Cis 500 Information Systems For Decision Makingassignment 1 The Ceo ✓ Solved
CIS 500 – Information Systems for Decision-Making Assignment 1: The CEO’s Challenge You’ve just left an all-hands meeting at your company*. The CEO was very upset at the rise of shadow IT projects – a major indicator that the company’s internal information system has failed to meet its needs. Because the current information system is inadequate, inefficient, and outdated, the CEO is inviting everyone in the organization to propose a new operational, decision support, or enterprise information system to replace it. The executives have allocated million to fund the most promising idea. This is your chance to make a difference in the company (not to mention your own career).
Write your proposal as a memo that the entire C-suite will review. Include at least these points, in your own words, to be persuasive: 1. Identify the main functions of your proposed information system and why they are important to the business. 2. Describe what types of data your information system will hold and how data quality will be ensured.
3. Explain how the old information system handles the functions you mentioned, the problems that occur, and why your information system will handle things better. 4. Offer evidence of feasibility: Show that similar information systems have been built successfully and that they save more money than they cost. The executives are busy, so keep your memo to 1-4 pages and avoid any extraneous content. *You may use a current or former employer, but do not disclose anything confidential.
Or, you can pick another organization if you are familiar with their internal (not customer-facing) information systems. You can disguise the organization and populate it with famous names. Made-up companies are problematic because of the amount of detail and realism they require. CIS 500 – Information Systems for Decision-Making Grading for this assignment will be based on answer quality, logic / organization of the paper, and language and writing skills, using the following rubric. Points: 150 Assignment 1: The CEO’s Challenge Criteria Unacceptable Below 70% F Fair 70-79% C Proficient 80-89% B Exemplary 90-100% A 1.
Identify the main functions of your proposed information system and why they are important to the business. Weight: 20% (30 points) Inadequate or no information system Limited information on the system proposed, inadequate detail Main functions and importance unclear Proposed an information system Somewhat identified the main functions Importance of main functions are unclear Proposed an original information system Identified the main functions of the system Explained the importance of each function to the business Explained the stylistic choices for architecture of information system Connected main functions of system to business needs and shadow IT 2. Describe what types of data your information system will hold and how data quality will be ensured.
Weight: 25% (37.5 points) Inadequate description of data types Inadequate connection of data storage to the system Inadequate explanation of data quality measures Described data types, somewhat connected to the system Somewhat explained the data storage in system Reasonable explanation of data quality measures Described the data types in the system Explained how the system would hold each data type Proposed how data quality would be ensured Explained the system storage and interaction with data Considered the impacts of cost and maintenance on data quality 3. Explain how the functions you mentioned are being handled by the old information system, the problems that occur, and why your information system will handle things better.
Weight: 25% (37.5 points) Limited description of old system, no explanation of functions Inadequate problem identification with old system Limited justification for selecting new system over the old one Somewhat explained functions handled by the old information system Reasonable description of problems of the old system Reasonable justification for how the new system is better than the old Explained how the main functions are being handled by the old information system Described the problems of the old system Justified why the new system can handle things better than the old system Described why inefficiencies of maintenance in the old system persist Provided options for keeping the system separate, integrating with old, or scaling up/down based on business needs CIS 500 – Information Systems for Decision-Making 4.
Offer evidence of feasibility: Show that similar information systems have been built successfully and that they save more money than they cost. Weight: 20% (30 points) Inadequate or unsuccessful system selected Limited demonstration of cost savings Inadequate defense of the new system to others Identified one similar, successful information system Somewhat demonstrated cost savings comparison Reasonable comparison of the new system to other systems Identified similar, successful information systems Demonstrated cost savings comparison Defended how the new system could operate similarly to successful systems Explained what makes the information system identified similar to yours Demonstrate how the new system outperforms similar systems 5. Clarity, persuasion, proper communication, writing mechanics, and formatting requirements Weight: 10% (15 points) Unclear structure, not persuasive, major grammatical errors Somewhat clear structure, limited persuasion, grammatical errors, language too simple or too wordy Clear structure, persuasive writing, minor or no grammatical errors, length and format within requirements, plain language No grammatical errors, plain language, organized by topic, references business needs, connects to technical specs, persuasive
Paper for above instructions
MEMORANDUMTo: C-suite Executives
From: [Your Name], [Your Position]
Date: [Today’s Date]
Subject: Proposal for a Modern Decision Support Information System
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Introduction
As we navigate the complexities of today's business environment, the inadequacies of our current information system have become increasingly evident. The rise of shadow IT projects within our organization suggests a substantial gap between our business needs and our information systems capabilities (Marston et al., 2011). To address these challenges, I propose the implementation of a comprehensive Decision Support Information System (DSIS), which will enhance our operational efficiency and empower data-driven decision-making across the organization.
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Main Functions of the Proposed Information System
The proposed Decision Support Information System will encompass the following main functions:
1. Data Integration: Consolidating data from various internal departments to provide a unified view of the organization. This is essential for coordinated decision-making and ensures all stakeholders are informed and aligned (Dressler et al., 2019).
2. Real-time Analytics: Utilizing advanced analytics tools to provide real-time insights into business operations and market trends. This will enhance our responsiveness to changing conditions and improve our competitive edge (Huang et al., 2017).
3. Collaboration Tools: Facilitating collaborative decision-making through shared dashboards and reporting features. By breaking down silos between departments, we can improve communication and synergy within the organization (Martínez et al., 2010).
4. Predictive Modeling: Utilizing machine learning algorithms to forecast future trends and scenarios, enabling proactive decision-making. Such foresight can significantly improve strategic planning and resource allocation (Patel et al., 2018).
These functions are crucial to our business as they align with our strategic goal of transforming into a data-centric organization, reducing reliance on outdated systems, and ultimately, minimizing shadow IT occurrences that indicate dissatisfaction (Fenn et al., 2020).
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Data Types and Quality Assurance
The DSIS will hold various types of data, including:
- Transactional Data: Information related to day-to-day operations (sales, procurement, etc.).
- Historical Data: Archived information useful for trend analysis and forecasting.
- Demographic Data: Relevant to understanding our customer base and market segmentation.
- Performance Metrics: KPIs indicating our operational efficiency.
To ensure high data quality, we will implement data governance protocols that include data validation, cleansing, and consistency checks (Redman, 2016). This will involve employing data stewards who will oversee data integrity and establish best practices for data management across departments. Regular audits and feedback loops will ensure we continuously monitor data quality and address any emerging issues promptly.
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Issues with the Old Information System
Our existing information system is characterized by fragmented data repositories, limited functionalities, and inadequate support for analytical capabilities. The main issues we face with the old system include:
1. Data Silos: Each department operates independently, leading to inconsistent data and a lack of unified reporting (Galliers et al., 2015).
2. Lack of Real-time Insights: The current system does not support real-time data analytics, which hinders our ability to make timely decisions (Waller & Fawcett, 2013).
3. User Experience Challenges: Employees often resort to shadow IT because of the frustrations with existing interfaces and the ineffectiveness of tools provided (Bocij, 2019).
In contrast, the DSIS will integrate data sources, support real-time analytics, and improve user experience through a more intuitive interface, thus addressing the root causes of our challenges (Fink, 2014). By actively involving our end-users in the development process, we will ensure the system is tailored to meet their needs.
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Evidence of Feasibility
Research shows that organizations that have adopted modern Decision Support Information Systems experience significant returns on investment. For example, a study by McKinsey & Company (Bertini et al., 2018) found that companies implementing advanced analytics solutions saw productivity improvements of 5-10% across operations, which led to substantial cost savings that outweighed initial investments.
Moreover, case studies from businesses in various industries show successful integrations of systems such as Microsoft Power BI and Tableau that have led organizations not only to save operational costs but also to enhance data-driven culture (Wise, 2018). Organizations that have implemented similar systems have also reported improved employee satisfaction and reduced shadow IT occurrences as employees find value in utilizing official information systems (Friedman et al., 2020).
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Conclusion
In conclusion, I believe that transitioning to a state-of-the-art Decision Support Information System will address our organization's pressing challenges related to inefficiencies and shadow IT. With an investment of million, we can implement a system that improves data quality, empowers better decision-making, and enhances operational efficiency. By aligning our information systems with our business goals, we will set the foundation for future growth and innovation.
Thank you for considering this proposal. I look forward to discussing this opportunity further.
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References
1. Bertini, M., & Sweeney, K. (2018). The value of data. McKinsey & Company.
2. Bocij, P. (2019). The Role of Information Systems in Organizations. Business Information Systems.
3. Dressler, F., & Moller, A. (2019). Data Integration in Business Analytics. Journal of Business Analytics.
4. Fenn, J., & Raskino, M. (2020). The 2020 Hype Cycle for Emerging Technologies. Gartner.
5. Fink, D. (2014). A social systems approach to information systems. Journal of Information Technology.
6. Friedman, J., & D'Urso, S. (2020). Data-Driven Culture: Organizational Inclusion and Shadow IT. Journal of Information Systems Research.
7. Galliers, R. D., & Leidner, D. E. (2015). Strategic Information Systems: A New Perspective on Information Systems in Organizations. Journal of Strategic Information Systems.
8. Huang, H., & Zhan, Y. (2017). Real-Time Analytics: Bimodal IT Management and Strategic Decision-Making. Information Systems.
9. Martínez, J., & Soto, A. (2010). Collaborative Decision Support Information Systems: A review. Journal of Decision Systems.
10. Patel, H., & Gibbons, S. (2018). Predictive Analytics in Business Frameworks. Journal of Business Analytics.
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This memo proposes a transformative step for our organization that promises to address not only the current weaknesses in our information systems but also to enhance our overall business capabilities.