Data Products - Accumulation of Imperfect Actions Towards a Focused Goal - DRT S2E15
403:17 Brian's career as musician, designer, and product consultant
10:48 What is the process to uncover desirability, feasibility, and other product requirements
17:59 Importance of defining success quantitatively as early as possible
20:06 How do you deal with interviewing end users, if access to them is complicated
27:27 What does lo-fi prototype mean for algorithms? ("so what" exercise)
32:28 How do you deal with stakeholders who are married to a solution instead of focusing on problem solving?
36:17 Data centric ML, and interpretation of data explorations, and ML results
45:09 Human centric data science, data assumptions, context, and provenance
46:29 Wrap up and verdicts
KEY TAKEAWAYS
1. The process of creating a robust user experience starts by considering all stakeholders and understanding the end-user's needs
2. Regardless of your organizational role, you can demonstrate product leadership by asking the right questions to surface unspoken needs.
3. Product development has to be focused on driving value and outcome, rather than just delivering outputs. No one needs a technically right, and effective wrong solution.
4. Product development process has to be lean and focused on regular build-measure-learn cycles that maximize traction (how many new user showed concrete intent to use the product vs how many interviews we did)
5. Cross-functional teams need to own the problem space and work collaboratively to find the best solution, including considering whether or not machine learning is necessary for the desired outcome.
6. The "So What" exercise (aka "abstraction laddering") is a valuable tool in figuring out the fundamental reason you are solving a problem, as it helps you think about the overall system design based on users' workflow, and how an algorithm can improve that process.
7. It is important to tackle the riskiest assumptions first using experiment artifacts like low fidelity mockups, clickable prototypes, and eventually live data "steel threads".
8. The operationalization of the model must be included in the design of the solution and is not a separate step. The end-to-end experience is what makes the solution a complete data product, and not just a project.
9. When designing decision support tools, eg. based on advanced analytics, focusing on a UX that conveys trust is very important. Frameworks like CED (Conclusions, Evidence, Data) are good options to consider for that purpose