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