Ideation. Align on the key problem to solve, and the potential data inputs to consider for the solution.
Data preparation. Collect and get the data in a useful format for a model to digest and learn from.
Prototyping and testing. Build a model or set of models to solve the problem, test how well they perform and iterate until you have a model that gives satisfactory results.
Productization. Stabilize and scale your model as well as your data collection and processing to produce useful outputs in your production environment.

How would you measure the model’s quality?

Understanding the key factors may require problem business space knowledge, which is one of the reasons it’s important for business / product people to be heavily involved at this stage.

Non-scalable methods such as a quick manual download, writing a rudimentary scraper or buying a sample of data even if a little expensive may be the most practical approach.

Once the data is in good shape, the data science team can start working on the actual model. Keep in mind that there’s a lot of art in the science at this stage.

Create a mechanism that refreshes the data over time — either updates existing values or adds new information

Check for outliers. While the model as a whole may scale very well, there may be small but important populations that the model doesn’t work well for.

It is very useful to set up an on-demand way to outsource such tasks as they come up