My Product Manager Survival Handbook

Developing software products is a messy process. It’s messy because it’s so flexible and fairly new, only about 60 years old. It’s also abstract and not limited by the physical world, only by our creativity. 

If you’re building with more concrete mediums, whether it’s furniture or a cake, there are well-documented rules about how parts work together and how it should look. In this sense, software engineers more closely resemble artists such as writers or musicians where rules are flexible and the result isn’t how it looks, but how it makes us feel. Great technology delights - think of the raw excitement the first time Steve Jobs presented the iPhone in 2007. Or the sheer joy of finishing tedious paperwork in minutes instead of hours because a computer automated it.

But, building delightful products is a process littered with worries. Work experience can help because it closes the gap between the execution required and final product vision. Unfortunately, experience is a nice way of saying learning from mistakes and there are always new ways to make mistakes. Experience isn't always enough. 

So, what are my worries? 

The One Skill That Data Scientists Are Not Being Trained On

After attending the Toronto Machine Learning Micro-Summit this past week, one theme came up repeatedly during the presentations -  communicate with the business team early, and often, or you'll need to go back and re-do your work. 

There was the story of an insurance company that created a model that recommended whether to replace or fix a car after a damage claim. It sounded great - the Data Scientists got a prototype up and running and had business team buy-in. But, the problem was that their models weren't very accurate. Usually when that happens it means that your data is noisy or the algorithm isn't powerful enough. They went back to their business team and it turns out that they missed 2 key features: the age of the vehicle and if it's a luxury model. 

Another example was a telecom that built a model to optimize call center efficiency. The data science team spent a month building the model and everyone was excited to get it in production. Then, they were told that the call center runs on an outdated application. It turns out that integrating with the application would cost more than the ROI of the project.