In modern data science, automation primarily emphasizes workflow orchestration. This involves automating the entire data science pipeline, from initial data preparation and feature engineering to model training, evaluation, and deployment. By focusing on workflow orchestration, automation streamlines and coordinates the various stages of a data science project, enhancing efficiency and reproducibility. It also fosters collaboration among team members, speeds up model development, and boosts overall productivity in data science endeavors.