What are the Challenges in Implementing Data-Driven Approaches?
Despite the advantages, several challenges exist:
Data Quality: Ensuring the accuracy and completeness of datasets. Integration: Combining data from different sources and formats. Interpretability: Making ML models understandable and actionable for researchers. Scalability: Managing and processing large datasets efficiently.
Overcoming these challenges requires robust data management practices, advanced computational tools, and interdisciplinary collaboration.
