The Emerging Directions in Data for Multimodal Foundation Models (DataMFM) workshop focuses on advancing the data foundations of multimodal AI systems. While text data has benefited from decades of large-scale curation and standardization, multimodal data—spanning images, documents, video, audio, and cross-modal pairings—continues to face fundamental challenges in quality, diversity, coverage, and reproducibility. This workshop examines emerging research directions that move beyond brute-force web-scale data collection, including the transition from web-scale to world-scale data, agentic and self-generated data pipelines, and principled data selection and mixture design. Core topics include data contamination and provenance, benchmark construction and renewal, multimodal alignment across heterogeneous modalities, and evaluation practices that remain robust as models and datasets evolve. By bringing together researchers and practitioners from academia, industry, and open-data initiatives, DataMFM aims to to define the foundation of next-generation multimodal data ecosystems.