Gartner Data & Analytics Summit attendees rate “Augmented Data Science” as transformational
Every year I look forward to Gartner’s North American analytics conference. There are always some inspiring keynote speakers, a bunch of informative sessions to attend, and multiple opportunities to reconnect with industry colleagues. And, there always seems to be some annual underlying theme that Gartner wants attendees to take away.
In 2016 the underlying theme was about the emergence of the “citizen data scientist” — the business analyst who wants to advance their career by incorporating elements of data science into their analyses. Last year’s theme was about empowering (or in many cases, creating) the office of the Chief Data Officer (CDO) to lead the data-driven transformation of their organizations.
This year, the message was clear — Gartner believes that “augmented analytics,” an approach that automates insights using machine learning and natural-language generation, marks the next wave of disruption in the data and analytics market.
Innovative Analytics in Action
DataRobot was invited by Rita Salaam to represent innovation in augmented data science during her “Innovative Analytics in Action: Emerging Trends You Need to Know” session that kicked off the conference on Sunday (March 4). Artificial intelligence (AI) vendors ThoughtSpot, Automated Insights, and Stories demonstrated their solutions for natural language processing (NLP), natural-language generation (NLG), and augmented data discovery (respectively).
But, it was the presentation by Yong Kim, one of our customer facing data scientists, that really got the crowd excited. He walked everyone through our vision of what constitutes automated versus manual approaches to machine learning, and then demonstrated how DataRobot automates best practices for data science in the context of solving a hospital patient readmission prediction problem.
At the end of Yong’s presentation, Rita polled the audience to gauge “the potential impact of augmented data science.” More than half of the audience rated it “transformational” (the biggest impact), and the majority of the rest said it would have “high impact” (the next highest classification). Wow!
To wrap up her session, Rita polled the audience about the four technologies they saw that day (augmented data discovery, NLQ, NLP, and augmented data science) to determine:
- Which capability do you think is most relevant to your organization?
- Which capability do you consider to be the most disruptive in the future?
Rita asked attendees not to disclose the exact numbers, but I think it’s fair to say that “augmented data science” was the clear winner in both polls. When the results are published by Gartner, we’ll be sure to share the details.
Machine Learning at the peak of the Gartner AI Hype Cycle
These results align with Gartner’s research that tracks the evolution of technologies along the various “Hype Cycles” that they produce. “Machine Learning” is now at the peak of the Gartner Hype Cycle for Artificial Intelligence — a clear reflection of the buzz that continued to dominate conversations at the conference.
Machine learning, and specifically automated machine learning (aka augmented data science), were repeatedly positioned throughout the week as key technologies that will enable the upskilling of “Quantitative Professionals” (advanced Excel users, Tableau dashboard creators, etc.) to become the “Citizen Data Scientists” that Gartner defined at the conference two years ago.
Who has the Badge Scanner?
We clearly benefited from the wave of interest generated by Yong during his presentation, and the constant references to the importance of adopting augmented data science by Gartner analysts. Our booth was packed during exhibition hours, with conference attendees seeking more information about the various aspects of data science that can be automated.
Trained data scientists, who were skeptical that software could replicate the advanced coding that they do, discovered that DataRobot automates a lot of the pre-processing and feature engineering steps they loathe, and gets them to a fully trained model faster. They left the booth and returned later with colleagues, asking us to demonstrate how they can use DataRobot to tune model parameters of libraries they didn’t have that much experience with. And, the citizen data scientists (or those who aspire to be one someday) came by in droves to see if DataRobot really is as easy to use as Yong made it look.
This was a successful event, and we’ve received some great feedback of our demo.
Automated machine learning or augmented data science: no matter what you call it, it was certainly the talk of the Gartner Data & Analytics Summit 2018.
About the Author:
Bob Laurent is a Sr. Director of Product Marketing at DataRobot. Prior to DataRobot, he ran product marketing at Alteryx, where he was responsible for driving awareness and growing a loyal customer base of empowered data analysts. He has more than 20 years of marketing, media relations, and telecom network engineering experience with Fujitsu and NYNEX (now Verizon). Bob resides in Dallas with his wife and two boys, and holds a Bachelor of Science degree from Clarkson University, plus an MBA from New York University’s Stern School of Business.