The text emphasizes the importance of maintaining a healthy skepticism toward data, recognizing that data can be misleading, contain errors, or reflect biases, and encourages individuals to become comfortable enough with data to identify when something seems off. It advises against overreacting to fluctuations in data, suggesting that such changes might be due to errors in the data pipeline, misinterpretation, or simply unexpected results. The example of Net Promoter Score (NPS) is used to illustrate how metrics can be volatile due to factors like sample size changes or survey modifications, recommending the use of rolling averages and visualizations to better understand these metrics. Developing "data intuition" involves practice, understanding the ebb and flow of data, and leveraging the expertise of data scientists and analysts to gain a deeper understanding of data sources and how they are modeled. The text encourages ongoing engagement with qualitative research and collaboration with data teams to build confidence in interpreting data.