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Essay on necessity of data analytics

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Big data can be understood as a collection of data sets that are very large and complex which is difficult to be processed using on-hand database management tools or traditional data processing applications. The data sets that are included here are with sizes beyond the ability of commonly used software tools for capturing , curating, managing, and processing the data within a tolerable elapsed time. The big data target is important due to constant improvement in traditional DBMS technology and new databases like NoSQL and their ability to handle larger amounts of data. Big data is a huge volume and huge variety of information that require new forms of processing which helps in making decisions and helps in optimization of processes. Text detection and localization in big data images is necessary for content-based image analysis. This problem is difficult due to the complications in the background, the non-uniform brightness of the image, the varying text font,and their sizes.
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On the other hand, Big data can be challenges such as the importance of privacy and security, and talent of organization to find the full value of Big data. Body According to the research by MGI and McKinsey's Business Technology Office, big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus. Dominic and David mentioned Bigger and better data give companies both more-panoramic and more-granular views of their business environment. Database Applications in Business Intelligence Table of Contents Introduction…………………………………………………………………………………3 Motivation…………………………………………………………………………………..
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Additionally, both quantitative and qualitative researchers use techniques to verify their data. Quantitative investigators incorporate numerous control procedures and random sampling techniques to maximize internal and external validity. Lastly, Onwuegbuzie and Leech also note how both type of inquirers attempt to explain complex relationships that exist in the social science. Hypothesis testing involves developing the null hypothesis, choosing the suitable test static, identifying the statistical significance, deciding the decision rule to reject or not reject the null hypothesis, and collecting the data and completing the required calculations. The null hypothesis is simply a statement, which the investigator comes up with, that is being tested.
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