Dataiku Overview

Dataiku is a company that deals with artificial intelligence and Machine learning.  the company was founded in the year 2013 in the city of Paris by Florian Douetteau, Clement Stenac, Thomas Cabrol and Marc Batty. The company boasts a valuation of $4.6 billion and a staff strength of over 1000 employees worldwide with its headquarters in New York City, the United States and branches in Paris,  London, Munich,  Sydney, Singapore and Dubai. The company also has a revenue of $150 million.

Funding history of the company.

Two French technology venture capital groups, Serena capital and Alven Capital, contributed 3.6 million to Dataiku in January 2015.

Next, in October 2016, $14 million was raised with the help of FirstMark Capital, a venture capital firm based in New York City.

The business received a $28 million Series B investment in September 2017 from Battery Ventures and other previous investors.

Dataiku revealed a $101 million Series C fundraising round led by ICONIQ Capital in December 2018. Alven Capital, Battery Ventures, Dawn Capital, and FirstMark Capital were additional investors. The business was listed in the Forbes Cloud 100, a list of the top 100 private cloud providers worldwide, in September 2019.

The company reported that Capital G, the late-stage growth venture capital fund backed by Alphabet Inc., has acquired some shares formerly held by Serena Capital in a secondary round valued the company at $1.4 billion, making it a unicorn, in December 2019, one day after the launch of Dataiku 6.

A $100 million series D fundraising round headed by Stripes and Tiger Global Management and involving current investors Battery Ventures, Capital G, Dawn Capital, FirstMark Capital and ICONIQ was announced by Dataiku in August 2020. The business declared it was “still a unicorn,” but did not provide its new worth.

Dataiku announced another $400 million Series E round in August 2021, valuing the company at a total of $4.6 billion.

Company’s product.

In 2014, Dataiku created software called Data Science Studio. The software supports predictive modelling to create business applications. Later versions of the software included other features. Dataiku applications are a kind of DSS customization that allows you to reuse projects. They can be surfaced in two different ways which are: visual applications that allow users to package a project with a GUI on top, which empowers more people within an organization to leverage AI and self-service analytics.

The tag phrase for the company since 2018 has been “your path to enterprise AI”, Which shows the importance that every company has its own unique path to follow in order to leverage AI In the enterprise.

Picture is not an exception as the company enables organizations to speed up all transformations no matter where they are [on-premise, hybrid cloud or full cloud].

How does Dataiku make cloud integration easy?

According to survey dawn of 200 + IT executives, 64% of companies have a hybrid cloud approach when it comes to projects concerning machine learning. This implies that on-premise data centres and private cloud services are combined with public cloud services. This process, however, doesn’t come without challenges, full maturity in this domain will come with the seamless combination of multiple clouds and on-premise solutions into hybrid architecture.

Moving to cloud providers and the integration of open-source technologies is not slowing down and doesn’t seem like it will slow down anytime soon. With this information, companies need a platform like Dataiku that de-couples skills, people and the projects they’ve established from the underlying technologies and infrastructure. That way if the technology changes or the company changes from one cloud to another users can continue utilizing their current skills to maintain existing projects and create new ones with minimal distraction.

Dataiku enables companies to

• Move from one underlying technology or cloud provider to another with minimal effects on its data and AI projects, which reduces the risk of technological lock-in and strategic cloud dependency.
• Switch data sources and computing engines from one technology to another,  this makes it easy to maintain projects as new options surface.
• Run AI and data projects across multiple platforms, allowing companies to operate across on-premises installations and cloud infrastructure.

Originally, data systems included both infrastructures for computation and storage which implied that both would be scaled simultaneously even if one dimension was resource constrained. The solution to this problem is to use de-coupled computation and storage systems so that workloads can be scaled independently. Dataiku utilizes a pushdown architecture to enable companies to take full advantage of distributed and highly scalable computing systems including SQL engines. Spark and Kubernetes as well as optimized data routes to access elastic storage systems. This enables companies to get insights faster as leveraging cloud elasticity helps teams to scale resources up and down to meet with their project needs easily. With Dataiku controlling these resources, users are automatically given access to the right technology for their projects at the right time.

Dataiku helps companies quickly realize the value of cloud providers [AWS, GCP and Microsoft Azure]  by allowing teams to partner and adopt data science practices. Dataiku’s end-to-end platform and cloud agnosticism make it easy for companies to use the cloud providers and their available services while allowing users of all levels to rapidly go from data exploration ad preparation to fully establish AI applications, without depending exclusively on technical experts.

Dataiku offers a free edition and enterprise versions with additional features such as multi-user partnerships or real-time scoring.