In data science, the first step towards solving a problem is to clearly define it. Problem definition involves identifying and formulating a clear problem statement, setting goals, and defining success metrics. By doing so, we establish a roadmap for the entire data science lifecycle. Let's consider an example to understand this better.

Suppose we have a mobile app that suggests restaurants to users based on their preferences. The problem statement could be: 'Develop a recommendation system that suggests restaurants to users based on their location, cuisine preferences, and previous ratings.' In this case, the goal would be to improve user satisfaction and engagement.

After defining the problem, the next step is to collect relevant data. We need data that can help address the defined problem. Various methods and sources can be used for data collection, such as web scraping, API integration, surveys, or existing datasets. For our restaurant recommendation app example, we may collect data such as restaurant details, user ratings, user preferences, and location data.

Once we have collected the necessary data, we can proceed with the next stages of the data science lifecycle, which involve preparing the data, building models, and deploying them for production use.