Data overload is a significant problem for business leaders in today's information age, with 90% of data being unstructured, making it challenging to analyze and derive insights from collected data. Robust AI applications require high-quality data to deliver accurate results, but the inability to analyze data hinders developers from implementing the right AI solutions. Data versioning is a key element of effective ML and data science workflows, ensuring data remains organized, accessible, and reliable throughout the project lifecycle. Implementing data versioning requires expertise in data engineering, data modeling, and involvement from multiple stakeholders, but it can address challenges such as storage limitations, data management complexity, security, and collaboration issues. Organizations can overcome these challenges by using different versioning approaches, including data duplication, metadata, full data version control, and automating the versioning process. A best practice for effective data versioning is to define the scope and granularity of versioning, track data repositories, commit changes regularly, integrate versioning with experiment tracking systems, use branching and merging techniques, automate the versioning process, define data disposal policies, and ensure data privacy. Encord is a robust data management solution that enables efficient versioning and curation of large datasets for scalable ML models, providing features such as natural language search, annotation, security, and integrations with cloud storage platforms.