073 - Addressing the Functional and Emotional Needs of Users When Designing Data Products with Param Venkataraman

Episode Description Simply put, data products help users make better decisions and solve problems with information. But how effective can data products be if designers don’t take the time to explore the complete needs of users? To Param Venkataraman, Chief Design Officer at Fractal Analytics, having an understanding of the “human dimension” of a problem is crucial to creating data solutions that create impact. On this episode of Experiencing Data, Param and I talk more about his concept of ‘attractive non-conscious design,’ the core skills of a professional designer, and why Fractal has a c-suite design officer and is making large investments in UX.    In our chat, we covered: Param's role as Chief Design Officer at Fractal Analytics, and the company's sharp focus on the 'human dimension' of enterprise data products. (2:04) 'Attractive non-conscious design': Creating easy-to-use, 'delightful' data products that help end-users make better decisions by focusing on their needs. (5:32) The importance of understanding the 'emotional need' of users when designing enterprise data products. (9:07) Why designers as well as data science and analytics teams should focus more on the emotional and human element when building data products. (16:15) 'The next version of design': Why and how Param believes the classic design thinking model must adapt to the 'post-data science world.' (21:39) The core competencies of a professional designer and how it relates to data products. (25:59) Why non-designers should learn the principles of good design — and how Fractal’s internal Phi Design System helps frame problems from the perspective of a data product's end-user, leading to better solutions. (27:51) Why Param believes the coming together of design and data still needs time to mature. (33:40) Quotes from Today’s Episode “When you look at analytics and the AI space … there is so much that is about how do you use ... machine learning … [or] any other analytics technology or solutions — and how do you make better effective decisions? That’s at the heart of it, which is how do we make better decisions?” - Param Venkataraman (@onwardparam) (6:23)    “[When it comes to business software,] most of it should be invisible; you shouldn’t really notice it. And if you’re starting to notice it, you’re probably drawing attention to the wrong thing because you’re taking people out of flow.” - Brian O’Neill (@rhythmspice) (8:57)   “Design is kind of messy … there’s sort of a process ... but it’s not always linear, and we don’t always start at step zero. … You might come into something that’s halfway done and the first thing we do is run a usability study on a competitor’s thing, or on what we have now, and then we go back to step two, and then we go to five. It’s not serial, and it’s kind of messy, and that’s normal.” - Brian O’Neill (@rhythmspice) (16:18)   “Just like design is iterative, data science also is very iterative. There’s the idea of hypothesis, and there’s an idea of building and experimenting, and then you sort of learn and your algorithm learns, and then you get better and better at it.” - Param Venkataraman (@onwardparam) (18:05)   “The world of data science is not used to thinking in terms of emotion, experience, and the so-called softer aspects of things, which in my opinion, is not actually the softer; it’s actually the hardest part. It’s harder to dimensionalize emotion, experience, and behavior, which is … extremely complex, extremely layered, [and] extremely unpredictable. … I think the more we can bring those two worlds together, the world of evidence, the world of data, the world of quantitative information with the qualitative, emotional, and experiential, I think that’s where the magic is.” - Param Venkataraman (@onwardparam) (21:02)   “I think the coming together of design and data is... a new thing. It’s unprecedented. It’s a bit like how the internet was a new thing back in the mid ’90s. We were all astounded by it, we di