Read The Case Study Titled Business Intelligence For Small A ✓ Solved

Read The Case Study Titled Business Intelligence For Small And Middl

Read The Case Study Titled Business Intelligence For Small And Middle-sized enterprises. Write a four to five (4-5) page paper in which you: Provide a succinct narrative of the main lessons learned from the article. Compare the advantages and disadvantages of Web, memory, and vector BI solutions. Consider the author’s notation that BI has become an essential part of all enterprises; discuss three (3) reasons why solutions that are aimed at large-scaled enterprises do not fit well with small and middle-sized enterprises. Reflect upon the commonalities of Web and memory-based BI solutions. Use at least four (4) quality resources in this assignment. Note: Wikipedia and similar Websites do not qualify as quality resources. Your assignment must follow these formatting requirements: This course requires use of new Strayer Writing Standards (SWS). The format is different than other Strayer University courses. Please take a moment to review the SWS documentation for details. Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow SWS or school-specific format. Check with your professor for any additional instructions. Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length. The specific course learning outcomes associated with this assignment are: Describe the relationship between business intelligence, data warehousing, and data mining. Use technology and information resources to research issues in data warehousing. Write clearly and concisely about topics related to data warehouse planning using proper writing mechanics and technical style conventions.

Sample Paper For Above instruction

Introduction

The case study titled “Business Intelligence for small and middle-sized enterprises” offers critical insights into how BI solutions are adapting to the unique needs of smaller organizations. Unlike large-scale enterprises, small and middle-sized enterprises (SMEs) face distinct challenges and opportunities when implementing Business Intelligence (BI). This paper distills the main lessons learned from the case study, compares various BI solution types—Web, memory, and vector—and discusses why solutions designed for large enterprises are often unsuitable for SMEs. It concludes with an analysis of the commonalities between Web and memory-based BI solutions.

Main Lessons from the Case Study

The case study emphasizes the growing importance of BI for SMEs, highlighting that BI is no longer a luxury reserved for big corporations but an essential enabler of competitive advantage across business scales. A primary lesson is that smaller organizations require scalable, flexible, and cost-effective BI systems that can be tailored to their specific operational demands and resource constraints. The article also underlines the significance of leveraging web-based BI solutions as they facilitate easier access to data and improve decision-making processes, even for organizations with limited IT infrastructure.

Another key lesson centers on the importance of user-friendly interfaces. For SMEs, the ease of use critically influences adoption rates of BI tools. The case study stresses that overly complex systems can hinder integration and operational efficiency, reducing the return on investment. Additionally, the case highlights that cloud-based BI solutions can significantly reduce upfront costs and maintenance burdens, making them particularly attractive for smaller firms with limited capital.

Comparison of Web, Memory, and Vector BI Solutions

Web BI solutions are characterized by their accessibility and scalability. These systems are often cloud-based, allowing users to access data and analytics from anywhere with an internet connection. They are generally cost-effective and easy to deploy but may raise concerns regarding data security and integration complexity.

Memory-based BI solutions are known for their speed and real-time data processing capabilities. They store data temporarily in-memory, enabling faster query responses and analytical processing. This makes them well-suited for scenarios requiring rapid insights but less ideal for handling large volumes of historical data due to limitations in memory capacity and expense.

Vector BI solutions utilize mathematical and statistical vector models to analyze multi-dimensional data. They are especially valuable for complex data analysis involving multiple variables and relationships, such as in predictive modeling and pattern recognition. However, they require specialized expertise and may be less accessible to non-technical users, which can hinder widespread adoption in SMEs.

Challenges of Large-Scale BI Solutions for SMEs

The case study points out that BI solutions tailored for large enterprises often do not align well with the needs of SMEs for three main reasons:

1. High Cost and Complexity: Large-scale BI systems often entail significant investment in infrastructure, licensing, and personnel training—costs that are prohibitive for many SMEs with limited budgets.

2. Over-Designed Features: These solutions typically encompass extensive functionalities that exceed the operational needs of SMEs, leading to unnecessary complexity and underutilization.

3. Implementation and Maintenance Burdens: The scale and complexity of enterprise-grade BI require specialized IT teams for implementation and ongoing maintenance, posing a logistical and financial challenge for smaller organizations.

Commonalities Between Web and Memory-Based BI Solutions

Despite their differences, Web and memory-based BI solutions share several key features:

- Real-Time Data Processing: Both facilitate rapid data analysis, providing timely insights that support quick decision-making.

- Accessibility: They promote user accessibility through web interfaces or in-memory processing, which can be accessed remotely or on local machines.

- Flexibility and Scalability: Both models can be scaled according to organizational needs, although they do so through different mechanisms (cloud deployment vs. hardware upgrades).

These similarities underscore the trend toward agile, accessible BI systems that cater to the needs of smaller organizations, emphasizing speed and user-friendliness.

Conclusion

The case study underscores that BI has become indispensable for organizations of all sizes, but especially for SMEs seeking competitive edge without excessive resource expenditure. Web and memory-based solutions offer compelling advantages due to their speed, accessibility, and scalability. However, large-scale enterprise solutions often prove incompatible with the constraints and needs of smaller firms, mainly because of high costs, unnecessary complexity, and maintenance challenges. Recognizing these factors allows SMEs to adopt tailored BI solutions that drive insights and strategic decisions effectively.

References

  1. Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and OLAP technology. ACM Sigmod Record, 26(1), 65-74.
  2. Inmon, W. H. (2005). Building the data warehouse (4th ed.). Wiley.
  3. Kimball, R., Ross, M., Thornthwaite, W., Mundy, J., & Becker, B. (2013). The data warehouse toolkit: The definitive guide to dimensional modeling. Wiley.
  4. Negash, S. (2004). Business intelligence literature review. Communications of the Association for Information Systems, 13(1), 177-212.
  5. Sharda, R., Delen, D., & Turban, E. (2020). Business Intelligence and Analytics: Systems for Decision Support. Pearson.
  6. Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9), 96-99.
  7. Zhang, J., & Xu, L. (2018). Cloud-based business intelligence solutions for small and medium-sized enterprises: Challenges and opportunities. International Journal of Business Intelligence and Data Mining, 13(1), 24-43.
  8. Kim, S., & Kim, Y. (2019). A study on the adoption factors of BI systems for SMEs. Journal of Business Analytics, 4(2), 119-132.
  9. Power, D. J. (2002). When data warehousing meets online analytical processing. Communications of the ACM, 45(2), 41-45.
  10. Laudon, K. C., & Traver, C. G. (2016). E-commerce 2016: Business, technology, society. Pearson.

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