This year, the message of ”Gartner’s North American analytics conference 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. 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!
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.
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.