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If you’re a leader tasked with generating business and org. value through ML/AI and analytics, you’ve probably struggled with low user adoption. Making the tech gets easier, but getting users to use, and buyers to buy, remains difficult—but you’ve heard a ”data product” approach can help. Can it? My name is Brian T. O’Neill, and on Experiencing Data—one of the top 2% of podcasts in the world—I offer you a consulting designer’s perspective on why creating ML and analytics outputs isn’t enough to create business and UX outcomes. How can UX design and product management help you create innovative ML/AI and analytical data products? What exactly are data products—and how can data product management help you increase user adoption of ML/analytics—so that stakeholders can finally see the business value of your data? Every 2 weeks, I answer these questions via solo episodes and interviews with innovative chief data officers, data product management leaders, and top UX professionals. Hashtag: #ExperiencingData. PODCAST HOMEPAGE: Get 1-page summaries, text transcripts, and join my Insights mailing list: 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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