Product Manager As A Career Choice
- Amruta Bhaskar
- Aug 27, 2020
- 0 comentario (s)
- 1776 Puntos de vista
Data product managers haven’t been around for very long, but they are a special kind of managers. We still hear and use “product manager” more often, but the role has evolved over the years. Because of the massive changes in the tech and business landscapes, product managers deal with more information than they used to. Sometimes people say “product manager” when they talk about “data product manager” without really knowing the difference. So what’s the distinction? Data.
What is a product manager?
The product manager is the product expert. Everything from ideation and research to design and performance analysis is all part of the job. Product management means being a jack of all trades and managing every stage of the product life cycle. That means any and all details about the product are the product manager’s responsibility. Additionally, product managers must also be able to communicate the product internally and to customers. And if all goes well, you’ll have a successful product launch.
What makes a data product manager different?
Think of it this way: All data product managers are product managers, but not all product managers are data product managers. Both roles require market research and testing viability, developing strategies, and product development, almost everything a general product manager does. But the data product manager does something the general product manager does not. The key difference is the use of data.
From the beginning, data is the foundation, product, and the goal. A data product manager delves deeper into big data, using algorithms and statistics to produce a final product and then analyze the data produced by the product. The position combines many roles but doesn’t favour any single one. Programming, designing, engineering, and other skills are all encompassed into this manager role. It’s this combination that produces an expert manager who can handle every stage of development.
Data related skills like algorithms, machine learning, and UI/UX are skills not used with simple products but are a must for data products. This is what makes the data product manager such a desirable and necessary employee.
Usually, a Data Science team is composed of senior people, it is very common to have Masters or PhDs in the composition. These professionals tend to be lone wolves, they like to do everything on their own and have strong, inflexible opinions. The PM here needs to manage it acting as a strong facilitator. Always keeping the team environment healthy during the ceremonies, allowing everyone to talk and helping to conduct discussions to avoid endless rounds of argument, sometimes it takes an external Decider to close a discussion loop— it’s important to keep track of time in conversations or they will extend a lot. Being able to discuss with the same vocabulary as these professionals become very important here, reinforcing the need for technical data knowledge.
The life cycle of data products is different, development processes must also be adapted. The first distinction occurs in the modelling phase, where a solution must be proposed to the problem. Here, the team needs to choose some statistical (or ML) model to solve the problem, but this process involves a lot of research and experimentation, which makes the time indefinite. This step makes it more difficult to use some agile methodologies. It is important for the PM to give space for scientists to work autonomously, but to avoid losing too many time and over-engineering I recommend setting checkpoint deadlines. It is possible to use an adapted Scrum with Sprints for example, and at the end of each Sprint the partial modelling results are presented and re-discussed or re-prioritized.