Dataiku Nabs $400 Million in Quest to Democratize AI
Data science platform maker Dataiku announced today that it has reeled in $400 million in venture capital, giving it nearly $647 million in funding since it was founded in 2012, and a market capitalization of $4.6 billion. The Series E round gives the company more dry powder to continue its quest to democratize AI and advanced analytics, and to help customers fight the “FAANG mafia,” as its CEO puts it.
Dataiku focuses its product development efforts on creating enterprise AI solutions that can be used by data and business analysts, as opposed to strictly being used by highly trained data scientists, who are expensive and hard to find. We are still millions of data scientists short of meeting demand for data scientists, and Dataiku is determined to help make up the difference by creating better tools that give data analyst and regular business users the ability to work with data and create AI products.
“We differentiate by providing a collaborative visual data experience, for ML, data preparation, visualization, and ML operations and optimization,” Florian Douetteau, the CEO of Dataiku, told Datanami in a June interview. “We bill the product as a product usable by a very large audience, meaning the analysts, meaning people with essentially business skills, and then onboarding…the data engineers and data scientists [into the journey].”
In prior ages, data scientists were required to build big data and AI applications. But automation in the data exploration and model development tools means full-blown data scientists are needed only 10% to 20% of the time, according to Douetteau.
“Lots of things can be done within the realm of AI and analytics without having the need of the data scientist in the room,” he said. “And even if you need them in some situations, you don’t’ need them for the whole journey. I think that’s one important aspect.”
In 2018, McKinsey predicted the total impact of AI on the world’s economy by 2030 would be $13 trillion. Data science platform vendors like Dataiku are betting that smaller firms without the budgets to hire armies of data scientists will be looking for their entrée into Enterprise AI solutions.
Douetteau has been outspoken about the need for smaller business to take on the “FAANG Mafia,” i.e. Facebook, Amazon, Apple, Netflix, and Google, in their adoption of AI. (Microsoft is commonly mentioned in the same vein.)
While the tech giants have billions to invest in highly skilled individuals, smaller companies must be more tactical in their approach to technology adoption and hiring practices. The availability of distributed infrastructure on the cloud helps, as do new AI and ML development tools, like Dataiku’s.
“It’s a very strong barrier to entry,” he said of the FAANG’s head start in technology and data. “As a small company, the challenge you have is different. And indeed, by leveraging software engineering solutions, such as ours and also others, you can actually get things done.”
John Curtius, a partner at Tiger Global, the venture capital fund that led the Series E, says Tiger Global is happy to help “systematize data and AI” with its investment in Dataiku.
“We’ve seen that executing an AI strategy in which data is a part of day-to-day operations can have large-scale impact for organizations across sectors and sizes, and Dataiku is well-positioned to continue to help the enterprise realize this potential value given both the strength of their technology and the team,” Curtius states in a press release.
Tiger Global was joined by a roster of other investors in the Series E, including existing investors ICONIQ Growth, CapitalG, FirstMark Capital, Battery Ventures, Snowflake Ventures, and Dawn Capital. The round also included new investors, including Insight Partners, Eurazeo, Lightrock and Olivier Pomel, the CEO of Datadog.
This was Dataiku’s biggest funding round since April 2020, when the company announced a $100 million Series D led by Stripes. It cleared a $101 million Series C in December 2018 led by ICONIQ Capital.