The term Data Analysis is a process in which data sets are analyzed and inspected to collect information. From the collected information conclusions are drawn. A lot of techniques and technologies are used such as cleaning, transforming and modeling of data to take desirable business decisions. Cleaning of data involves replacement of inaccurate or corrupted data. This corrupted data is modified or removed using different techniques. While in transforming process data is transformed from one format to another. Afterwards, data model is created using activity model of detailed data. This approach is applied in a variety of domains such as science, business, research, and technology.
Why Analysis: Basically Data Analysis is a qualitative and quantitative technique used for enhancing business productivity which can be used for Business to Consumer (B2C) applications. In many of big organizations, data is collected from different portions such as customer, business, and economy. After collection of data, it is analyzed and then utilized as per requirement. It has become a basic need today for better business prospects. This kind of Business Intelligence (BI) leads to better performance of organizations and profitable business. Thus we can say that analysis of data is an important aspect of collecting useful information and business insights. It heads towards the better economic growth of business in many firms. Thus most of the organizations are using this approach.
How Analysis of Data helps in Business Growth: In this digital era organizations have a terabyte and petabytes of data in different forms which needs to be stored and managed. Traditional systems are not able to manage big data, so new techniques such as Hadoop and much more used for managing and storing big data. Organisations make accurate decisions based on these stored big data. For this purpose, Big Data Analysis technique was evolved. It insights the important information which is useful in making business decisions by companies. It helps in following aspects:
It lets organizations know that how better or poor their performance is.
Analysation of customer demand, behavior and requirement lead to effective marketing.
In making competitive strategies for the business environment from Data Analysis of the different organization.
Pertains customer’s viewpoint so that new innovations can be done.
Due to different choices of people, different products recommendation undergo profitable business.
Proper insights will reduce the risk of the business.
Data Analysis serving organizations: Many organizations are using Data Analysis techniques to examine their historical data to meet customer’s need and satisfaction. For example, Netflix uses Data Analysis to check the records of their users, who are recommended movies or TV shows according to their similar choices based on their previous activities. Facebook recommends us new friends which is possible with the help of Data Analysis. Also, videos recommended according to each user’s choice is a result of Data Analysis. Because of this users are easily getting what is required by them which improves company’s performance.
Data Analysis in different Domains: It is serving in the education sector, technology and business which improvise whole digital innovation. It is helping marketers and industry leaders for making profitable decisions. Thus it will be sufficient to say that it is an industry need. In industries, this technique is used to convert raw data into meaningful information for decision making. After Analysis outcome becomes precise and accurate, thus smarter solutions are developed for better customer satisfaction. This technique has taken organizations to a better business performance.
This article shows that Analysis of Data has its own importance. Making business decisions better, customer viewpoint, all these decisions help to make improvements in business which lead to the growth of organizations. Tableau Public, OpenRefine, Google search operators are some tools used for making the analysis of data. Programming languages which are in the top for decision making are Python, R, SQL. These are used as a part of data science workflow.