High-Level Overview
Big Squid was a technology company that developed Kraken, an automated machine learning (AutoML) platform designed to enable business decision-makers without data science expertise to build predictive models, forecast metrics, and run "what if" scenarios for faster, data-driven decisions.[1][2] It served data and analytics teams across industries by simplifying AI adoption, addressing the gap in resources for advanced analytics like key driver analysis and scenario planning.[4] Founded in 2009 (with some sources noting activity ramping up around 2016), the company raised $18.68M before being acquired by Qlik on September 30, 2021, integrating its no-code capabilities into Qlik's augmented analytics platform to enhance predictive insights.[2][4]
Post-acquisition, Big Squid's technology bolsters Qlik's offerings, helping organizations move from descriptive analytics toward proactive, AI-powered planning, with a focus on ease-of-use for non-experts.[1][4]
Origin Story
Big Squid emerged in 2009 (or circa 2016 per growth-focused accounts) in Salt Lake City, Utah, at 224 S 200 W, Suite 110, targeting the need for automation and scale in data analytics through a blend of services and software.[1][2][3] Founders are not named in available records, but the company quickly honed in on democratizing machine learning for business users lacking traditional data science skills, launching the Kraken platform as its core predictive toolkit.[1][2] Early traction centered on making AI approachable, gaining recognition for no-code model building amid rising demand for accessible analytics; this culminated in its 2021 acquisition by Qlik, a pivotal moment that embedded its tech into a global analytics leader serving 38,000+ customers.[2][4]
Core Differentiators
- No-code AutoML accessibility: Kraken allowed non-experts to generate trusted AI models for predictions, forecasts, and what-if analysis without coding, lowering barriers for business decision-makers.[1][2][4]
- Seamless integration and deployment: Models deploy via API directly into existing analytics workflows, enabling on-demand insights like key driver analysis within platforms like Qlik.[4]
- Focus on predictive planning: Emphasized "what might happen and why," helping teams explore scenarios and act with certainty, differentiating from basic data warehousing.[1][4]
- Proven scalability: Raised $18.68M and powered real-world AI adoption, now enhancing Qlik's end-to-end data integration for Active Intelligence.[2][4]
Role in the Broader Tech Landscape
Big Squid rode the democratization of AI trend, accelerating the shift from data warehousing to customer-centered analytics and augmented intelligence at a time when most organizations lacked ML expertise.[1][4] Its timing aligned with surging demand for no-code tools amid the 2010s AI boom and post-2020 analytics maturity push, fueled by market forces like talent shortages and the need for real-time, predictive decision-making in uncertain environments.[4] By integrating into Qlik, it influences the ecosystem by embedding AutoML into enterprise platforms, empowering 38,000+ users across 100+ countries to bridge data-to-action gaps and drive revenue optimization.[4]
Quick Take & Future Outlook
With its tech now core to Qlik's augmented analytics, Big Squid's legacy evolves through expanded AI capabilities, potentially powering advanced scenario planning in Qlik's cloud platform.[4] Trends like generative AI integration and real-time Active Intelligence will shape its trajectory, amplifying no-code predictions amid growing data literacy demands. Its influence may grow as Qlik scales these tools globally, solidifying Big Squid's role in making AI a standard for business foresight—echoing its original mission to unlock untapped data potential for smarter, faster decisions.[1][4]
