Experiencing Data w/ Brian T. O’Neill  (UX for AI Data Products, SAAS Analytics, Data Product Management)

052 - Reasons Automated Decision Making with Machine Learning Can Fail with James Taylor


Listen Later

In this episode of Experiencing Data, I sat down with James Taylor, the CEO of Decision Management Solutions. This discussion centers around how enterprises build ML-driven software to make decisions faster, more precise, and more consistent-and why this pursuit may fail.

We covered:

  • The role that decision management plays in business, especially when making decisions quickly, reliably, consistently, transparently and at scale.
  • The concept of the "last mile," and why many companies fail to get their data products across it
  • James' take on operationalization of ML models, why Brian dislikes this term
  • Why James thinks it is important to distinguish between technology problems and organizational change problems when leveraging ML.
  • Why machine learning is not a substitute for hard work.
  • What happens when human-centered design is combined with decision management.
  • James's book, Digital Decisioning: How to Use Decision Management to Get Business Value from AI, which lays out a methodology for automating decision making.
  • Quotes from Today's Episode

    "If you're a large company, and you have a high volume transaction where it's not immediately obvious what you should do in response to that transaction, then you have to make a decision - quickly, at scale, reliably, consistently, transparently. We specialize in helping people build solutions to that problem." - James 

    "Machine learning is not a substitute for hard work, for thinking about the problem, understanding your business, or doing things. It's a way of adding value. It doesn't substitute for things." - James

    "One thing that I kind of have a distaste for in the data science space when we're talking about models and deploying models is thinking about 'operationalization' as something that's distinct from the technology-building process." - Brian

    "People tend to define an analytical solution, frankly, that will never work because[…] they're solving the wrong problem. Or they build a solution that in theory would work, but they can't get it across the last mile. Our experience is that you can't get it across the last mile if you don't begin by thinking about the last mile." - James 

    "When I look at a problem, I'm looking at how I use analytics to make that better. I come in as an analytics person." - James

    "We often joke that you have to work backwards. Instead of saying, 'here's my data, here's the analytics I can build from my data […], you have to say, 'what's a better decision look like? How do I make the decision today? What analytics will help me improve that decision?' How do I find the data I need to build those analytics?' Because those are the ones that will actually change my business." - James 

    "We talk about [the last mile] a lot ... which is ensuring that when the human beings come in and touch, use, and interface with the systems and interfaces that you've created, that this isthe make or break point-where technology goes to succeed or die." - Brian

    Links
    • Decision Management Solutions
  • Digital Decisioning: How to Use Decision Management to Get Business Value from AI
  • James' Personal Blog
  • Connect with James on Twitter
  • Connect with James on LinkedIn
  •  

    ...more
    View all episodesView all episodes
    Download on the App Store

    Experiencing Data w/ Brian T. O’Neill  (UX for AI Data Products, SAAS Analytics, Data Product Management)By Brian T. O’Neill from Designing for Analytics

    • 5
    • 5
    • 5
    • 5
    • 5

    5

    39 ratings


    More shows like Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)

    View all
    Software Engineering Radio - the podcast for professional software developers by se-radio@computer.org

    Software Engineering Radio - the podcast for professional software developers

    272 Listeners

    HBR IdeaCast by Harvard Business Review

    HBR IdeaCast

    1,830 Listeners

    a16z Podcast by Andreessen Horowitz

    a16z Podcast

    1,033 Listeners

    Data Skeptic by Kyle Polich

    Data Skeptic

    480 Listeners

    UI Breakfast: UI/UX Design and Product Strategy by Jane Portman

    UI Breakfast: UI/UX Design and Product Strategy

    137 Listeners

    Acquired by Ben Gilbert and David Rosenthal

    Acquired

    3,987 Listeners

    Odd Lots by Bloomberg

    Odd Lots

    1,784 Listeners

    The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) by Sam Charrington

    The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

    441 Listeners

    Super Data Science: ML & AI Podcast with Jon Krohn by Jon Krohn

    Super Data Science: ML & AI Podcast with Jon Krohn

    298 Listeners

    Data Engineering Podcast by Tobias Macey

    Data Engineering Podcast

    140 Listeners

    Masters of Scale by WaitWhat

    Masters of Scale

    3,995 Listeners

    DataFramed by DataCamp

    DataFramed

    267 Listeners

    Practical AI by Practical AI LLC

    Practical AI

    192 Listeners

    Machine Learning Street Talk (MLST) by Machine Learning Street Talk (MLST)

    Machine Learning Street Talk (MLST)

    88 Listeners

    Product Thinking by Melissa Perri

    Product Thinking

    144 Listeners