Data versioning is a systematic approach to tracking and managing changes in datasets, data models, and schemas over time. It enables teams to monitor data modifications, maintain historical records, and ensure reproducibility in data-driven projects. Data versioning addresses critical challenges in managing evolving datasets, ensuring consistency, and enhancing team collaboration. Its key uses include reproducing experiments with identical data inputs and configurations, tracking changes in datasets and models to understand their impact on outcomes, facilitating collaboration by sharing standardized data and results across teams, providing an insurance policy against errors and accidental overwrites, and ensuring regulatory compliance. Organizations leveraging this practice experience enhanced collaboration, reproducibility, error mitigation, auditability, and compliance. However, its implementation comes with challenges such as complexity in setup, storage and cost implications, learning curve for teams, managing schema evolution, and ensuring data lineage visibility. To maximize the value of data versioning, organizations should select the right tools, integrate versioning early, automate processes, document changes thoroughly, train teams on versioning practices, and monitor and maintain data lineage. Data versioning is poised to evolve alongside technological advancements, driven by the rising reliance on data-driven processes across industries, with key trends including integration with AI/ML workflows, improved scalability, enhanced automation and governance, cloud-native integration, and empowering data versioning with tools like Acceldata.