Jana Eggers, a self-proclaimed math and computer nerd, is CEO of Nara Logics, a company that helps organizations use AI to eliminate data silos and unlock the full value of their data, delivering predictive personalized experiences to their customers along the way. The company leverages the latest neuroscience research to model data the same way our brains do. Jana also serves on Fannie Mae’s digital advisory board, which is tasked with finding affordable housing solutions across the United States. Prior to joining Nara Logics, Jana wore many different hats, serving as CEO of Spreadshirt, and General Manager of QuickBase at Intuit, among other positions. She also knows about good restaurants in PDX! In today’s episode, Jana and I explore her approaches to using AI to help enterprises make interesting and useful predictions that drive better business outcomes and improve customer experience. In addition to discussing how AI can help strengthen personalization and support smarter decision making, we also covered:

The power of showing the whys when providing predictions (i.e., explainable AI or XAI).

Jana’s thoughts on why some data scientists struggle with inflated expectations around AI

Brian’s #facepalm about lipstick and data

The power of what-if simulations and being able to remove factors from predictions

The power of context and how Nara Logics weighs recent data vs. long-term historical data in its predictions

How Nara Logics leverages the wiring of the brain—the connectome—to inspire the models they build and the decision support help they provide to customers

Why AI initiatives need to consider the “AI trinity”: data, the algorithm, and the results an organization is aiming for

Resources and Links:

Nara Logics

Follow Jana on Twitter

Connect with Jana on LinkedIn

Quotes from Today’s Episode

“We have a platform that is really built for decision support. How do you go from having […]20 to having about 500 to 2,000 decision factors coming in? Once we get that overload of information, our tool is used to help people with those decisions. And yes, we're using a different approach than the traditional neural net, which is what deep learning is based on. While we use that in our tool, we're more on the cognitive side. [...]I've got a lot of different signals coming in, how do I understand how those signals relate to each other and then make decisions based on that?” — Jana

“One of the things that we do that also stands us apart is that our AI is transparent—meaning that when we provide an answer, we also give the reasons why that is the right answer for this context. We think it is important to know what was taken into account and what factors weigh more heavily in this context than other contexts.” — Jana

“It is extremely unusual—and I can even say that I've never really seen it—that people just say, Okay, I trust the machine. I’m comfortable with that. It knows more than me. That's really unusual. The only time I've seen that is when you're really doing something new and no one there has any idea what it should be.” — Jana

“With regards to tech answering “why,” I’ve worked on several monitoring and analytics applications in the IT space. When doing root cause analysis, we came up with this idea of referring to monitored objects as being abnormally critical and n