Dataiku secures $14 million Series A funding

Dataiku Inc., an emerging market leader in end-to-end advanced analytics and collaborative data science, has announced a $14 million Series A investment round, led by New York venture capital firm, FirstMark Capital, with participation from all existing investors.

  • 8 years ago Posted in
Dataiku offers a unique collaborative tool that enables teams of data scientists and data analysts to quickly prototype and easily deploy scalable data-driven solutions in production, across the enterprise. 
 
With this latest fundraising round, Dataiku will substantially accelerate its commercial efforts in the United States, Europe, and Asia.  Dataiku is already serving over 100 customers – from SMBs to Fortune 500 companies - across industries and around the world.  The company experienced 300% growth in 2016, doubled its teams in Europe and in the United States last year alone, and welcomed new customers such as NPR, L’Oreal, Hostelworld, Bechtel, and other Fortune 1000 companies to its community of users.  In addition, Dataiku works with technology partners such as Microsoft, Cloudera, Tableau, Databricks, and HP Vertica amongst a dozen others.
 
Dataiku has been profitable since its founding in 2013 and had raised an initial $3.6M seed round (led by Alven Capital and Serena) prior to this $14M Series A.
 
“As the Big Data market reaches early maturity, the time has come for a platform that makes everything and everyone work together, whether it is Hadoop, machine learning, data scientists or data analysts, and that is exactly what Dataiku offers”, said Matt Turck, Managing Director at FirstMark Capital.  “It is a formidable challenge to build a platform that can do so much, and I am deeply impressed by the breadth, depth, and maturity of Dataiku’s product: powerful data blending and wrangling capabilities, a rich native coding environment for data scientists, intuitive tools for data analysts, powerful collaboration features, and rock solid capabilities to deploy predictive analytics in production enterprise-wide, with deep attention to security and data governance.  The market has rightly recognized this feat, and Dataiku has been growing explosively. I am thrilled that Dataiku chose FirstMark to lead their Series A.”
 
As the demand for in-house built data solutions continues to be a major challenge for most organizations, Dataiku recognizes  the need to empower data teams with a platform that streamlines and accelerates the development process -  from raw data to complete predictive application.  
 
“At Dataiku, we believe anyone should be able to contribute to the data science process. Every individual can be effective if given the freedom to use the technologies, languages, or interface that he or she knows best. That’s also how business and IT teams can work together, which is the key for a truly data-driven enterprise. This is why we strive to deliver a platform that maximizes team collaboration and efficiency without removing technical depth and usability,” said Dataiku CEO, Florian Douetteau. “We are proud to have FirstMark Capital by our side and truly honored by their recognition of Dataiku as the solution that will enable organizations to quickly accelerate growth and time to insight by scaling data science. I firmly believe that Dataiku is on its way to breaking the unnecessarily siloed steps involved in the data science process and providing a new kind of data analytics platform which is both cross-user and cross-domain.”
 
This year, Dataiku has tackled the growing problems that organizations face when empowering and scaling data teams, harnessing data governance, and delivering quality data products rapidly around the globe. In August, the company released their latest software update with two major announcements: in addition to becoming completely Spark Native by supporting the widely-adopted programming language Scala (in addition to Python, R, Pig, Hive, SQL, and more), Dataiku 3.1 also announced the full integration of 5 visual machine learning backends (H2O Sparkling Water, Spark MLlib, Scikit-Learn, XGBoost, and Vertica ML), enabling analysts to build models without having to type a single line of code.
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