How to Win Hearts and Minds as a Data Leader

Joe Kleinhenz talks about his journey from starting out in data all the way to becoming a leader in one of the largest insurance organizations in the United States. We'll learn about the importance of staying on top of technology, how to win hearts and minds of nontechnical folks, centralized versus decentralized team, pros and cons, how to hold effective conversations with stakeholders and how to go from individual contributor to leader.Joe Kleinhenz: The critical skills you bring to the table is the ability to break down complex ideas into ones that translate for nontechnical folks.Ginette Methot: I'm Ginette.Curtis Seare: And I'm Curtis.Ginette: And you are listening to Data Crunch—a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Data Crunch is produced by the Data Crunch Corporation and analytics training and consulting company. One of the biggest challenges companies have in getting value from their data is finding the right talent. Good talent is scarce and building a top-tier team is hard if not impossible for some companies. If you are having this challenge try out our analytics as a service offering: we bring a fully equipped data science team to bear on your projects, on demand and with no long-term contract constraints. If you want to start seeing success for your data science efforts quickly and economically, head over to datacrunchcorp.com for more details.Today we'll be hearing about Joe Kleinhenz's journey from starting out in data all the way to becoming a leader in one of the largest insurance organizations in the United States. We'll learn about the importance of staying on top of technology, how to win hearts and minds of nontechnical folks, centralized versus decentralized team, pros and cons, how to hold effective conversations with stakeholders and how to go from individual contributor to leader. There's lots of unpack in this episode, so let's get to it.Curtis: If we could just start out just by talking about what got you interested in data in the first place, where your journey started, and we can go from there.Joe: I actually first started thinking about using math to predict future outcomes when I was a teenager. I read a book by Asimov called Foundation and whole premise of the book series was I'm using mathematics to predict the future. It's all science fiction stuff in it that point, but that's kind of what certainly got me first interested in it.Curtis: So it was a, it was a work of fiction that got you interested.Joe: Yeah, that captured my imagination. I didn't even at that point even know, it was a, you know, data science was a thing, and as I got my path into the technology, within IT, I was doing business consulting for awhile and got into data warehousing, and this was in the late nineties. From there, ended up in part of GE financial that was doing a lot of direct marketing, and they had a group called database marketing, which was essentially the precursors for data scientists. They had predictive modelers, statisticians essentially in there that were, by today's standards, relatively simplistic tools like linear regression to build, you know, models predicting who would respond to direct-drip marketing offers. I used to joke with people that I ran a team of bad people that decided to call you at dinner with an offer. You can just have the here. Um,Curtis: And you made those people very effective at, at being bad, I assume.Joe: Yes. Yes. At that point there was very few restrictions on what you could do. We were even using credit data for some of the, the algorithms cause we were with credit card companies. Credit data at the time, there wasn't the regulatory restrictions there is now, it's incredibly predictive. When you combine that with recency frequency data on purchasing behavior, you'd really kind of tune in on, you know, what someone would be interested in.