As Asset Management (AM) organizations struggle to harness the full potential value of their assetswith the available contemporary digital tools they are looking for new technologies to assist in the AM process.Digital Twins (DTs) is a novel concept capable of providing AM organizations with access to real-time dataregarding asset performance and conditions, reliable communication channels, and records of historical realestate data during Operation and Maintenance (OM). DTs also integrates well with Artificial Intelligence (AI)applications, facilitating data-driven predictive analytics. However, the potential benefits of DT implementation are predicated on data trust, integrity, and security. By introducing Blockchain Technology (BCT) data canbe stored in decentralized databases avoiding a single point of trust. Hence, this study aims to identify the keydata categories and characteristics defined by Asset Information Requirements (AIR) and how this affects thedevelopment and maintenance of an Asset Information Model (AIM). A mixed-method approach was used togather empirical data through semi-structured interviews and a digital questionnaire. The findings include adefinition of three key data categories, Core Data, Static OM Data, and Dynamic OM Data, and the data characteristics required to perform data-driven predictive analytics through AI. This is followed by a discussionregarding the process of defining, delivering, and maintaining the AIM and the potential use of BCT as a facilitator for data trust, integrity, and security. The findings contribute to the body of research by inspiring novelresearch and extensive adoption of DT-based AM.