Dataiku Named a Gartner Magic Quadrant Leader 3 Times Running

Dataiku Company, Dataiku Product, Scaling AI, Featured Catie Grasso

Today, we’re proud to announce that we’ve been named a Leader for the third year in the 2024 Gartner Magic Quadrant for Data Science and Machine Learning Platforms. Among all evaluated vendors, Dataiku is placed furthest on the “Completeness of Vision” axis. 

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Dataiku Is a Leader & the Universal AI Platform

Dataiku — the Universal AI Platform — is the only platform on the market that unifies all your data work, from analytics to Generative AI, for maximum Return on AI (ROAI). Below, we highlight some of the strengths outlined by Gartner in the report, including Generative AI and cross-team collaborative features, to demonstrate how Dataiku is the unmatched choice for organizations looking to power AI transformation at scale and also control AI operations:  

GenAI Vision

The Generative AI landscape will continue to shift and evolve, with different technologies and providers coming and going. Dataiku is the only infrastructure-independent provider, connecting to existing infrastructure and integrating with the latest and greatest technology of today and tomorrow, so teams can experiment and switch underlying architectures easily.

The Dataiku LLM Mesh is an enterprise-ready approach to the rise of Generative AI, focused on providing enterprise-wide usage of Generative AI models to abstract away the complexity of managing cost and performance. As you can see in the visual below, our partners are a critical piece of this puzzle, helping us address the critical need for an effective, scalable, and secure platform for integrating LLMs in the enterprise: 

LLM Mesh

Further, LLM Cost Guard enables effective tracing and monitoring of LLM usage, including ready-to-use dashboards to better anticipate Generative AI costs. 

Many Dataiku customers already have Generative AI use cases in production, including:

  • Heraeus, who uses LLMs in Dataiku to support sales lead identification and qualification processes, saving up to 70% of time along the way.
  • LG Chem, who built GenAI-powered services to enable employees to find safety regulations and guidelines quickly and accurately. 
  • Ørsted, who uses an LLM-driven news digest to ensure its executive team has a more aligned understanding of market dynamics.
  • Whataburger, who created an LLM-powered dashboard to come through thousands of customer reviews each week. 

Collaboration

At Dataiku, we’ve been talking about collaborative data science since our founding in 2013 (in fact, it was the impetus behind the creation of the Dataiku platform at the time). MIT professor Thomas Malone reinforced this notion of collective intelligence when he said, "Almost everything we humans have ever done has been done not by lone individuals but by groups of people working together, often across time and space.”

Dataiku drives usability by everyone, from the code-free to the code-first, bringing intelligence to all through: 

  • Accessible and widespread consumption: Bringing valuable insights and self-service analytics to the masses via applications, dashboards, and reports.
  • Projects and a visual flow: Everyone on the team benefits from common objects and visual representation of the step-by-step approach in an analytics project, which ensures the entire process is documented for future users.
  • Discussions, Comments, Tags, and Wikis: Project collaboration happens over time with team members and stakeholders in different roles and at different stages of the project lifecycle. Documenting motivations behind the project as it moves from design to production with a Wiki, Comments, or Tags preserves knowledge about elements within the project for current and future users.
  • Automatic model documentation: Organizations can easily generate records and documentation of how a model was built and how it has performed during its lifecycle for both regulatory compliance and explainability purposes. Having these assets generated automatically frees up data scientists to continue innovating, rather than maintaining the technical debt of existing model systems. 
  • Version control: Every change users make within Dataiku is automatically recorded in the project repository. This allows teams using Dataiku to easily trace all actions performed on their project, revert back to prior versions of objects, know their history, and work in multiple versions as they build production-ready AI projects.

We see collaboration in practice everyday with our customers. Zeus uses Dataiku Cloud to drive collaboration and visibility on enterprise-wide analytics projects, from process engineers to operators to plant managers. The corporate engineering team at Oshkosh uses Dataiku to facilitate and enable project collaboration between data scientists, analysts, and management. Regeneron found success with Dataiku by bringing together the right mix of wet-lab scientists, data scientists, and compute specialists onto their BioPerceptron platform, a deep learning solution built with Dataiku for biomedical image processing.

People-First Approach

As data science, ML, and AI adoption continue to rise and competition continues to intensify, companies will continue to take on higher-value projects and will need that common thread of collaboration to be clear throughout each and every one of them if they expect to actually scale their data efforts across the business. 

We believe that our commitment to cross-profile collaboration makes us the people-first AI platform of choice for use across the enterprise. With Dataiku, organizations can unite their teams, operations, and technology in one central place to succeed in the era of Generative AI. By focusing on the impact of AI on people and business, AI leaders can have the confidence to utilize the technology and effectively navigate challenges around infrastructure, architecture, governance, and more.

Dataiku was designed to support everyone, uniting people across teams (from data to domain experts) in working with data, meeting them where they are with no-, low-, and full-code features. In a Gartner Peer Insights review, one data scientist in the insurance industry said,

Bottom line … Dataiku has increased the throughput of our data science team, supercharging the amount of value we provide to the business. Its unique combination of features allowed us to create a common workflow, enhanced by CICD pipelines and automations, across coders and non-coders alike.

What Does the Future Hold for Dataiku? 

I sat down with Dataiku co-founder and CEO Florian Douetteau for his vision of what’s next for Dataiku, including his thoughts on how the Generative AI landscape will evolve (and how Dataiku will continue to support the technology). Check out the full video below:

 

Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, Afraz Jaffri, Aura Popa, Peter Krensky, Jim Hare, Raghvender Bhati, Maryam Hassanlou, Tong Zhang, 17 June 2024. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. GARTNER is a registered trademark and service mark, and MAGIC QUADRANT and PEER INSIGHTS are trademarks and service marks, of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Dataiku. Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, not do they represent the views of Gartner or its affiliates. 

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