Introduction

In today’s digital world, data is the critical element. Data travels through several phases or stages as it develops, consumes, tests, processes, and is reused. Such steps are mapped out for data science professionals by a data analytics infrastructure. It is a cyclic phase that encompasses the data life cycle, with each stage having its own significance and characteristics.

The circular shape lifecycle encourages data professionals to move forward or backward with data analytics. Based on this information, professionals can surrender the complete research and return to the first step of the data analytics life cycle to redo the entire analysis. Therefore, to know more about this phase, you can stick to this article till the conclusion. Alternatively, you can refer to the Data Analytics Training in Noida, which will help you to gain knowledge from scratch to an advanced level and assist in starting your professional journey.

Different Stages of Data Analytics

A scientific method for providing a structured framework for the data analytics life cycle is into six stages. The framework is simple and cyclical. This means that all the steps in the data analytics life cycle must be in proper order. These steps are as follows:

Stage 1: Data Discovery & Formation

Everything starts with a goal in mind. In this phase, you will define the purpose of your data and how to achieve it by the end of the data analytics lifecycle. Here, the goal is to conduct evaluations and assessments to develop a hypothesis for resolving business problems and challenges.

The first stage also outlines the potential use and requirement of data, such as where the information is coming from, what story you want your data to convey, and how your organization will benefit from the incoming data.

Stage 2: Data Preparation and Processing

This stage includes everything that has to do with data. During this phase, experts transition from business requirements to information requirements. However, the data preparation and processing step entail gathering, processing, and cleansing the collected data. One of the most critical aspects of this phase is to ensure that the data is available for processing.

Stage 3: Design a Model

After sketching out your company goals and gathering a plethora of data (structured, unstructured, or semi-structured), it’s time to construct a model that uses the data to achieve the goal. That’s the reason model planning is the name given to this stage of the data analytics process. This step also incorporates cooperation to identify the approaches, strategies, and methodology for building the model in the next phase. However, this process starts with finding the relationship between data points to choose the essential variables and, subsequently, create a suitable model.

Stage 4: Model Building

This stage involves creating data sets for testing, training, and production. However, the data analytics professionals develop and run the model they have developed in the previous stage with utmost care. They also develop and execute the model using tools and techniques such as decision trees, regression approaches, and neural networks. Moreover, the experts put the model through a trial run to see if it matches the datasets.

It assists them in determining if the tools they presently have will be sufficient to implement the model or if a more robust system is required.

Stage 5: Result in Communication and Publication

Do you remember the goal you defined for your company in step 1? Now it’s time to see if the tests you ran in the previous step matched those requirements. The communication phase begins with cooperation with key stakeholders to decide if the project’s outcomes are successful or unsuccessful. However, the project team must determine the study results, calculate the business value associated with the conclusion, and write a narrative to summarize and communicate the outcomes to stakeholders.

Stage 6: Effectiveness Evaluation

As your data analytics lifecycle ends here, the final stage is to offer stakeholders a complete report that includes results, code, briefings, and technical papers/documents.

Furthermore, to assess the report’s success, the data is transported from the sandbox to a live environment and watched to see if the results match the desired aim. If the findings meet the objectives, the reports, and outcomes end here.

Conclusion

Hopefully, you may find this article informative. We have compiled the different stages involved in the data analytics lifecycle. This lifecycle provides an organized approach to dealing with data, which in turn helps with accurate business decisions. As a result, to understand this technology and have an in-depth proficiency, we suggest you enroll in the Data Analytics Online Training.