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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
Wednesday Nov 21, 2018
Wednesday Nov 21, 2018
Kathy Koontz is the Executive Director of the Analytics Leadership Consortium at the International Institute for Analytics and my guest for today’s episode. The International Institute of Analytics is a research and advisory firm that discusses the latest trends and the best practices within the analytics field. We touch on how these strategies are used to build accurate and useful custom data products for businesses.
Kathy breaks down the steps of making analytics more accessible, especially since data products and analytics applications are more frequently being utilized by front-line workers and not PhDs and analytics experts. She uses her experience with a large property and casualty insurance company to illustrate her point about shifting your company’s approach to analytics to make it more accessible. Small adjustments to a data application make the process effective and comprehensible.
Kathy brings some great insights to today’s show about incorporating analytic techniques and user feedback to get the most value from your analytics and the data products you build for the information.
Conversation highlights:
- What is The International Institution of Analytics?
- What is the analytics leadership consortium?
- The “squishy” parts of analytics and how to compensate for them.
- The real value of analytics and how to use it on all levels of a company.
- How beta testers give perspective on data.
- The 3 steps to finding the ideal beta tester.
- Learning from the feedback and implementing it.
- How to keep ROI in mind during your project.
- Kathy’s parting advice for the audience.
Resources and Links:
Thank you for joining us for today’s episode of Experiencing Data. Keep coming back for more episodes with great conversations about the world of analytics and data.
Quotes from today’s episode:
“Oftentimes data scientists see the world through data and algorithms and predictions and they get enamored with the complexity of the model and the strength of its predictions and not so much with how easy it is for somebody to use it.” — Kathy Koontz
“You are not fully deployed until after you have received this [user] feedback and implemented the needed changes in the application.” — Kathy Koontz
“Analytics especially being deployed pervasively is maybe not a project but more of a transformation program.” — Kathy Koontz
“Go out and watch your user group that you want to utilize this data or this analytics to improve the performance.” — Kathy Koontz
“Obviously, it’s always cheaper to adjust things in pixels, and pencils than it is to adjust it in working code.” — Kathy Koo
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