057 - How to Design Successful Enterprise Data Products When You Have Multiple User Types to Satisfy

Designing a data product from the ground up is a daunting task, and it is complicated further when you have several different user types who all have different expectations for the service. Whether an application offers a wealth of traditional historical analytics or leverages predictive capabilities using machine learning, for example, you may find that different users have different expectations. As a leader, you may be forced to make choices about how and what data you’ll present, and how you will allow these different user types to interact with it. These choices can be difficult when domain knowledge, time availability, job responsibility, and a need for control vary greatly across these personas. So what should you do? To answer that, today I’m going solo on Experiencing Data to highlight some strategies I think about when designing multi-user enterprise data products so that in the end, something truly innovative, useful, and valuable emerges. In total, I covered: Why UX research is imperative and the types of research I think are important (4:43) The importance for teams to have a single understanding of how a product’s success will be measured before it is built and launched (and how research helps clarify this). (8:28) The pros and cons of using the design tool called “personas” to help guide design decision making for multiple different user types. (19:44) The idea of ‘Minimum valuable product’ and how you balance this with multiple user types (24:26) The strategy I use to reduce complexity and find opportunities to solve multiple users’ needs with a single solution (29:26) The relevancy of declaratory vs. exploratory analytics and why this is relevant. (32:48) My take on offering customization as a means to satisfy multiple customer types. (35:15) Expectations leaders should have-particularly if you do not have trained product designers or UX professionals on your team. (43:56) Resources and Links My training seminar, Designing Human-Centered Data Products: http://designingforanalytics.com/theseminar Designing for Analytics Self-Assessment Guide: http://designingforanalytics.com/guide (Book) The User Is Always Right: A Practical Guide to Creating and Using Personas for the Web by Steve Mulder https://www.amazon.com/User-Always-Right-Practical-Creating/dp/0321434536 My C-E-D Design Framework for Integrating Advanced Analytics into Decision Support Software: https://designingforanalytics.com/resources/c-e-d-ux-framework-for-advanced-analytics/ Homepage for all of my free resources on designing innovative machine learning and analytics solutions: designingforanalytics.com/resources

Om Podcasten

Are you an enterprise data or product leader seeking to increase the user adoption and business value of your ML/AI and analytical data products? While it is easier than ever to create ML and analytics from a technology perspective, do you find that getting users to use, buyers to buy, and stakeholders to make informed decisions with data remains challenging? If you lead an enterprise data team, have you heard that a ”data product” approach can help—but you’re not sure what that means, or whether software product management and UX design principles can really change consumption of ML and analytics? 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 product designer’s perspective on why simply creating ML models and analytics dashboards aren’t sufficient to routinely produce outcomes for your users, customers, and stakeholders. My goal is to help you design more useful, usable, and delightful data products by better understanding your users, customers, and business sponsor’s needs. After all, you can’t produce business value with data if the humans in the loop can’t or won’t use your solutions. Every 2 weeks, I release solo episodes and interviews with chief data officers, data product management leaders, and top UX design and research professionals working at the intersection of ML/AI, analytics, design and product—and now, I’m inviting you to join the #ExperiencingData listenership. Transcripts, 1-page summaries and quotes available at: https://designingforanalytics.com/ed ABOUT THE HOST Brian T. O’Neill is the Founder and Principal of Designing for Analytics, an independent consultancy helping technology leaders turn their data into valuable data products. He is also the founder of The Data Product Leadership Community. For over 25 years, he has worked with companies including DellEMC, Tripadvisor, Fidelity, NetApp, Roche, Abbvie, and several SAAS startups. He has spoken internationally, giving talks at O’Reilly Strata, Enterprise Data World, the International Institute for Analytics Symposium, Predictive Analytics World, and Boston College. Brian also hosts the highly-rated podcast Experiencing Data, advises students in MIT’s Sandbox Innovation Fund and has been published by O’Reilly Media. He is also a professional percussionist who has backed up artists like The Who and Donna Summer, and he’s graced the stages of Carnegie Hall and The Kennedy Center. Subscribe to Brian’s Insights mailing list at https://designingforanalytics.com/list.