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An example of a company that collects big data for competitive advantage is Walmart. I worked for Walmart in the past for over 7 years and their competitive nature is on another level. Not only are their prices lower than their competitors, but they also track your order history and recommend products based on your past orders and shopping trips. If you have the Walmart app you can even find your past purchases on them from not only online orders, but from in-store purchases too. Those electronic receipts you get are tracking you down from your credit/debit cards. I remember a friend asking how she gets an eReciept on her Walmart app when she did not purchase through the app. It is from the swipe of that card. It is scary how you can be tracked by anything nowadays, but this is how data is collected. Then you have social media data with market trends. Now the temperatures are increasing, and households would be buying more frozen treats. Walmart is paying attention to those social media marketers to judge future buying patterns. The corporation is using data mining to find trends in data collected at the point of sale. This is how Walmart is collecting data from a variety of sources to better understand a situation and make more informed decisions. According to the article, How Big Data Analysis helped increase Walmart’s sales turnover? It stated, “Walmart has a broad big data ecosystem. The big data ecosystem at Walmart processes multiple Terabytes of new data and petabytes of historical data every day. The analysis covers millions of products and 100’s of millions of customers from different sources. The analytics systems at Walmart analyze close to 100 million keywords on daily basis to optimize the bidding of each keyword. The main objective of leveraging big data at Walmart is to optimize the shopping experience for customers when they are in a Walmart store, or browsing the Walmart website or browsing through mobile devices when they are in motion” (ProjectPro, 2022). Velocity is the speed and timeliness at which data is collected (Sharpe et al, 2019). When you think about it the click of the mouse on their website is instantly collecting that data from customers browsing and shopping. This is the quick action allowing Walmart to have the competitive advantage. Also, the swipe of the bar code every time a sale is completed can be tracked and collect for data. In the text Sharpe et al (2019) states, “with information about the transactions you make, the websites you visit, and the social media you interact with, retailers can customize offers to you and predict when and how to deliver those offers to maximize the chance that you will purchase. Advertisers can place their message in specific channels to maximize the impact for the group of consumers they are targeting” (p. 748). Veracity is the quality and accuracy of the data. Veracity aids in the separation of what is relevant from what is not, resulting in a better comprehension of data and how to interpret it for action. Walmart has high veracity data that contains many records that are useful to the corporation’s analysis and is making a significant contribution to the overall findings. Companies that use data mining gain a competitive advantage, better understanding of their consumers, greater control over their operations, improves customer acquisition, and uncover new business opportunities. Challenges with managing a data mining project are errors in the data, missing values or incomplete. You cannot predict outcomes if the data has errors. Another thing can be the performance of the algorithm used. Is it efficient to distribute the data. When

thinking of Walmart using data from purchases it made me think of privacy and security challenges. They would have to keep up with their privacy and security of personnel information agreements. ProjectPro (2022, Apr. 26). How Big Data Analysis helped increase Walmart’s Sales turnover? Retrieved from https://www.projectpro.io/article/how-big-data- analysishelped-increase-walmarts-sales-turnover/109#toc-1 Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019). Business statistics (4th ed.).

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