127.8K
Downloads
161
Episodes
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 Oct 06, 2020
Tuesday Oct 06, 2020
Join the Free Webinar Related to this Episode
I'm taking questions and going into depth about how to address the challenges in this episode of Experiencing Data on Oct 9, 2020. 30 Mins + Q/A time. Replay will also be available.
Welcome back for another solo episode of Experiencing Data. Today, I am primarily focusing on addressing the non-digital natives out there who are trying to use AI/ML in innovative ways, whether through custom software applications and data products, or as a means to add new forms of predictive intelligence to existing digital experiences.
Many non-digital native companies today tend to approach software as a technical “thing” that needs to get built, and neglect to consider the humans who will actually use it — resulting in a lack of business or organizational value emerging. While my focus will be on the design and user experience aspects that tend to impede adoption and the realization of business value, I will also talk about some organizational blockers related to how intelligent software is created that can also derail a successful digital transformation efforts.
These aren’t the only 10 non-technical reasons an intelligent application or decision support solution might fail, but they are 10 that you can and should be addressing—now—if the success of your technology is dependent on the humans in the loop actually adopting your software, and changing their current behavior.
Links
- Want to address these issues? Learn about my Self-Guided Video Course and Instructor-Led Seminar
- Subscribe to my Free DFA Insights Mailing List: https://designingforanalytics.com/mailing-list/
Comments (0)
To leave or reply to comments, please download free Podbean or
No Comments
To leave or reply to comments,
please download free Podbean App.