Data Analytics Lifecycle Phases
Objective Discovery – Phase 1
Understanding the company’s behavior or the team is one of the critical factors in starting the Data Analytics process. Phase 1 is all about studying the business domain, history, capabilities, team strength, and so on. A detailed study is conducted on resources, technology, and past data, depending on the project’s objective.
Some of the outputs of Phase 1 include finding a business problem and figuring out a broad idea in terms of implementation. Hence, a comprehensive observation of the organization’s behavior and assets is essential to effectively frame and meet the objective.
Preparation of the data – Phase 2
Preparation and planning play a vital role while working on a large project from time to time. Every data analytics team engages in preparing the data based on the phase 1 result from the team. Most of the data analytics believe in working with an analytic sandbox concept. Sandbox concept is an integral part of data preparation because It helps accurately segregate the data.
It is recommended to follow either ELT (extract, load, and transform) or ETL (extract, transform, and load) method to place data in the Sandbox. The data in the Sandbox is generally organized that helps in working with the data effectively. The data is prepared and organized well because it helps in planning the model of the activity.