Data Analysis Advantages And Disadvantages

In general, Data Analysis is used to check the validation of the vaguely collected data in terms of variations and profitability. However, if Data Analysis is not used we cannot obtain the accurate, factual and optimum solution of the given challenge. Data Analysis tools helps us to understand the question and produces answer in a detailed space. It also helps to find the gaps between data such as missing values, unidentified variations, inconsistency of data and many more. However, the main objective of data analysis is to detect the problem. In addition to this, Data Analysis is widely used in detection of duplicity, sampling (purposive, stratified, cluster, simple random), fraud detection and forensic audits, ANCOVA, payrolls, time series,

For analysis, advanced statistical tools are used and the experimenter can draw necessary conclusions and inferences. In business sectors, they have a huge amount of scattered data in terms of profits, loss, demand, supply, sales and production. The industrial, insurance, agricultural, banking, information technology, food industry, telecommunication, retail, utilities, travel, pharmacy and many more have challenges to manage their data. As the coin always has two sides, there are both advantages and a few disadvantages of data analysis. The key advantages of data analysis are- The organizations can immediately come across errors, the service provided after optimizing the system using data analysis reduces the chances of failure, saves time and leads to advancement. It is also used to compare strategies between two companies so as to reduce the prices and gaining attention of target customers, ultimately leading to maximization of profit and minimization of cost(as done in Game Theory). However, big data analysis sometimes becomes more tedious and disadvantageous because it uses software Hadoop which requires special provisions in the computers. For now use of Hadoop for real-time analysis is not available. The manner in which the data is collected and the decision making view can vary from one person to another. Here, the quality of data gets affected and leaves the data insufficient or inefficient. In order to tackle this problem, the researcher must be well experienced and should have deep knowledge about the characteristic under study. Also, we need to update data from time to time so as to avoid the changes in trend caused by the past data especially, for the rapidly growing