In the first stage of the data science lifecycle, the focus is on problem identification and data collection. This stage is crucial as it lays the foundation for the entire data science process.

Problem Identification: The first step is to clearly define the problem or question that you want to answer. For example, let's say you want to analyze customer churn in a subscription-based business. The problem statement could be: 'Identify factors that contribute to customer churn and develop strategies to reduce it.'

Data Collection: Once the problem is well-defined, the next step is to gather relevant data. Depending on the problem at hand, data can be collected from various sources such as databases, APIs, surveys, or even web scraping. For our customer churn example, you may collect data on customer demographics, subscription details, transaction history, and customer feedback through surveys.

Data Documentation: It is important to clearly document the data sources, collection methods, and any potential biases or limitations in the dataset. This documentation will help ensure the reproducibility of the analysis and provide transparency.

By properly identifying the problem and collecting relevant data, you set the stage for the subsequent stages of the data science lifecycle.