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Qlik unveiled the acquisition of Big Squid in a move aimed at advancing the automated machine learning capabilities of the analytics vendor's platform.
Financial terms of the acquisition, revealed on Sept. 30, were not disclosed.
Big Squid, founded in 2009 and based in Salt Lake City, is a developer of predictive analytics software that uses machine learning (ML) capabilities to fuel predictive models. Its tools enable customers build models without having to write code, and the models get smarter over time as they're automatically fed with pertinent data as it gets collected.
Qlik, founded in 1993 and based in King of Prussia, Pa., already offers some machine learning capabilities through Insight Advisor, and has augmented those with partnerships with vendors including AWS, resulting in a connector with Amazon SageMaker, and DataRobot.
The addition of Big Squid, however, will make it easier for users without coding knowledge to build machine learning models and derive insights based on those predictive capabilities, according to Josh Good, vice president of product marketing data analytics at Qlik.
"It will make it seamless for data and analytics teams, who lack modeling expertise and resources, to execute predictions and scenarios right inside of Qlik themselves, which will expand the value of the insights they're already working on directly within Qlik," he said.
He said that adding Big Squid's autoML capabilities directly into Qlik will enable data and analytics teams to explore more potential scenarios and become more predictive.
"This has become more and more important in a world where market conditions change rapidly and [customers] want to leverage real-time data to react to multiple unplanned scenarios," Good said. "With autoML built directly into Qlik, teams can explore a whole range of new possibilities … without needing to tap data scientist expertise that's in short supply for most companies."
Among them, he continued, are more deeply examining key drivers, looking at what-if scenarios and running on-demand predictions.
Meanwhile, because of the added ways Big Squid's capabilities will enable Qlik users to explore their data and build predictive models, it is a good acquisition for Qlik, according to Donald Farmer, founder and principal of TreeHive Strategy.
Donald FarmerFounder and principal, TreeHive Strategy
"They are a natural fit for Qlik, which has been adding some automated insight features but really has not enabled users to build robust, scalable machine learning models," he said. "This acquisition ensures the Big Squid technology can develop further into a more complete machine learning stack, while also giving Qlik a much-needed injection of 'real' data science modeling and skills."
Regarding Big Squid's technology, Farmer added that its ease of use makes it a particularly strong complement to Qlik's existing ML capabilities.
"The technology is good -- automated machine learning with an emphasis on simplicity and ease of use," he said. "They did excellent design work to reduce the complexity and time involved in deploying machine learning models for business intelligence users."
The purchase of Big Squid is the latest for Qlik in a growing line of acquisitions aimed at advancing the capabilities of the vendor's platform from a focus on business intelligence to what it calls active intelligence.
Active intelligence is the ability to deliver data and analytics in real time to customers wherever they may be at any moment to fuel data-driven decision-making.
Other Qlik acquisitions include Podium Data in 2018 to add data management capabilities, Attunity in 2019 to add data integration capabilities, Knarr Analytics and RoxAI in early 2020 to add alerting prowess and Blendr.io in late 2020 to expand its ability to integrate with SaaS and cloud data storage platforms.
New automation capabilities
In addition to its acquisition of Big Squid, Qlik on Sept. 28 introduced Qlik Application Automation, a no-code tool that automates workflows between SaaS applications and Qlik Cloud.
Qlik previously had automation capabilities for alerting, reporting and API integrations, according to Good. Its Application Automation feature, meanwhile, which is a result of capabilities attained through the acquisition of Blendr.io, eliminates the manual task of integrating SaaS applications and automates them with a no-code user interface, he continued.
In addition to the no-code user interface, Application Automation includes smart connectivity to SaaS applications such as Salesforce and Microsoft Teams, native integration with Qlik Cloud, automation triggers and scheduling capabilities, and centralized management.
"This workload is a significant bottleneck to users getting access to all their relevant SaaS application data, be it from … any of the hundreds of other applications in use daily across an organization," Good said. "Teams can build workflows in minutes that create the flow of data into the cloud for analysis and into downstream systems to trigger insight-driven action."
And that flow into downstream systems to trigger data-driven decisions enables active intelligence, according to Good.