Product Management Tips For Data Science Projects
November 21, 2019
Data science has traditionally been an analysis-only endeavor: using historical statistics, user interaction trends, or AI machine learning to predict the impact of deterministically coded software changes. For instance, “how do we think this change to the onboarding workflow will shift user behavior?” This is data science (DS) as an offline toolkit to make smarter decisions.
Increasing, though, companies are building statistical or AI/Machine Learning features directly into their products. This can make our applications less deterministic – we may not know exactly how applications behave over time, or in specific situations – and harder to explain. It also requires product managers to engage much more directly with data scientists about models, predictability, how products work in production, how/why users interact with our products, and how our end users measure success. (Hint: most users don’t understand or care about F1-scores; they just want to get the right answer.)
- Provide Much Deeper Context than Traditional Software Projects, Especially Use Cases and Business Goals
- Remember That Data Science Projects are Uncertain and Our Judgment May Be Weak
- Choosing/Accessing Data Sets is Crucial
- Describe How Accurate This Application Needs to Be, and Anticipate Handling “Wrong” Answers
- “Done” Means Operationalized, Not Just Having Insights
Data-driven applications are more complicated than deterministic software products. And working with data scientists has some unique challenges. We need to approach these thoughtfully, recognize the patterns, and respect the special talents of each group.
On demand recording
Meet our panelists
Rich Mironov
Rich Mironov is a 35-year veteran of Silicon Valley tech companies. He coaches product executives, parachutes into companies as the interim VP of Product Management, and was the ‘product guy’ or CEO at six start-ups. Rich’s long-running blog (www.mironov.com) covers software, start-ups, product strategies, and the inner life of product managers. He is the author of “The Art of Product Management” (2008) and organized the first Product Camps.
Mike Watson VP Engineering at Synerzip
Mike Watson, VP Engineering at Synerzip, is a veteran engineering leader with over 15 years of experience leading software teams. Mike’s passion is in helping software product development organizations transition into strong Agile practices and cultures within. He has experience working with large and medium public companies (such as Motorola and Tangoe), as well as mid-to-late stage startups (such as 4thpass, Solbright and Quintessent). Mike has been fortunate to work with a few of the leading minds in modern agile process development.
Mike’s technical specialties include Kanban/Scrum, highly scalable enterprise software, SaS/Cloud compliance, Application Security, System Integration and Mobile Commerce.
