Sparse data refers to datasets where a significant number of elements are zero or missing, posing challenges for analysis and modeling due to the lack of information. Techniques like dimensionality reduction, imputation, and specialized algorithms are often employed to effectively handle and extract meaningful patterns from sparse data.