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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 Jan 15, 2019
Tuesday Jan 15, 2019
Vinay Seth Mohta is Managing Director at Manifold, an artificial intelligence engineering services firm with offices in Boston and Silicon Valley. Vinay has helped develop Manifold’s Lean AI process to build useful and accurate machine learning apps for a wide variety of customers.
During today’s episode, Vinay and I discuss common misconceptions about machine learning. Some of the other topics we cover are:
- The 3 buckets of machine learning problems and applications.
- Differences between traditional product development and developing apps with machine learning from Vinay’s perspective.
- Vinay’s opinion of what will change as a result of growth in the machine learning industry
- Maintaining a vision of a product while building it
Resources and Links:
- CRISP-DM
- Ways to Think About Machine Learning by Benedict Evans
- The Lean AI process
- Vinay Seth Mohta on LinkedIn
- Big Data, Big Dupe: A little book about a big bunch of nonsense by Stephen Few
Quotes from Vinay on today’s episode:
“We want to try and get them to dial back a little bit on the enthusiasm and the pixie dust aspect of AI and really, start thinking about it, more like a tool, or set of tools, or set of ideas that enable them with some new capabilities.”
“We have a process we called Lean AI and what we’ve incorporated into that is this idea of a feedback loop between a business understanding, a data understanding, then doing some engineering – so this is the data engineering, and then doing some modeling and then putting something in front of users.”
“Usually, team members who have domain knowledge [also] have pretty good intuition of what the data should show. And that is a good way to normalize everybody’s expectations.”
“You can really bring in some of the intuition that [clients] already have around their data and bring that into the conversation and that becomes an almost shared decision about what to do [with the data].”
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