Amazon’s Big Data Analytics incorporation with Business Intelligence
Data has become part of all the firms in the world. The power of developing an excellent customer base and product line is embedded in big data analytics. Big data analytics allow large synthesis chunks of data into useful information used to make business decisions. For any business willing to grow its wings, big data is the resource that allows you to learn about the diversity of the market and all other sectors of interest with great clarity, while saving time for research (Hewage et al., 2018). Fortune 1000 companies 2019 rank Amazon as the second-best company in the world. This article focuses on how Amazon has integrated big data analytics in its business intelligence and assisted in improving service delivery.
Amazon’s Background
Amazon started as an online retailer for books. Jeff Bezos quit his job and started Amazon in a garage where he had rented in Bellevue. He was frustrated by not taking the chance to invest in the future global technology of the internet revolution and took the blame by starting Amazon, a start-up capital of $250,000 in 1995 (Huang & Kang, 2018). Within the first two months of operation, Amazon had sold books to all 50 states and went beyond to more than 45 countries.
The visionary Bezos had foreseen Amazon’s growth in e-commerce selling everything anyone would need, “an everything store.” Bezos’ used to move extremely fast in his decisions and implementation that it was alarming how quickly the company grew. Even the employee could barely keep up (Sun et al., 2018). He fully understood the power behind technology and the internet and was not willing to give away the chance to harness its power when he could. The reason Amazon is what it is today and remains viable decades after its evolution.
Amazon’s Approach to Big Data Analytics with Business Intelligence
Amazon is currently credit in big data analytics for its Echo Look appliance. A state of the art technology used to take photos or videos and a check style software using the latest machine advance learning. When a client instructs Echo Look to take photographs or video, it automatically does so. It sends them to the cloud, stores it, performs data analytics, and then gives fashion recommendations that suit the client (Dedić & Stanier, 2016). It’s a development of personal stylists within the click of a button by employing big data analytics and business intelligence.
The rate of growth in big data analytics in Amazon never stops. The company believes in big data because it has helped propel it be among the best companies in the world of e-commerce. It employs big data analytics in all its functions from customer information, orders, tracking of order delivery, ensuring the closest warehouse to customers are selected, tracking inventory, and reducing shipment cost for all orders to below 40%. Without the employment of big data, it would be impossible to make accurate analytics using other analysis (Hewage et al., 2018). Through this relentless use of big data analytic in business intelligence that allows Amazon to grow exponentially, keep up with the competition, and predict the short and long-term trends of the e-commerce sector.
What They Are Doing Right
The best and biggest method Amazon utilizes big data is the employment of a recommendation engine. The engine predicts other products or services a customer may need when a customer searches for a particular product and lists the products alongside the searched products. These recommendation systems have improved Amazon’s annual sales by 35% due to its ability to persuade customers to purchase more from their listing (Huang & Kang, 2018).
The delivery system is designed to deliver the customer products faster than they arrive by ensuring the manufacturer and Amazon track inventory to select a warehouse closest to the customer. Big data analytics make these functionalities possible, reducing delivery costs by 10-40%. Big data is also used in price optimization, where the customers find the best offers and prices of commodities at Amazon. These attract more customers, increases sales, and net income (Dedić & Stanier, 2016). Prices of products are set through customer activity on the website, competitors’ pricing, order history, availability of products, the marginal profit requirements, among many other factors.
The biggest gain for Amazon in Big Data Analytics was in 2006 when it launched Amazon Web Services and Amazon cloud computing services. The platform offers Amazon and its retailers the ability to analyze customer demographics and habits of spending. The cloud computing services create a scalable application that can harness big data without the need for hardware. The big data applications used in executing cloud computing services help companies use data to quire business intelligence related to customers from a wide spectrum of data available in the Amazon Web Service (Huang & Kang, 2018).
What They Are Doing Wrong
The security status of customer information essential in big data analytics for business intelligence is the most daunting task for Amazon. The internet has millions and millions of viruses, hackers, and stalkers who steal information to exploit customers. It is hard to secure data from all areas, considering the amount of clientele Amazon has (Sun et al., 2018). Hackers can use the customer’s phone or personal computer IP address to penetrate the cloud’s firewall. Cybersecurity for big data is real, and data insecurity is undeniably a menace.
Amazon’s Echo Look, although a very good and advanced technology, is facing challenges due to pictures and video uploaded. People also prefer buying clothes after they feel and fit them rather than just looking at it in photographs or being styled by a virtual machine. The challenges of product quality have also been on the rise due to manufacturers being unreliable in delivering services and products.
Conclusion
The path to success in big data analytics for business intelligence is not 100% effective; knowledge from other aspects need to be considered for better Amazon. The implementation of product sampling techniques for products that customers need to access before buying (Sudhakar, 2018). For amazon to improve to be more successful in the implementation and maintenance of big data analytics with business intelligence, they need to incorporate showrooms and sample products to customers before delivering the actual order for sensitive products like clothes, jewelry, shoes, and other fashions. They can also offer platforms for returning goods if the clients are not satisfied with the product and offer a replacement immediately. Amazon’s usage of big data analytics for their business intelligence should step up their security for data in their cloud platforms and maximize their strengths to deliver quality and better products and services (Sun et al., 2018).
References
Dedić, N., & Stanier, C. (2016, November). Towards differentiating business intelligence, big data, data analytics, and knowledge discovery. International Conference on Enterprise Resource Planning Systems (pp. 114-122). Springer, Cham.
Hewage, T. N., Halgamuge, M. N., Syed, A., & Ekici, G. (2018). Big Data Techniques of Google, Amazon, Facebook, and Twitter. Journal of Communications, 13(2), 94-100.
Huang, X., & Kang, F. (2018). Company reputation and auditor choice: evidence from fortune 1000 companies. Accounting Research Journal.
Sudhakar, K. (2018). Amazon Web Services. International Journal of Management, IT and Engineering, 8(6), 192-198.
Sun, Z., Sun, L., & Strang, K. (2018). Big data analytics services for enhancing business intelligence. Journal of Computer Information Systems, 58(2), 162-169.