Data is and has always been, highly crucial in today's digital society. It passes through several stages during its life cycle, including development, testing, processing, consumption, and reuse. However, the data analytics lifecycle is a diagram that outlines these processes for project experts. Also, we can group it in a circular framework known as the Data Analytics Lifecycle. Each stage has its significance and unique characteristics. Thus, we will understand and examine each aspect of the data analytics lifecycle in this detailed article. In addition, you can enroll in Data Analytics Training for a more advanced approach to getting familiar with its concepts and life cycle.

Stepwise Data Analytics Life Cycle:

The Data Analytics lifecycle is for big data challenges and data science initiatives. In order to have an actual picture, the life cycle is progressive. Thus, to handle the different needs for executing Big Data analysis, a step-by-step approach is required to organize the activities and duties associated with data acquisition, processing, and repurposing.

● Data Discovery and Formation

To begin, there must be a defined goal. By the end of the data analytics lifecycle, you will have identified the purpose of your data and the techniques for achieving that goal.

● Preparation and Processing of Data

Everything having to do with data is brought together at this point. The second step prioritizes business requirements while giving equal emphasis to information requirements. Before moving on to the next phase, all gathered data must be collected, processed, and cleaned up.

● Designing a Model

After outlining your company objectives and collecting a multitude of data, structured, unstructured, or semi-structured, it's time to develop a model that will assist you in reaching your objectives. It is what we call data mining. However, collaborative work is necessary to establish and enhance methodological methods and procedures.

● Model Development

The final stage of the data analytics architecture process is the creation of data sets for testing, learning, and product components. However, the data analytics specialist methodically constructs and operates the model developed in the previous stage.

● Communication and Publication of Results

The communication phase starts with a collaborative effort with key stakeholders to determine if the project's outputs were a success or a failure. Thus, the project team helps to identify and quantify the results as part of its work.

● Effective Evaluation

As your data analytics lifecycle concludes, the next step is to provide a complete report to stakeholders. It includes significant results, code, briefings, technical papers or documents, and other supporting resources. Furthermore, the data is transported from the sandbox to a live setting in order to evaluate the efficiency.

Importance of Data Analytics Life Cycle:

The circular structure of the Data Analytics lifecycle guides data professionals to move ahead or backward using data analytics. According to the lifecycle model, professionals might abandon the entire inquiry and return to the first step to perform a complete analysis.

While professionals debate the data analytics lifecycle, there is no accepted structure for the above stages. However, it is quite unlikely that you would come across a physical data analytics architecture that is uniformly followed by all professional data analysts. Due to this uncertainty, there are times when you can add extra phases and skip basic processes. Also, you have the option of working on multiple cycles simultaneously or skipping a complete step.


Data analysts, business intelligence analysts, database administrators, data engineers, executive project sponsors, project managers, and data scientists can all be engaged in the planning, execution, and decision-making during these six phases. All these stakeholders are deeply involved in the project's effective planning and completion. By looking at these stages and their importance in the business organization, if you desire having a course in this domain, we suggest you enroll for the Data Analytics Online Training. Such training will help you gain proficiency and make you a competent expert in the data analytics industry.