University data is classified either as structured (in the form of numbers, figures, statistics, trends, etc.) or unstructured (in the form of minutes of meetings, written content, voice, visual media, etc.), which manifests as information, knowledge, and wisdom about a university. This book highlights and discusses the issues surrounding representation and misrepresentation of university data, especially as it pertains to the reflection of truth or implication of truth of a university. From the eyes of a practitioner, this book highlights the challenges and real-life consequences facing the university, along with actionable solutions that can be practised by everyone within the institution. Specifically, this book will address ways to advance the institution within the rapid development of artificial intelligence, machine learning, big data, blockchain, and personalised learning. It is written with the intention to address key areas of the UiTM2025 agenda to become a Globally Renowned University by 2025.