The first and most important risk of any data science project is the goal definition. The correct goal definition plays a major part in the success formula. It is often tempting to jump into the implementation stage of the project right after you have the task definition, regardless of whether it is vague or unclear. By doing this, you risk solving the task in an entirely different way from what the business actually needs. It is important that you define a concrete and measurable goal that will give your team a tool that they can use to distinguish between right and wrong solutions.
To make sure that the project goal is defined correctly, you may use the following checklist:
- You have a quantifiable business metric that can be calculated from the input data and the algorithm's output.
- The business understands the most important...
