Organizations recognize data as a valuable asset, making accurate and reliable data collection a strategic priority, especially with 72% of global organizations using generative AI tools to enhance their decisions. However, accessing quality data is challenging due to its complexity and volume, which can lead to biases, inaccuracies, and irrelevant information causing 85% of AI projects to fail. To optimize the ML model development lifecycle, improving data collection is crucial. Data collection is the foundation of any data-driven process, ensuring that organizations gather accurate and relevant datasets for building AI algorithms. Effective data collection strategies are essential for maintaining training data quality and reliability, particularly as more businesses rely on AI and analytics. The process involves defining objectives, identifying data sources, choosing collection methods, preprocessing data, annotating data, storing data, documenting metadata, and monitoring data quality. Despite the best practices, challenges remain, including data accessibility, privacy concerns, bias in large datasets, and resource constraints. Encord is an end-to-end AI-based multimodal data curation platform that can help mitigate these challenges by providing robust data curation, labeling, and validation features.