In today's world, there is a growing need to analyze data stored in a Data Lake, which is a collection of large, heterogeneous databases. Our work is part of a medical application that aims to help healthcare professionals analyze complex data for decision-making. We propose mechanisms that promote data accessibility. The data are stored in a Data Warehouse (DW) that is periodically built from a data lake. Depending on the needs of the decision-maker, data are extracted from the DW and transferred to a Data Mart (DM) for querying. In this paper, we present a schema recommendation system based on the principle of collaborative filtering. This system can predict the DM schemas that were developed in the past that best match the data need expressed by a decision-maker. It does this by comparing the attributes present in the schemas with the attributes deduced from the need to propose a list of predictions for the most suitable schemas. The technique used is simple, while allowing us to solve the problem of periodic updates to the source data. An experiment was conducted for a medical application.