On Line Analytical Processing (OLAP) is a technology basically created to provide users with tools in order to explore and navigate into data cubes. Unfortunately, in huge and sparse data, exploration becomes a tedious task and the simple user's intuition or experience does not lead to efficient results. In this paper, we propose to exploit the results of the Multiple Correspondence Analysis (MCA) in order to enhance data cube representations and make them more suitable for visualization and thus, easier to analyze. Our approach addresses the issues of organizing data in an interesting way and detects relevant facts. Our purpose is to help the interpretation of multidimensional data by efficient and simple visual effects. To validate our approach, we compute its efficiency by measuring the quality of resulting multidimensional data representations. In order to do so, we propose an homogeneity criterion to measure the visual relevance of data representations. This criterion is based on the concept of geometric neighborhood and similarity between cells. Experimental results on real data have shown the interest of using our approach on sparse data cubes.