Qlik announced the acquisition of Big Squid to enhance the automated machine learning capabilities of the analytics provider’s platform.
The financial terms of the acquisition, which were announced on September 30th, were not disclosed.
Big Squid was founded in 2009 and is based in Salt Lake City. It is a developer of predictive analytics software that uses machine learning (ML) to support predictive models. Its tools enable customers to build models without writing any code, and the models get smarter over time, as they are automatically fed relevant data as they are captured.
Qlik, founded in 1993 and based in King of Prussia, Pennsylvania, already offers some machine learning functions through Insight Advisor and has expanded these through partnerships with providers such as AWS, resulting in a connector with Amazon SageMaker and DataRobot.
However, the addition of Big Squid will make it easier for those with no programming skills to build machine learning models and derive insights based on those predictive capabilities, said Josh Good, vice president of product marketing data analytics at Qlik.
“This allows data and analysis teams that lack modeling expertise and resources to seamlessly execute predictions and scenarios directly in Qlik themselves, which further increases the value of the insights they are already working on directly in Qlik,” he said.
He said that the integration of Big Squid’s autoML capabilities directly into Qlik will allow data and analytics teams to explore more potential scenarios and become more predictive.
“This will be in a world where market conditions are changing rapidly and [customers] want to use real-time data to respond to multiple unplanned scenarios, ”said Good. “With autoML, which is built directly into Qlik, teams can explore a whole host of new opportunities … without having to rely on the expertise of data scientists, which is in short supply for most companies.”
Among them, he went on, they examine the key drivers in more depth, examine what-if scenarios, and make on-demand predictions.
It is a good acquisition for Qlik, according to Donald Farmer, founder and director of TreeHive Strategy, because of Big Squid’s additional capabilities that allow Qlik users to explore their data and build predictive models.
Donald FarmerFounder and Director, TreeHive Strategy
“They are a perfect fit for Qlik, which added some automated Insight functionality but didn’t really allow users to build robust, scalable machine learning models,” he said. “This acquisition ensures that Big Squid technology can evolve into a more complete machine learning stack while also giving Qlik a much-needed injection of ‘real’ data science models and capabilities.”
Regarding Big Squid’s technology, Farmer added that its ease of use makes it a particularly powerful addition to Qlik’s existing ML capabilities.
“The technology is good – automated machine learning with an emphasis on simplicity and usability,” he said. “You did an excellent design job to reduce the complexity and time it takes to deploy machine learning models to business intelligence users.”
The acquisition of Big Squid is the latest in a growing series of acquisitions for Qlik aimed at expanding the capabilities of the vendor’s platform from a focus on business intelligence to what is known as active intelligence.
Active intelligence is the ability to provide real-time data and analytics to customers anytime, anywhere to support data-driven decisions.
Other acquisitions by Qlik 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 increase notification capabilities, and Blendr.io in late 2020 to expand its ability to Expand integration with SaaS and cloud data storage platforms.
New automation functions
In addition to acquiring Big Squid, on September 28, Qlik introduced Qlik Application Automation, a no-code tool that automates workflows between SaaS applications and Qlik Cloud.
According to Good, Qlik previously had automation capabilities for alerts, reports, and API integrations. The application automation feature that comes from the skills acquired through the Blendr.io acquisition removes the need for manual integration of SaaS applications and automates them with a no-code user interface, he continued.
In addition to the no-code user interface, application automation includes intelligent connectivity to SaaS applications such as Salesforce and Microsoft Teams, native integration with Qlik Cloud, automation triggers and planning functions, and centralized administration.
“This workload is a significant bottleneck for users who have access to all of their relevant SaaS application data, be it from … one of the hundreds of other applications that an organization uses every day,” said Good. “Within minutes, teams can create workflows that generate the flow of data to the cloud for analysis and to downstream systems to trigger knowledge-driven actions.”
And this flow into downstream systems to trigger data-driven decisions enables active intelligence, according to Good.