The Data Centric Analysis Process focuses on deriving value by treating data as a primary asset throughout the decision-making lifecycle. It begins with understanding the business context and identifying key questions or objectives, followed by discovering, collecting, and assessing the quality and relevance of available data. Analysts then apply techniques such as descriptive statistics, visualization, and exploratory data analysis (EDA) to uncover patterns, trends, and anomalies. The process emphasizes semantic consistency, data governance, and metadata management to ensure data is trustworthy, interoperable, and reusable across domains. Insights generated are interpreted in context and communicated effectively to stakeholders to inform strategy, operations, or further analytical modelling.