Data teams often struggle to communicate effectively with business leaders who request complex projects based on buzzwords they’ve encountered. Executives might come back from conferences or read reports and demand advanced solutions like AI-driven tools or data architectures without fully understanding what those terms entail. This disconnect leads to unrealistic expectations, misaligned project goals, and needless urgency to address issues that might not even exist. The core issue lies in the vocabulary surrounding data. Terms like “real-time,” “self-service,” or “data quality” are frequently used without a clear understanding of their implications. Business professionals might assume they know what these concepts involve, but without proper definitions, they often lead to confusion and miscommunication. The result is a cycle where data teams spend time clarifying these terms rather than focusing on solving actual business problems. The author aims to clarify key data buzzwords and their real meanings to help bridge this communication gap. By doing so, they hope to align the expectations of business leaders with the capabilities of data teams, ultimately leading to more effective projects that meet genuine needs rather than just following trends. This approach promotes better understanding and sets the stage for more successful collaborations between data professionals and business stakeholders.