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Is the value of your enterprise analytics SAAS or AI product not obvious through it’s UI/UX? Got the data and ML models right...but user adoption of your dashboards and UI isn’t what you hoped it would be? While it is easier than ever to create AI and analytics solutions from a technology perspective, do you find as a founder or product leader that getting users to use and buyers to buy seems harder than it should be? If you lead an internal enterprise data team, have you heard that a ”data product” approach can help—but you’re concerned it’s all hype? My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I share the stories of leaders who are leveraging product and UX design to make SAAS analytics, AI applications, and internal data products indispensable to their customers. After all, you can’t create business value with data if the humans in the loop can’t or won’t use your solutions. Every 2 weeks, I release interviews with experts and impressive people I’ve met who are doing interesting work at the intersection of enterprise software product management, UX design, AI and analytics—work that you need to hear about and from whom I hope you can borrow strategies. I also occasionally record solo episodes on applying UI/UX design strategies to data products—so you and your team can unlock financial value by making your users’ and customers’ lives better. Hashtag: #ExperiencingData. JOIN MY INSIGHTS LIST FOR 1-PAGE EPISODE SUMMARIES, TRANSCRIPTS, AND FREE UX STRATEGY TIPS https://designingforanalytics.com/ed ABOUT THE HOST, BRIAN T. O’NEILL: https://designingforanalytics.com/bio/
Episodes
Tuesday Dec 03, 2019
Tuesday Dec 03, 2019
Angela Bassa is the director of data science and head of data science and machine learning at iRobot, a technology company focused on robotics (you might have clean floors thanks to a Roomba). Prior to joining iRobot, Angela wore several different hats, including working as a financial analyst at Morgan Stanley, the senior manager of big data analytics and platform engineering at EnerNOC, and even a scuba instructor in the U.S. Virgin Islands.
Join Angela and I as we discuss the role data science plays in robotics and explore:
- Why Angela doesn’t believe in a division between technical and non-technical skill
- Why Angela came to iRobot and her mission
- What data breadcrumbs are and what you should know about them
- The skill Angela believes matters most when turning data science into a producer of decision support
- Why the last mile of the UX is often way longer than one mile
- The critical role expectation management plays in data science, how Angela handles delivering surprise findings to the business, and the marketing skill she taps to help her build trust
Resources and Links
Designing for Analytics Seminar
Quotes from Today’s Episode
“Because these tools that we use sometimes can be quite sophisticated, it’s really easy to use very complicated jargon to impart credibility onto results that perhaps aren’t merited. I like to call that math-washing the result.” — Angela
“Our mandate is to make sure that we are making the best decisions—that we are informing strategy rather than just believing certain bits of institutional knowledge or anecdotes or trends. We can actually sort of demonstrate and test those hypotheses with the data that is available to us. And so we can make much better informed decisions and, hopefully, less risky ones.” — Angela
“Data alone isn’t the ground truth. Data isn’t the thing that we should be reacting to. Data are artifacts. They’re breadcrumbs that help us reconstruct what might have happened.” — Angela
[When getting somebody to trust the data science work], I don’t think the trust comes from bringing someone along during the actual timeline. I think it has more to do with bringing someone along with the narrative.—Angela
“It sounds like you’ve created a nice dependency for your data science team. You’re seen as a strategic partner as opposed to being off in the corner doing cryptic work that people can’t understand.” — Brian
“When I talk to data scientists and leaders, they often talk about how technical skills are very easy to measure. You can see them on paper, you can get them in the interview. But there are these other skills that are required to do effective work and create value.” — Brian
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