Data plays an important role in the present-day setting, which is predominantly digital. During its production, testing, processing, consumption, and reuse, it goes through several steps. These stages are mapped out by the Data Analytics Lifecycle for specialists working on data analytics initiatives. A circular arrangement of these phases creates the Data Analytics Lifecycle. Each phase has a distinct purpose and personality. Significant big data projects should be utilized with the phases of data analytics. The cycle is continuous, and it is utilized to precisely express the project itself. To investigate the numerous needs for evaluating the information on big data, a methodical approach is required to organize the actions and tasks involved in gathering information, processing it, analysis, and reuse. Modifying, processing, and cleaning raw data comprise data analysis. The goal is to produce substantial, valuable information that aids in business decision-making. The phases of Data Analytics and Lifecycle outline the process for creating, gathering, processing, using, and evaluating data to accomplish the goals of the company. It offers a methodical approach to managing data so that it can be transformed into information and used to achieve organizational and project objectives. The process delivers the guidance and techniques needed to extract information from the data and move further toward reaching corporate objectives. Data professionals use the cyclic structure of the phases to go ahead or backward with data analytics. Individuals can decide whether to carry on with their current research or abandon it and conduct a fresh analysis in light of the recently acquired insights. The way they proceed is guided by the Data Analytics lifecycle.