Data Analytics is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. The results so obtained are communicated, suggesting conclusions           and supporting decision-making. Data visualization is at times used to portray the data for the ease of discovering the useful patterns in the data. The terms Data Modeling and Data Analysis mean the same.
Data has become a lot more accessible and it can be used by everyone within a company to increase productivity and enhance decision-making. It’s no surprise that data analytics has become an important tool     across organizations. By bringing together data from across the business, companies can get real-time insights into finance, sales, marketing, product development, and other processes.
These insights enable teams within a company to collaborate better, achieve better results, and outperform the competition. Data analytics enables employees to view data in context and make smarter business     decisions to achieve improved products and services.
1.Data Requirements Specification The data required for analysis is based on a question or an experiment. Based on the requirements of those directing the analysis.
2.Data Collection Data Collection is the process of gathering information on targeted variables identified as data requirements. The emphasis is on ensuring accurate and honest collection of data. Data                 Collection ensures that data gathered is accurate such that the related decisions are valid. Data Collection provides both a baseline to measure and a target to improve.
3.Data Processing The data that is collected must be processed or organized for analysis. This includes structuring the data as required for the relevant Analysis Tools.
4.Data Cleaning The processed and organized data may be incomplete, contain duplicates, or contain errors. Data Cleaning is the process of preventing and correcting these errors.
5.Data Analysis Data that is processed, organized and cleaned would be ready for the analysis. Various data analysis techniques are available to understand, interpret, and derive conclusions based on the                 requirements. Data Visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data.
6.Communication The results of the data analysis are to be reported in a format as required by the users to support their decisions and further action. The feedback from the users might result in additional             analysis.