This post may look somewhat off-topic but it's not. And I'll explain why.
There is a growing trend for a new class of product managers. These are data product managers or machine learning product managers who not only understand the usual product management practices but also how these products can be made more powerful by using data.
And there is a good chance that data product management will become its own discipline over a period of time if it's not already.
So I wrote this post to particularly focus on some of the aspects of a data project manager's role, the kind of opportunities and challenges they face and also some common industry-standard frameworks and tools for implementing machine learning models and data models in production that can be useful for a data product manager.
The primary difference between a software product manager and data product manager is that as a software product manager you have much more control over your features.
But a data product manager has to depend on the data, models and accuracy for an answer to 3 important questions:
- How long will it take to build the product?
- How much investment will be needed?
- What will be the end performance of the product?
In most cases there is a great uncertainty regarding all these 3 aspects for data or AI based products.
The major challenge of a data product manager is to make sure that there is the right data and adequate data available to develop the algorithms/ build the models that developers are working on.
Additional responsibilities of data product managers is to make sure:
Collection of data
Security of data
Variety of data
Accuracy of data