Though we haven't been immune to the events of 2020, here at Dataiku, we're very fortunate to have an agile team that has been able to adapt to new circumstances as well as a product whose value has proven integral to organizations’ own recovery from this crisis. That's why we're proud to announce that hot on the heels of our $100 million Series D investment round as well as the release of Dataiku 8, this week, Dataiku has been named a Leader in the The Forrester Wave™: Multimodal Predictive Analytics and Machine Learning Solutions, Q3 2020. Earlier in 2020, Dataiku was also named a Leader in the Gartner Magic Quadrant for Data Science and Machine-Learning Platforms.
Dataiku has always stayed true to the vision that in order to stay relevant in a changing world, companies need to harness Enterprise AI as a widespread organizational asset instead of siloing it into a specific team or role. That's why from the beginning, Dataiku has provided one simple UI for data wrangling, mining, visualization, machine learning, and deployment based on a collaborative and team-based user interface accessible to anyone on a data team, from data scientist to beginner analyst.
We believe that our placement in the Forrester Wave (on top of all the other developments in the market and at Dataiku this year) solidifies this vision, showing the world that for Enterprise AI to provide business results — including making an organization more agile across all teams — companies need a tool that orchestrates end-to-end as well as brings all profiles together.
More than 450 people around the globe at offices in New York, Paris, London, Munich, Sydney, and Singapore make Dataiku work. The excitement of 2020 has been in huge part thanks to the teamwork and collaboration that we prioritize not only in our product, but in our own company. Together, we're looking forward to taking our current end-to-end platform offering even further with more solutions, more service offerings, and more product offerings to continue to be the AI platform, even as organizations graduate to having hundreds, thousands, or hundreds of thousands of machine learning models in production.