Data analytics is primarily conducted in business-to-consumer (B2C) applications. Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Global organizations collect and analyze data associated with customers, business processes, market economics or practical experience. Data is categorized, stored and analyzed to study purchasing trends and patterns.Evolving data facilitates thorough decision-making.
Data analysis is the process of examining, transforming, and arranging raw data in a specific way to generate useful information from it. In essence, data analysis allows for the evaluation of data through analytical and logical reasoning to lead to some sort of outcome or conclusion in some context. It is a multi-faceted process that involves a number of steps, approaches, and diverse techniques.
Data Analytics Process
The very first step consists of business understanding. Whenever any requirement occurs, firstly we need to determine business objective, assess the situation, determine data mining goals and then produce the project plan as per the requirement. Business objectives are defined in this phase.
Second step consists of Data understanding. For further process, we need to gather initial data, describe and explore the data and verify data quality to ensure it contains the data we require. Data collected from the various sources is described in terms of its application and need for the project in this phase. This is also known as data exploration. This is necessary to verify the quality of data collected.
Next come Data preparation. From the data collected in last step, we need to select data as per the need, clean it, construct it to get useful information and then integrate it all. Finally we need to format the data to get appropriate data. Data is selected, cleaned, and integrated in the format finalized for the analysis in this phase.
Once data is gathered, we need to do data modeling. For this, we need to select modeling technique, generate test design, build model and assess the model built. Data model is build to analyze relationships between various selected objects in the data, test cases are built for assessing the model and model is tested and implemented on the data in this phase.
Next come data evaluation where we evaluate the results generated in last step, review the scope of error and determine next steps that need to be performed. Results of the test cases are evaluated and reviewed for the scope of error in this phase.
Final step in analytic process is deployment. Here we need to plan the deployment and monitoring and maintenance, we need to produce final report and review the project. Results of the analysis are deployed in this phase. This is also known as reviewing of the project.