, however, eliminate the need to know code and possess the skills of data scientists and data analysts. Therefore, they have the potential to open analytics use to more business users -- using workflows they already know rather than BI platforms -- as the sophistication of NLQ and data storytelling tools increase.

"Used correctly, NLQ is increasingly powerful," Henschen said. "The next phase will be exposing this feature outside the confines of reports and dashboards so it's a utility for data-driven decision making and decision support that's available pervasively within applications and processes."

As a result, analytics vendors are emphasizing NLQ and data storytelling capabilities. For example, beyond those who have had such capabilities for years, AWS recently introduced NLQ feature QuickSight Q to its BI platform and Salesforce reached an agreement to acquire data storytelling vendor Narrative Science with plans to roll it into Tableau.

Yellowfin has long prioritized data storytelling, first introducing Yellowfin Stories in 2018, and its July 2021 update integrated Stories with the rest of its platform rather than keeping it a standalone feature.

Guided NLQ, meanwhile, does include an "Ask a Question" feature that makes it suitable for business users in addition to experienced analysts and power users, according to Henschen.

"The 'Ask a Question' feature available within Dashboards can be used to simplify things for business users," he said. "It's a low-tech way to get people to the right information and deeper insights they're after."

Additional features

Beyond Guided NLQ, the latest Yellowfin analytics platform update incudes 20 more new capabilities.

Among them are an integration between Stories and Guided NLQ, Mobile App support for multi-chart canvas and tabular reports, new options for waterfall charts, enhanced REST API capabilities, and data governance features like command-line configuration and single-sign on options.

"Quietly slipped into this release are a handful of good enterprise management and deployment features which reinforce just how much Yellowfin has grown into a first-rate enterprise analytics platform," Farmer said. "None of these will make the headlines, but they are important and telling features for IT teams managing Yellowfin in the most demanding environments."

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