Gartner_logo_white-1

Get the Gartner Hype Cycle™ for Data Science and ML

Uncover Top Trends for 2023 & Beyond

Time and time again, we hear how organizations are increasingly investing in AI, but struggling to generate (and maintain) actual business impact from their analytics and AI projects. How can they sift through what’s actually worth the resources versus what’s purely hype?

The Gartner Hype Cycle for Data Science and Machine Learning highlights what data science and ML concepts have high or transformational benefits, their business impacts, key drivers, obstacles to implementation, and more.

Get the Gartner Hype Cycle

Speed to Value + Relevance Are Key

Start With Systemization

According to Gartner, “The two most prominent drivers within data science and machine learning are:

  • Speed to deliver value from investments with the pace at which businesses operate
  • Applicability to be relevant to the requirement, while providing transparency and wider usage across the enterprise.” 

At Dataiku, we believe that a systemized approach to AI requires empowering all people (including the business) in a central place, governing the lifecycles of all AI projects, and accelerating the time it takes to deliver AI projects from months to days. 
GET THE FULL EBOOK ON SYSTEMIZATION
Figure1

 

Everyday AI New York conference

Everyday AI: The New Wave of Democratization

You Won't Scale AI Without Enlisting Non-Experts to the Cause

According to Gartner, “DSML is long past just being limited to expert data scientists. It is now widely adopted by business experts, self-service business analysts, or other people that don’t have a technical background in programming or machine learning.” 

Dataiku allows companies to leverage one central solution to design, deploy, govern, and manage AI and analytics applications, and it's accessible for everyone (whether technical and working in code or on the business side and low- or no-code). Everyday AI is what helps organizations execute faster by including more people in analytics processes.
MORE ON EVERYDAY AI

Gartner, Inc., Hype Cycle for Data Science and Machine Learning, 2022. Farhan Choudhary, Peter Krensky, 29 June 2022. GARTNER and Hype Cycle are a registered trademark and service mark 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 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 or other designation. 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.

Copyright © 2023 Dataiku. All rights reserved. | Online Privacy