The term Data analysis It is a process in which data sets are analyzed and inspected to gather information. 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 corrupted data. These corrupted data are modified or removed 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 the data collection, it is analyzed and then used according to 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 collecting useful information and business 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 terabyte and petabytes of data in different forms that must 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 big stored data. For this, the Big Data Analysis technique was developed. It allows knowing the important information that is useful in making business decisions by companies. Help in the following aspects:
- It allows organizations to know how good or bad their performance is.
- Analysis of customer demand, behavior and requirements lead to effective marketing.
- In the elaboration of competitive strategies for the business environment from the Data Analysis of the different organizations.
- It belongs to the customer’s point of view so that new innovations can be made.
- Due to the different choices of people, the recommendation of different products undergoes profitable business.
- The right insights will reduce business risk.
Data analysis at the service of organizations: Many organizations are using data analysis techniques to examine their historical data to meet customer needs and satisfaction. For example, Netflix uses data analytics to verify the records 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 Data Analysis. Due to 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. You are helping marketers and industry leaders make profitable decisions. Therefore, it will be enough to say that it is a necessity of the industry. In industries, this technique is used to convert raw data into meaningful information for decision making. After the analysis, the result becomes precise and exact, thus 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. Make better business decisions, from the customer’s point of view, all these decisions help to make improvements in business that lead to growth of organizations. 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.