Auto ML functionality to extend Qlik’s product portfolio with its 6th acquisition in the last 24 months. Commentary by Gernot Molin and Stefan Sexl
Since the major acquisition of Attunity in May 2019, Qlik has extended its portfolio by acquiring the complementary software solutions Knarr (Collaboration), RoxAI (Alerting), Blendr.io (PaaS), Nodegraph (Lineage) and now Big Squid. Qlik obviously follows an acquisition strategy of buying technology with small innovative vendors and integrate this technology into its own platform.
As with the other acquisitions we expect that the product will be fully integrated in the Qlik solution stack, building and end-to-end SaaS offering from data preparation, modeling up to analysis, AI and alerting.
Big Squid is one of the many attractive startups in the segment of Auto ML, an emerging technology to automate manual and repetitive machine learning tasks. It therefore holds the promise of greatly accelerating machine learning projects whilst also reducing the necessity for hardcore data scientist skills within those ML projects.
The AutoML market splits into three Groups: OpenSource Libraries, Startups and Tech Giants.
– As with everything around ML the open-source market is also here a strong player. However as is to be expected with open-source libraries they usually target specialists and need in depth python and R development know-how. Examples: Auto-sklearn, Auto-Keras, Caret
– The Big Tech-Giants also play a formidable role in the AutoML arena. Notably Google was one of the first TechGiants to deliver solutions in this space. By now all HyperScalers have an offering here.
– Thirdly there are the startups which are raising large amounts of money to capture market share in this space. DataRobot and H2O.ai are some good examples for some already established startups that are formidable players in the area of AutoML. Most startups aim to offer on the promise that tools can be launched with a clean UI that can be used by non-technical users. Many tools also offer visualizations to explain their findings from the resulting ML model (i.e. explainable AI). Here startups like Big Squid are particularly interesting that really aim to bring no-code AutoML to non-technical users.
As with some of the other acquisitions, Qlik has therefore picked a vendor that targets end users rather than tech experts, obviously orienting the whole portfolio to the rather non-technical user. As with the other acquisitions, we expect Qlik to integrate the front-end step by step to offer an unified user experience.
Combined with the traditional core strengths of the Qlik platform like database performance, scalability and customer satisfaction (see BI Survey evaluation) we see Qlik positioning themselves as end-user oriented complete offering for Data & Analytics.
Generally, the availability of easy-to-use Auto ML solutions in the major BI & reporting platforms will accelerate the market acceptance of Machine Learning and extend the number of users. The availability of this technology in existing platforms with a broad user base like Qlik will give many users the impetus to initially engage with it.