Document Type:Thesis

Subject Area:Computer Science

Document 1

Big Data involves the collection of data over various transactions that customers make over a long period of time. Big Data involves data sets that are extremely large and meaningless until analyzed to reveal the trends, patterns, and associations especially when it comes to human being behaviors and various interactions. Big Data promises to leverage on the power of data to draw analytics and give meaningful use to the data especially for industries that require customer predictions from the previous patterns that have been exhibited over a period of time. A case scenario would be data collected from the cash dispensing ATM machines (Walker & Saint, 182). The data can be used to tell the average withdrawal of customers in the ATM and the time periods when people withdraw money a lot.

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With better performing industries, better economies are born and therefore better business practices in the country. Objectives • To identify how Big Data can be used in collaboration with Business Intelligence to bring profitability to businesses • To assess the impact of Big Data and Business Intelligence on companies that have carried out the practice • To explore on ways through which an organization can integrate both their Big Data and Business Intelligence for better decision making. • To identify risks that may come as a result of decisions made by Big Data and Business Intelligence. Research Questions • How can retail industries leverage the power of Big Data and Business Intelligence? • What advantages do retail industries making decisions with Big Data and Business Intelligence have over retail industries that just make decisions that are not data-driven? • What can be done to campaign for data-driven decision-making practices within retail industries? Literature Review Retailers are known to have the most customer related information than any other member who is involved in the production cycle.

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However, information holding and insight are two different terms and this is a reason why most of the retailers are not using the information to drive their ventures into productivity. By understanding what their customer's search for the e-commerce industry was able to customize on Big Data and analytics to make massive profits in the year 2016, earning a profit of twenty-nine billion dollars (Erevelles et al. Understanding what customers want was driven by Business intelligence and Big Data analysis of their customer searches which then enabled them to understand their customer's ecosystem and provide products and services according to what their customers wanted and not according to their gut feelings. While Retail organizations have the biggest edge when it comes to data they also have the highest competition rates with different players innovating and finding better methods to carry out sales in the retail industry environment.

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Walmart as an example is a big online retail shop that has over a million transaction in any given hour. With the kind of transactions, it is only possible to store the data in petabyte databases. By understanding what their customers want retail industries such as Amazon and Walmart are able to provide goods and services that give the buyers a sense of security and a good feeling of safety among their customers. The Big Data theory invented by Arno Penzias and Robert Willison is also important to understand the main need for Big Data, While the two desired silence in their equipment, they always got a static noise which was seemingly useless until they discovered that it was not useless.

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The challenge of their assumptions led to the discovery of the cosmic microwave background radiation (Maass et al. With data seemingly coming from everywhere Big Data in the retail industry requires the understanding of what data is useful or seemingly useful and what data is not useful, most of the time from a decision-making perspective this will challenge the existing norms and practices that the organizations and the industries engage in. Methodology Both a qualitative method and a quantitative method will be used to collect data on the importance of Big Data and Business Intelligence for both firms that have used the approach and the firms that have not used the approach. Sharda, Ramesh, Dursun Delen, and Efraim Turban.  Business intelligence: a managerial perspective on analytics.

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