Decision Support Systems (DSS) have traditionally relied on relational data warehouses and ETL pipelines, which provide stable analytics over structured data but struggle with the rapid emergence of heterogeneous data lakes. Rebuilding DSS infrastructures to natively accommodate NoSQL sources is rarely feasible due to the critical role of legacy systems. This paper addresses the challenge of incrementally extending DSS with evolving NoSQL data while preserving analytical consistency, integrity, and compatibility with existing assets. To this end, we propose an MDE-based approach that leverages a unified model to abstract NoSQL structures into entities, attributes, and relationships. A set of transformation rules is introduced to generate evolution operations that update DSS models and keep them consistent. We demonstrate the applicability of our approach through a medical case study based on the MIMIC-III clinical database, extended with NoSQL sources from telemedicine, Internet of Medical Things, and rehabilitation data. The results show that our method enables seamless model evolution, preserving semantics and unlocking new analytical capabilities without disrupting the operational core.