The usefulness of time-series data or cross-sectional data in data science depends largely on the objective of the research. For example – analysing effectiveness of marketing campaigns do not require time-series data as it is a one-time affair. However, considering both descriptive as well as predictive analytics on historical data trends & patterns; time series data is extremely important.
A perfect example of the same is in any business wherein a customer journey is tracked and analysed and the probability of a prospect getting converted to customer is devised. Also, its heavily used in Financial Applications such as predicting the Future Stock prices based on the historical data.