A common problem with OnLine Analytical Processing (OLAP) databases is data explosion - data size multiplies, when it is loaded from the source data into multidimensional cubes. Data explosion is not an issue for small databases, but can be serious problems with large databases. In this paper we discuss the sparsity and data explosion phenomenon in multidimensional data model, which lie at the core of OLAP systems. Our researches over five companies with different branch of business confirm the observations that in reality most of the cubes are extremely sparse. We also consider a different method that relational and multidimensional severs applies to reduce the data explosion and sparsity problems as compression and indexes techniques, partitioning, preliminary aggregations.