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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 Sep 24, 2019
Tuesday Sep 24, 2019
Scott Friesen’s transformation into a data analytics professional wasn’t exactly linear. After graduating with a biology degree and becoming a pre-med student, he switched gears and managed artists in the music industry. After that, he worked at Best Buy, eventually becoming their Senior Director of Analytics for the company’s consumer insights unit. Today, Scott is the SVP of Strategic Analytics at Echo Global Logistics, a provider of technology-enabled transportation and supply chain management services. He also advises for the International Institute for Analytics.
In this episode, Scott shares what he thinks data scientists and analytics leaders need to do to become a trustworthy and indispensable part of an organization. Scott and I both believe that designing good decision support applications and creating useful data science solutions involve a lot more than technical knowledge. We cover:
- Scott’s trust equation, why it’s critical for analytics professionals, and how he uses it to push transformation across the organization
- Scott’s “jazz” vs “classical” approach to creating solutions
- How to develop intimacy and trust with your business partners (e.g., IT) and executives, and the non-technical skills analytics teams need to develop to be successful
- Scott’s opinion about design thinking and analytics solutions
- How to talk about risk to business stakeholders when deploying data science solutions
- How the success of Scott’s new pricing model was impeded by something that had nothing to do with the data—and how he addressed it
- Scott’s take on the emerging “analytics translator” role
- The two key steps to career success—and volcanos
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Quotes from Today's Episode
“You might think it is more like classical music, but truly great analytics are more like jazz. ” — Scott
“If I'm going to introduce change to an organization, then I'm going to introduce perceived risk. And so the way for me to drive positive change—the way for me to drive adding value to the organizations that I'm a part of—is the ability to create enough credibility and intimacy that I can get away with introducing change that benefits the organization.” — Scott
“I categorize the analytic pursuit into three fundamental activities: The first is to observe, the second is to relate, and the third is to predict. ” — Scott
“It's not enough to just understand the technology part and how to create great models. You can get all that stuff right and still fail in the last mile to deliver value.” — Brian
“I tend to think of this is terms of what you called ‘intimacy.’ I don’t know if you equate that to empathy, which is really understanding the thing you are talking about from the perspective of the other person. When we do UX research, the questions themselves are what form this intimacy. An easy way to do that is by asking open-ended questions that require open-ended answers to get that person to open up to you. ” — Brian
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