Data is crucial in today’s digital world. As it gets created, consumed, tested, processed, and reused, data goes through several phases/ stages during its entire life. A data analytics architecture maps out such steps for data analytics professionals. It is a cyclic structure that encompasses all the data life cycle phases, where each stage has its significance and characteristics.
The lifecycle’s circular form guides data professionals to proceed with data analytics in one direction, either forward or backward. Based on the newly received information, professionals can scrap the entire research and move back to the initial step to redo the complete analysis as per the lifecycle diagram.
However, while there are talks of the data analytics lifecycle among the experts, there is still no defined structure of the mentioned stages. You’re unlikely to find a concrete data analytics architecture that is uniformly followed by every data analysis expert. Such ambiguity gives rise to the probability of adding extra phases (when necessary) and removing the basic steps. There is also the possibility of working for different stages at once or skipping a phase entirely.
Yet, suppose, there is ever a discussion about the stages of the data lifecycle. In that case, the below-listed phases are likely to be present, as they represent the fundamentals of almost every data analysis process. upGrad follows these basic steps to determine a data professional’s overall work and the data analysis results.
A scientific method that helps give the data analysis process a structured framework is divided into six phases of data analytics architecture.
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