The text discusses the challenges large organizations face in accessing, organizing, and storing data due to various constraints and complexities. It identifies 14 types of data that, while offering significant opportunities, are challenging to manage because of their inherent properties, corruption from human interaction, or the manner in which they change. Examples include clickstream data, IoT data, healthcare data protected by HIPAA, and financial records governed by the Gramm–Leach–Bliley Act. Additionally, human factors such as data silos in SaaS platforms, inconsistencies from customer inputs, and cross-organizational politics further complicate data management. The text also highlights difficulties arising from the need for data cleaning, algorithmic processing, and maintaining reproducibility in data science operations. The author emphasizes that understanding these challenges is key to effectively organizing and utilizing data to drive business decisions.