The term Data analysis it is a process in which data sets are analyzed and inspected to gather insights. Conclusions are drawn from the information collected. Many techniques and technologies such as data cleansing, transformation, and modeling are used to make desirable business decisions. Data cleansing involves replacing inaccurate or damaged data. These corrupted data are modified or deleted using different techniques. While in the transformation process the data is transformed from one format to another. Subsequently, the data model is created using the detailed data activity model. This approach is applied in a variety of fields such as science, business, research, and technology.

why analysis: Basically, data analysis is a qualitative and quantitative technique used to improve business productivity that can be used for Business to Consumer (B2C) applications. In many of the large organizations, data is collected from different parties, such as the customer, the business, and the economy. After data collection, it is analyzed and then used as per the requirements. It has become a basic necessity nowadays for better business prospects. This type of Business Intelligence (BI) leads to better performance of organizations and profitable businesses. Therefore, we can say that data analysis is an important aspect of gathering useful information and trading insights. It is directed towards the best economic growth of business in many companies. Therefore, most of the organizations are using this approach.

How data analysis helps in business growth: In this digital age, organizations have terabytes and petabytes of data in different forms that need to be stored and managed. Traditional systems cannot manage big data so new techniques like Hadoop and much more are used to manage and store big data. Organizations make accurate decisions based on this stored big data. For this, the Big Data Analysis technique was developed. It allows to know the important information that is useful in making commercial decisions by companies. Help in the following aspects:

1. It allows organizations to know how good or bad their performance is.
2. Analysis of customer demand, behavior and requirements lead to effective marketing.
3. In the elaboration of competitive strategies for the business environment from the Data Analysis of the different organizations.
4. It belongs to the customer’s point of view so that new innovations can be realized.
5. Due to the different choices of people, the recommendation of different products is subjected to profitable business.
6. The right knowledge will reduce business risk.

Data analysis at the service of organizations: Many organizations are using data analytics techniques to examine their historical data to meet customer needs and satisfaction. For example, Netflix uses data analytics to verify the logs of its users, who are recommended movies or TV shows based on their similar choices based on their past activities. Facebook recommends us new friends, which is possible with the help of Data Analysis. Likewise, the recommended videos according to the choice of each user are the result of the Data Analysis. Because of this, users easily get what they need, which improves business performance.

Data analysis in different domains: It is at the service of the educational, technological and business sector where all digital innovation is improvised. It’s helping marketers and industry leaders make profitable decisions. Therefore, it will suffice to say that it is an industry necessity. In industries, this technique is used to convert raw data into meaningful information for decision making. After the analysis, the result becomes precise and accurate, so smarter solutions are developed for better customer satisfaction. This technique has led organizations to better business performance.

This article shows that Data Analysis has its own importance. Making better business decisions, from the customer’s point of view, all of these decisions help make business improvements that lead to organizational growth. Tableau Public, OpenRefine, Google search operators are some tools used to perform data analysis. The programming languages ​​that are at the top for decision making are Python, R, SQL. These are used as part of the data science workflow.