Introduction
The main problem in the IT digital age refers to consolidating the effectiveness of the Infrastructure of Information Technology (IT). As a result, global enterprises have to take advantage of the IT infrastructure system that makes more profit than other enterprises that do not rely on IT infrastructure. This task is to study the revocation of Amazon’s IT infrastructure related to the database management system, which is broken down into parts. As SDLC (Systems Development Life Cycle) consists of a certain methodology for exposing and fixing data inefficiencies, through it, we can have a possibility for articulating the structured strategy that will help identify, analyze, and fix the problems with the data at Amazon (Gumilang, 2022). The accomplishment of data handling deals with the contributors of the IT global industrial sector’s risks to be better is not the only one. This also makes a specific model nearly as scale and replicated for similar purposes.
Amazon not only gives an example of e-commerce sector success but can also be seen as a trendsetting company in cloud services because it stores a relatively large amount of data daily. This data management will boost operation efficiency, the domain of wisdom for decision-making, and finally, gain a competitive advantage. In parallel with effective data management systems, one can encounter such problems as loss of data information, late reply to data retrieval, errors, and data security issues. These challenges may slow down the business operation of Amazon’s transaction processing and forecasting of weakened customers who have taken the and renew renewal innovations. The institution aims to prove its impact in releasing highly functional solutions by demonstration, thus revealing the foundation for industry competitiveness. This article talks about the improper data management in Amazon as a company, the vice which has some negative effect of the simulation, and how it requires the solution through its severity in the e-Governance system SDLC.
Part A: Problem Research and Identification at Amazon
Amazon, which is probably suffering from a vast scale and various operating activities, comes across data management issues. As a result, it cannot own the situation and serve clients well. Integration issues are one of the key ones, leading to fragments that do not let through the flow of data and routine analysis in particular. Therefore, The GPS systems form a dispensation of the different departments or locations that lack integrations on data handling and access. The comprehension intensifies as more data is generated by Amazonian global actions, customer communication, and complex traffic operations (Gumilang, 2020). In addition, Amazon needs a great deal of data to process, secure, and make it accessible to users under strict parameters. This challenge makes the agility and responsiveness of the company to the changing market patterns even more difficult.
The author chose an example of ineffective data management for the main issue of this problem due to its immediate effect on their operational efficiency and the service quality they are committed to delivering. In the times where big data are undoubtedly one of the most vital things, Amazon’s capability to process information at great speed and with high accuracy is not only important for the company’s continuing to dominate the market, especially due to its prominent innovative heritage. Not only does this issue get fixed and put the organization on the right track, but it is also beneficial for the clients and a priority for implementation.
Insufficient data organization will work against Amazon in processing orders, warehouse break-up, and insufficient stock management. However, these inefficiencies could lead to delayed deliveries, stockouts, and high operation costs. As a result, customers would not be satisfied, so they also cannot be loyal. In addition to that, the need for smoother procedures to collect and correlate data from various sources is known to be the thing that causes delays in decision-making and reduces the effectiveness of responding to the customer’s needs. Regardless of the consequences of ill-managed data, the strategic decision-making of Amazon (Mendoza, 2020). As they are market expansion, customer experience improvements and the implementation of new services depend on the availability of the right data at the right time. As information is the key decision-making process, poor information management can lead to such critical errors as misinformed decisions, missed opportunities, and strategic mistakes. So, in Amazon’s case, it can be a risk for its position and revenues in e-commerce and cloud concerns since these markets are extremely competitive.
It is, therefore, fundamental to introduce a systematic way to help make the current data management bottlenecks more efficient, propelling Amazon’s performance. An advanced data management system would regulate operations to eliminate manual work, minimizing costs that would otherwise be spent on order processing, inventory management, and customer service, among which customer service is the most costly department, thereby improving overall productivity. On the other hand, it will allow Amazon to develop data analytics that will result in a business direction with customer prosperity, personalized experience, and great campaigns that target customers. Amid a quickly evolving digital marketplace, the skill that differentiates one company from another and delivers its competitive advantage is handling the data right. For Amazon, however, managing the information well can enable it to innovate, be agile, and stay focused on customers. This competitive supremacy is the most suitable instrument for the company to continue to be an e-commerce leader and a pioneer in cloud computing, so the greatly required investment in data management issues is the utmost priority that will lead to continued success and increased relevance in the market.
Part B: Applying the SDLC Model to Amazon’s Data Management Issue
Planning and Requirements Gathering
The major aims of Amazon in resolving its data management challenges are to allow for greater scalability so that it can efficiently handle the data volumes that grow with time, provide more satisfactory customer services (that are personalized and delivered on time and thus improved) through the increased operation flexibility that allows it to adapt to market changes or swiftly grab unique opportunities either quickly. These objectives emphasize the importance of a robust data management system, which is the backbone of Amazon’s business model, where the firm is vigorously innovating to ensure continuous technological advancement in e-commerce and cloud computing. To zon might use several strategies in a unified plan. R to comprehensively understand the current system shortcomings and collect user requirements with people from stakeholders’ functioning within various departments can help map the actual number of the challenges of the data management and integration given by (Mohan, 2022). Reviewing the results obtained from the IT staff and the end-users will enlighten us on usability issues and the relevance of certain features. Notably, vulnerability testing of current data architecture can produce various inefficiencies, security breaches, and limitations on scalability. It is essential to know Amazon’s industrial processes so it is a basis for designing a sustainable solution that satisfies the various needs of the company’s operations.
Analyzing System Needs
Using a better knowledge of Amazon’s present systems, it can be seen that the biggest shortcomings come from the isolated data silos, which impoverish data sharing and analysis in different business areas. Various platforms like e-commerce sites, logistics systems, and cloud services often complicate the integration process, making data unified management and analysis difficult. What makes matters worse here is the increasing volume and intricacy of data that Amazon deals with, whose processing upsets the existing infrastructure and hinders data retrieval and storage. The Data Flow Diagram (DFD), which drawings the current data management system, is useful because it allows for a graphical demonstration of how the data moves within Amazon’s IT setup. The DFD will help set up the most effective workflows and identify the bottlenecks, redundancies, and areas needing data integration. The changes can be suggested using the SDLC procedure, like adding computation speed, not including repetitiveness, and completing integration tools. Therefore, this graphical portrayal is created to build the most efficient and coherent data management system.
System Design
The proposed design of the system advocates a data architecture that grows alongside Amazon while focusing on how data is managed, organized, and secured. The primary elements are in the cloud for the rollout of the data and the scale-ability, advanced data integration functions to merge information from different sources, and strong data governance policies for data quality and safety. On the other hand, the solution employs machine learning technologies for predictive analytics and automated decisions, thus improving Amazon’s capability to translate textual info into actionable findings. Concerning the planned system design, Human-Computer Interaction (HCI) feature selection should also be considered as they facilitate internal Amazon users in dealing with the data management system in a straightforward and time-saving way (Christanto, 2023). The company will offer a user-friendly interface for data, analysis tools, customized dashboards for different user types, and an easy-to-use process for data entry, querying, and reporting. Having brought HCI to the forefront, the new design seeks to facilitate access to and utilization of data, enabling Amazon’s teams to work closely with data more meaningfully.
System Testing
To ensure that the newly designed data management system for Amazon complies with the tes company’s strict reliability, efficiency, and user-friendliness requirements, it is essential to run exhaustive testing procedures. From wear and tear to extreme climates, this comprehensive testing strategy intends to examine the system in multiple scenarios, guaranteeing its ability to distribute books throughout any part of Amazon’s huge operation. The testing strategy plan involves boosting testing or stress testing, which entails bombarding the system with extreme conditions like large data volume and many users’ concurrent use of the software. This test is critical to the reliability and scalability of the system. It ensures that the system can manage the maximum operational flow at peak times without struggling to handle work capacity. Testing performance is another of paramount importance since it assesses the system’s response times, throughput rates, and general resource utilization. The standard designed to the committed performance benchmarks will supervise the measuring machine to ensure it delivers expeditious and qualitative requisites, which are all consistent with Amazon’s operational standards.
On the other hand, usability testing is a crucial component in this plan and is related to the varied slices of Amazon users; thus, they can identify the system interface, ease of navigation, and user experience in general. At such a stage, one of the most critical things is uncovering the human-computer interaction (HCI) features that require some improvement to make it less constraining and more efficient. Moreover, this feedback mechanism is stunning, so the system’s design can accurately fit the end-user’s requirements. However, feasible results of the cycle are versatile (Tilley, 2017). Reliability and accuracy are covered first since the system must seamlessly run many scenarios and refrain from reporting any mistakes to provide consistent performance and data integrity. This is the fundamental key to trusting that Amazon’s pledge to deliver smooth service to the customers and ensure operational continuity will be consistent.
In the second option, we assume that the system will test its ability to sustain the load – this means that it has to operate properly even under the conditions of a significantly high data volume rate and user requests. However, the most important issue here is that it is essential for Amazon’s growth. Scalability is one of the keys to the system. Finally, the main feature of the validation is the ergonomic nature of the system; it must provide a user-friendly interface and hassle-free processes that allow easier data management and analysis control for Amazon’s staff without high learning costs. As a whole, these testing approaches and goals create the framework for an outperforming data management system capable of meeting organizational and technical standards and exceeding user experience standards. This complete testing strategy reveals how Amazon is determined to be innovative and efficient and iterate its IT infrastructure system. Thus, the industry is governed by this as a baseline.
Conclusion
Implementing the SDLC model proposed to fix the problem of Data Management Inefficiency in Amazon allowed me to learn that large-scale IT infrastructures have both challenges and opportunities. This project showed that a systematic approach to evaluating the inefficiencies, developing a complete solution, and a test run to confirm the system would enhance skills development and provide solutions to the organization’s needs. This study confirms the fundamental part of reliable data management in maintaining operation effectiveness, strategically making decisions, and improving client satisfaction in modern enterprises. While this technology is ever-expanding, companies will utilize data even more, making this system a framework for other giant organizations looking to improve their operations. Hence, further studies could take the next step to bridge the gap between traditional data management systems and the emergence of new technologies like AI and blockchain by focusing on their usage to provide higher data security, privacy, and data analytics capabilities. Moreover, an analysis that considers the effects of a new edition of data privacy rights regulation on data management methods will yield important tips for active companies in many countries. Real-time and the pace of progress, including in the digital marketplaces and the use of technology, call for continued research so that data management practice remains at the ceiling of innovation and efficiency.
References
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Tilley, S., & Rosenblatt, H. J. (2017). Systems analysis and design. Cengage Learning.
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