Big Data and Information Systems Assignment Report Sample
INTRODUCTION
Big data is defined as a tremendously diverse and large collection of unstructured, semi-structured and structured data that grows increasingly with time. Such sets of data are so complicated and huge in variety, velocity and volume that the conventional data management mechanisms cannot process, evaluate, and store them. Moreover, an information system refers to a combination of telecommunication networks, hardware, and software to gather useful and valuable data, particularly within a company. Several companies make use of information technology for completing and managing their operations, communicating with their customers, as staying ahead of their competition (Batko and Ślęzak, 2022). This report will describe how big data could be effectively implemented for assisting business processes, objectives and activities of Amazon and will present strategic recommendations on incorporating big data in the company. Moreover, it will also examine numerous types of information systems and how each system assists decision-making across different management levels within Amazon.
PART 1
Applied big data to support Amazon’s business activities and objectives
Amazon, a leading e-retailer globally, is undergoing swift development within both the operations of its business and also its customer base. Because of this, the organization is dealing with large data volumes which require to be handled an evaluated in an efficient way in order to uphold its competitive advantage. Big data is something that includes processing big sets of fast-moving and complicated data, provides an influencing solution for addressing such challenges. Fundamental value proposition of Amazon is its concentration on offering a flawless and customized shopping experience. Big data has the power to substantially augmenting customer experiences through allowing for an exact customer segmentation, dynamic pricing and personalized suggestions. Through evaluating buying patterns, feedback and search history. The company can forecast the buying behaviors of customer and provide personalized product recommendations. For instance, utilizing big data analytics, the company can offer product suggestions on the homepage on the basis of customer demands and preferences, as well as browsing data, which can lead to rise in conversion rates.
Moreover, the company functions through a very complicated supply chain network. Also, due to COVID-19 pandemic driven scarcities of goods or trade disturbance of Brexit, one must be known to the fact that contemporary supply chains are unexpectedly brittle. Big data permits for real time predictive evaluation and aids in keeping the global network of production, distribution and demand to work in a great way (Sheng et al, 2021). This is imaginable as big data mechanisms can incorporate data on trends of customers in order to offer information which is not seen anywhere before. Through assessing data from numerous sources like weather information, seasonal drifts, and orders of customers, the company can forecast demand of product in a precise way and also alter its inventories as per that. This assists the company in reducing stock outs, as well as risk of storing additional inventory, confirming goods are obtainable at a place where the customer need them. Also, concurrent data analytics could be utilized in order to improve delivery routes, decreasing costs and time required in shipping of products.
Additionally, Amazon might face security and fraud risks as it processes vast bulk of transaction on daily basis. The role which big data plays in fraud analytics is becoming progressively important, since it authorizes financial institutions to recognize complicated arrangements, forecast risky activities and improved security measure concurrently, confirming a secure and safe transactional atmosphere for the customers. Moreover, through evaluating data of customer, retailers can know doubtful purchases and avert fraud. For instance, if a buyer buys products with the use of credit card that is stolen, the retailer can make use of big data in order to spot fraud and also avert it from taking place again. This help the company uphold the trust of customer and eliminate financial dangers.
Furthermore, big data enables business to gather and process concurrent data points and also evaluate them in order to adapt swiftly and uphold a competitive edge. Such insights could direct and accelerate the production, planning and also introduction of new features, updates, and products. Big data analytics could assist innovation determinations of Amazon through offering perception into preferences of customers, trends in the market, as well as the business activities of competitors. The company can make use of big data in order to recognize the loopholes within the markets and develop new services or products which provide for requirements which are not fulfilled (Jensen, Nielsen, and Persson, 2023). For example, the company can assess reviews of customers, queries raised at search engine and social media interaction for acquiring knowledge about the new fields of interest or well-known product characteristics. Such perception can inform the innovations such as Alexa or Amazon Echo.
Strategic recommendations on integrating big data into the company
Amazon can make investment within advanced or progressive data analytics technologies and tools, like artificial intelligence and machine learning algorithms, in order to improve the company’s data processing abilities. The company should make use of specialized tools as well as big data platforms for process and assessing large volume of data in an effective manner. Such tools can assist in uncovering concealed insights and patterns, enabling for informed and better decision making and policies and strategy formulation. Machine learning models could be utilized to forecast behavior of customer, augmenting supply chain effectiveness and improve pricing plans. In addition, AI-driven chatbots could augment customer service through replying to concerns or queries and solving problems without any human involvement. For example, Unilever uses AI which assists it to flawlessly connect product formation, suppliers and demands of customers, logistics, planning and manufacturing (Unilever, 2025).
In addition to this, for fully leveraging big data, the company should nurture a data based decision making culture crosswise each and every level of the company. This encompasses of motivating staff and also managers to take their decisions on the basis of data insights, instead of only experience or intuition. Moreover, the training programs need be employed in order to improve skills of workforce in data evaluation and understanding. Through encouraging such form of work culture, the company can confirm that its actions and plans are supported through a strong evidence. Google is the ideal example of effective data navigation in the current history. It applies data driven approach to almost all the business domains, involving human resources. The company have used their data in order develop a competitive advantage and inspires the company like Amazon to revolutionize their business operations, processes and activities (CodeStrigners, 2025).
Another strategic recommendation to Amazon is to employ robust data security as well as risk reduction measures. Solid security protocols to safeguard data from violations and unauthorized access are crucial for any volume of data, however the scale of this job makes it highly significant. Further, security measures such as access controls, frequent security checks and encryption are pervasive and need to be thoroughly employed. Risk or danger reduction assist IT department to get ready for probable data violations or losses with taking active steps like disaster recovery strategies, consistent monitoring for any kind of security issues, and frequent backups. Along with this, it is important for the company to follow data protection rules and guidelines like General Data Protection Regulation (GDPR). Through implementing robust security measures and clear data managing practice, Amazon can uphold trust and faith of customer and evade legal challenges.
Furthermore, Amazon should confirm continuing data governance, as it is crucial for handling data assets in an efficient way and also in adherence with the rules and regulations. The company can put in place clear and transparent procedures and policies for managing data, involving data quality, access, security, and regulation adherence (Mantri, and Mishra, 2023). Continuing governance confirms ongoing data managing crosswise the organization, reduced regulatory and legal dangers and assists in maintaining the complete data quality.
Part 2
Types of information systems and how they support different management levels at Amazon
Information systems plays an important role in assisting decision making through offering the essential perceptions, tools, and data. Though several information systems provide numerous advantages, usually businesses use the below elaborated ones in order to achieve their objectives:
- Management information system (MIS) - The management information systems offer help to managers through automating diverse procedures which were performed manually in the starting. Activities of business such as business performance evaluation and monitoring, business decision making, formulating a business plans and describing the flow of work. This also offers feedback through assessing the responsibilities and roles to the managers. MIS is considered a substantial application which assist managers enormously at Amazon. MIS offer managers with accessibility to data in relation to expenses, sales, inventory levels, as well as numerous other key performance indicators within the form of statistics and reports, and also analysis technologies and tools, making it simple to comprehend and use the data to take tactical decisions. MIS assist decision making at the tactical level within a large company, through offering crux of thorough data as well as converting it into easily understandable information. Managers utilizes such information in order to assess performance of the business and recognize improvement fields, and execute curative measures. For instance, inventory system of Amazon could utilize Management information system in order to generate reports on the sales trends, warehouse performance and inventory levels, enabling managers to take decisions on reallocating or restocking products crossways fulfillment centers.
- Transaction process systems (TPS) - Transaction processing system are defined as the basic and simple business systems developed to manage everyday operations of a business, like managing payments, handling inventory as well as processing orders. These systems are vital to the functioning of any business, either small or large. The strange feature of such type of system is that it enhances the reliability, consistency and performance of transactions of the business. TPS assists Amazon to confirm that their day-to-day operations run effortlessly, precisely and in an effective manner. This system assists decision making at the operational level within a large organization, since it manages daily practice of a business like sales transactions, processing payroll and more. It aids managers at the management’s operational level to make swift, data-based business decisions associated with these procedures (Alawamleh et al, 2021). At the company, Amazon, Transaction processing systems aids in confirming that its e-commerce platform works in an efficient way through processing a large numbers business transaction daily. This system plays a crucial role in upholding precise and exact financial records as well as tracking the levels of inventory.
- Decision support systems (DSS) - Decision support systems are developed to assist managers make complicated decisions through offering them with the analytical technologies and tools to gain perception within multifaceted business issues. This information system can also aid managers to put on diverse situations, do what-if assessment, and estimations on the basis of real-time and historical data. DSS incorporate data from several sources and involve machine learning algorithms and artificial intelligence in order to assist strategic decisions. DSS assist decision making at strategic and tactical levels in a large organization, as it offers models and tools which can aid managers analyze complicated information, perform forecasts and discover numerous decision choices. For instance, it can support decisions in relation to new market entrant, new product introduction in the market or pricing plans (Fu et al, 2022). Moreover, for the company, a Decision support system can be utilized by top levels managers in order to assess diverse marketing campaigns and improving pricing plans. For example, if Amazon desired to introduce a sale, this information system can stimulate numerous discount models for forecasting which will exploit satisfaction of customers and maximize company revenue.
- Executive support system (ESS) - An executive support system assists executive at top-level to handle flow of work and take business decisions. This system is very alike to Management Information System. It offers improved computing abilities, telecommunication, and also effectual display choices to the executives. This system also allows executive to obtain information and data through static graphs, textual information produced on demand and reports. Further, it assists in tracking strategies of competitors, predict future trends in the market, among other people and helps in measuring performance. Moreover, ESS allows senior managers to analyze the entire company performance and also screen tactical objectives (Parulian, Ali, and Sawitri, 2023). It concentrates on both external data like trends in the market, as well as internal data, such as financial performance in order to offers perceptions within the long-run direction of the organization. At Amazon, an ESS can offer high-level executives with concurrent, inclusive report on worldwide sales, operational performance and market demands and trends, assisting them to make business decisions in relation to introducing new business line, merger or diversification.
- Experts System- Experts systems involve proficiency or expertise to help managers to detect issues or also in resolving problems. Such systems are relied on artificial intelligence research principles. It is a knowledge driven information system, which utilised its knowledge related to a particular concept and serves as a practised advisor to the users. Software modules and knowledge base are the elements of an expert system. Such modules do interpretation on the basis of knowledge, as well as provide replies to questions of users. Expert systems support decision making at all levels, these systems could be utilised crosswise numerous management levels for dedicated tasks, like predictive maintenance, technical debugging and customer assistance. Through offering high-level mentorship, the systems can improve decision making, augment efficiency and decrease mistakes. The company, Amazon can get advantages from such information systems as it assists in improving operations of warehouse through routinely detecting possible block and suggesting solutions, augmenting entire operational competence.
Recommendations for Amazon
Based on the requirements of Amazon and forms of information systems underscored above, two systems which would especially improve the company’s decision making are DSS and ESS. These systems are elaborate with reason for recommendation and examples below:
Decision support systems (DSS): A decision support system will allow the company to make highly informed decisions in relation to market plans, assessment of behaviors of customers, as well as pricing of goods and services. Through incorporating huge volume of data from international operations, this information system would enable management of the company to assess numerous situations and forecast possible results prior to taking important business decisions (Sari, and Butun, 2021). For instance, if the company is thinking of introducing a new product, this system can evaluate demand patterns of customer, trends in the market and competitor pricing strategies in order to forecast the growth of the new product. For example, eBay uses decision support systems to evaluate data related to product trends and also preferences of customers, assisting them to improve satisfaction among its customer and offering. Such perception can be applied at the company, Amazon in order to augment its business lineup as well as market plans (MongoDB, 2025).
Executive support system (ESS): An executive information system would be useful and valuable for the executives of the company, offering them with a thorough picture of the performance of the company crossways diverse operations and markets. Such systems can exhibit key metrics like customer satisfaction, sales performance, competitor business practice and supply chain competence, offering executive the tools for making tactical business decisions associate with acquisitions or global diversification. For example, Tesco uses an ESS in order to monitor performance metric crosswise its global operations. Through checking data related to sales concurrently and performance of supply chain, the leadership at the company can swiftly answer to opportunities and issues within the market.
CONCLUSION
On the basis of above discussion, it can be concluded that employing big data within the businesses like Amazon can support its business objectives, activities and processes. Applying big data in business can help in improving experiences of customers, product suggestions, making the business operations flawless, managing risk, monitoring sentiment of customer and also recognising the developing trends. Moreover, incorporation of information systems in decision making procedures improves receptiveness and flexibility of companies. Through offering concurrent access to critical perceptions and data, information systems allow businesses to acclimate swiftly to the evolving conditions in the market, as well as investing on the upcoming chances of growth. Overall, this report has highlight application of big data into business processes, objectives and activities of Amazon, and also provided strategic recommendations to the company in relation to its integration. Further, it has discussed different types of information systems and recommended Decision support systems and Executive support system for Amazon.
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REFERENCES
Books and Journals
Alawamleh, H.A., ALShibly, M.H.A.A., Tommalieh, A.F.A., Al-Qaryouti, M.Q.H. and Ali, B.J., 2021. The challenges, barriers and advantages of management information system development: Comprehensive review. Academy of Strategic Management Journal, 20(5), pp.1-8.
Batko, K. and Ślęzak, A., 2022. The use of Big Data Analytics in healthcare. Journal of big Data, 9(1), p.3.
Fu, Q., Abdul Rahman, A.A., Jiang, H., Abbas, J. and Comite, U., 2022. Sustainable supply chain and business performance: The impact of strategy, network design, information systems, and organizational structure. Sustainability, 14(3), p.1080.
Jensen, M.H., Nielsen, P.A. and Persson, J.S., 2023. Benefits from Big Data Analytics Projects: A Critical System Heuristics Approach to Boundary Judgements. In European Conference on Information Systems: Co-creating Sustainable Digital Futures (pp. 1-15). AIS Electronic Library.
Mantri, A. and Mishra, R., 2023. Empowering small businesses with the force of big data analytics and AI: A technological integration for enhanced business management. The Journal of High Technology Management Research, 34(2), p.100476.
Parulian, R., Ali, H. and Sawitri, N.N., (2023). Executive Support System For Business and Employee Performance: Analysis Of The Ease of Use Of Information System, User Satisfaction and Transformational Leadership. Dinasti International Journal of Management Science, 4(6), pp.1031-1041.
Sari, A. and Butun, I., 2021. A case study of decision support system and warehouse management system integration. In Decision Support Systems and Industrial IoT in Smart Grid, Factories, and Cities (pp. 111-138). IGI Global.
Sheng, J., Amankwah‐Amoah, J., Khan, Z. and Wang, X., 2021. COVID‐19 pandemic in the new era of big data analytics: Methodological innovations and future research directions. British Journal of Management, 32(4), pp.1164-1183.
Online
CodeStrigners. 2025. Online. Available through: <https://www.codestringers.com/insights/data-driven-companies-four-compelling-case-studies/#:~:text=Conclusion,to%20revolutionize%20your%20own%20company.>
MongoDB. 2025 Online. Available through:<https://www.mongodb.com/blog/post/ebay-building-mission-critical-multi-data-center-applications-with-mongodb#:~:text=Pairing%20its%20rich%20catalog%20of,complemented%20with%20decision%20support%20systems.>
Unilever. 2025. Online. Available through: < https://www.unilever.com/news/news-search/2025/how-unilevers-digital-transformation-is-driving-operational-excellence/#:~:text=We're%20using%20AI%20to,research%20hubs%20in%20the%20industry.>
