Finally, the data PM knows that data, models, and outputs aren’t enough — they still have to be product managers and bring these components back to the business model and their organization’s strategy. Machine learning models that don’t align with the business model will not only waste time and money for no reason, they undermine an organization’s trust in machine learning. This is especially true in companies that are late to data science, have skepticism about the power of data science, or are very qualitative in their leadership.
The data PM can translate requirements between data scientists, engineers, designers, marketers, and other PMs. They build product instrumentation and data storage into their acceptance criteria while collaborating with data scientists to ensure that data will be accessible and usable for analysis and modeling as soon as possible. They don’t leave it to engineers who are not data scientists to make assumptions about what kinds of data will be valuable for data scientists.
The data PM understands that machine learning is useful for a lot of problems, but knows when a heuristic model may be more appropriate. When it’s not clear, they timebox exploration to see which approach may be more useful. They also know that there may come a time when a switch from heuristic model to machine learning model will be appropriate and plan ahead for this contingency.