Generative AI is rapidly transforming industries, creating both new opportunities and heightened risks in data management. A significant challenge is the accumulation of redundant, obsolete, and trivial (ROT) data, which can account for up to 70 percent of enterprise data and offers little organizational value. This growing volume of ROT data not only undermines operational efficiency and compliance but also contributes to increased energy consumption and environmental impact, with cloud computing and storage now reportedly surpassing aviation in global greenhouse gas emissions.

As cloud infrastructure and generative AI adoption expand, so do the associated risks and regulatory pressures. Frameworks like the AI Act and GDPR require organizations to retain only accurate and necessary data, making ROT data a liability that can complicate legal processes and increase costs. Moreover, the quality of data used for AI directly affects the reliability of AI outputs. Addressing ROT data is essential for responsible AI integration, improved compliance, reduced environmental impact, and the long-term sustainability of digital resources. This whitepaper highlights practical strategies for ROT data management, emphasizing technology, policy, and automation to support effective data governance and AI reliability.

Learning points:

• What is ROT data
• How ROT impacts your organization’s environmental footprint
• How ROT poses a security threat to any (and all) organizations
• How ROT impacts your organization’s AI efficiency and accuracy
• What your organization can to do address ROT data