Life Cycle of Data Analytics – Steps Explained

The term Data Analytics Life Cycle in Big Data and Data Science is frequently used when discussing learning and implementing Data Science and Big Data. This article will give an overview of the Data Analytic Lifecycle, explain why it's important, go through each phase in-depth, and then give an example of the Data Analytic Lifecycle.

What Is the Data Analytics lifecycle?

Data is crucial in today's environment, which is primarily digital. It goes through several steps during its production, testing, processing, consumption, and reuse. The Data Analytics Lifecycle maps out these stages for professionals working on data analytics initiatives. A circular arrangement of these stages creates the Data Analytics Lifecycle. Each phase has its own purpose and personality.

What Justifies the Need for a Data Analytics Lifecycle?

Significant big data projects should be employed with the data analytics lifecycle. The cycle is iterative and is utilized to represent the project itself accurately. In order to investigate the numerous needs for evaluating the information on big data, a step-by-step methodology is required to organize the actions and tasks involved in data collection, processing, analysis, and reuse. Modifying, processing, and cleaning raw data constitute data analysis. The goal is to produce important, valuable information that aids business decision-making.

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Lifecycle of Data Analytics – Importance

The Data Analytics Lifecycle outlines the process for creating, gathering, processing, using, and analyzing data to accomplish business objectives. It provides a systematic approach to managing data so that it can be transformed into information and used to achieve organizational and project objectives. The procedure provides the direction and methods required to glean knowledge from the data and advance toward achieving organizational goals.

Data Analytics Lifecycle – Steps

The phases in the life cycle of data analytics don't have a clearly defined structure. Hence these steps could not be consistent. Some data experts may follow additional procedures, while others might omit some steps entirely or simultaneously carry out many phases. Let's talk about the various data analytics life cycle stages.

The basic steps of each data analytics process are covered in this handbook. They are, therefore, more likely to be present throughout the life cycles of most data analytics projects. The six main stages of the data analytics lifecycle are

Step #1 Data Formation and Discovery

The purpose of the data must be determined during this step, along with a plan for achieving it. By mapping out the data, the stage involves establishing the key goals that a firm is attempting to ascertain. The team researches whether the business unit or organization has worked on comparable projects to draw on any lessons learned during this process and learn about the business domain.

Step #2 Processing and Preparation of Data

During this phase, the experts focus more on information requirements than business requirements. Making sure data is available for processing is one of the crucial elements of this step. The step comprises gathering, processing, and purifying the acquired data.

The team gets important data during the first step of this phase and moves on with the lifecycle of the company ecosystem. For this, a variety of data collection techniques are employed, including.

Entry of data: using digital technology or manual data input methods within the organization to get recent data

Data collection: collecting information from outside sources

Receipt of Signals: obtaining data from electronic devices, such as control systems and the Internet of Things.

Step #3 Create a Model

For the team to work with data and conduct analyses throughout the project, this phase requires the availability of an analytical sandbox. The group has numerous options for loading data.

Extract, Transform, Load (ETL) - Before loading the data into the sandbox, it changes the data by a set of business rules.

Extract, Load, and Transform (ELT) - This method loads the data into the sandbox and then transforms it following a set of business rules.

Extract, Transform, Load, Transform (ETLT) - This technique combines two transformation layers from ETL and ELT.

Step #4 Model construction

The team creates testing, training, and production datasets during this phase. Additionally, the team carefully constructs and implements models as anticipated throughout the model planning phase. They test the data and look for solutions to the predetermined goals. They test their models against the datasets using various statistical modeling techniques, including regression techniques, decision trees, random forest modeling, and neural networks.

Step #5 Publication and Communication of Results

This phase attempts to start working with major stakeholders and decide whether the project results are successful or unsuccessful. The team determines the most important conclusions of their investigation, evaluates the associated business value, and develops a condensed narrative to present the findings to the stakeholders.

Step #6 Evaluating the Results

The team gives a comprehensive report to the stakeholders during this last stage, together with coding, a briefing, key findings, and technical documents and papers. In addition, the data is transferred to a live environment and monitored to gauge the success of the analysis. The results and reports are complete if the findings are consistent with the goal. If they stray from the predetermined intent, on the other hand, the team goes backward in the lifecycle to an earlier phase to adjust the input and obtain a different result.

Example of the Data Analytics Lifecycle

Think about a chain of retail stores that wishes to increase sales by optimizing the prices of its products. The retail chain has thousands of products spread over hundreds of locations, making the situation extremely complex. After determining the goal of the chain of stores, you locate the required data, prepare it, and follow the Data Analytics lifecycle process.

Conclusion

To summarize, the Data Analytics lifecycle includes 6 key processes for creating, gathering, processing, using, and analyzing data. Understanding this lifecycle will help you gain an understanding of the overall process for your data science and analytics projects. So upgrade your skills with the best data science course from Learnbay, which also comes with 15+ real time projects sessions.