034 – ML & UX: To Augment or Automate? Plus, Rating Overall Analytics Efficacy with Eric Siegel, Ph.D.
Eric Siegel, Ph.D. is founder of the Predictive Analytics World and Deep Learning World conference series, executive editor of “The Predictive Analytics Times,” and author of “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.” A former Columbia University professor and host of the Dr. Data Show web series, Siegel is a renowned speaker and educator who has been commissioned for more than 100 keynote addresses across multiple industries. Eric is best known for making the “how” and “why” of predictive analytics (aka machine learning) understandable and captivating to his audiences.
In our chat, we covered:
- The value of defining business outcomes and end user’s needs prior to starting the technical work of predictive modeling, algorithms, or software design.
- The idea of data prototypes being used before engaging in data science to determine where models could potentially fail—saving time while improving your odds of success.
- The first and most important step of Eric’s five-step analytics deployment plan
- Getting multiple people aligned and coordinated about pragmatic considerations and practical constraints surrounding ML project deployment.
- The score (1-10) Eric gave the data community on its ability to turn data into value
- The difference between decision support and decision automation and what the Central Intelligence Agency’s CDAO thinks about these two methods for using machine learning.
- Understanding how human decisions are informed by quantitative predictions from predictive modes, and what’s required to deliver information in a way that aligns with their needs.
- How Eric likes to bring agility to machine learning by deploying and scaling models incrementally to mitigate risk
- Where the analytics field currently stands in its overall ability to generate value in the last mile.
“The greatest pitfall that hinders analytics is not to properly plan for its deployment.” — Brian, quoting Eric
“You don’t jump to number crunching. You start [by asking], ‘Hey, how is this thing going to actually improve business?’ “ — Eric
“You can do some preliminary number crunching, but don’t greenlight, trigger, and go ahead with the whole machine learning project until you’ve planned accordingly, and iterated. It’s a collaborative effort to design, target, define scope, and ultimately greenlight and execute on a full-scale machine learning project.” — Eric
“If you’re listening to this interview, it’s your responsibility.” — Eric, commenting on whose job it is to define the business objective of a project.
“Yeah, so in terms of if 10 were the highest potential [score], in the sort of ideal world where it was really being used to its fullest potential, I don’t know, I guess I would give us a score of [listen to find out!]. Is that what Tom [Davenport] gave!?” — Eric, when asked to rate the analytics community on its ability to deliver value with data
“We really need to get past our outputs, and the things that we make, the artifacts and those types of software, whatever it may be, and really try to focus on the downstream outcome, which is sometimes harder to manage, or measure … but ultimately, that’s where the value is created.” — Brian
“Whatever the deployment is, whatever the change from the current