Data sparsity refers to the challenge of dealing with datasets where the majority of the elements are zero or missing, which can complicate data analysis and model training. This issue is prevalent in fields like recommender systems and natural language processing, where it can lead to difficulties in extracting meaningful patterns and making accurate predictions.