First Part Of The Project: Please Refer To The Syllabus

He First Part Of The Projectplease Refer To The Syllabus For The Due

He First Part Of The Projectplease Refer To The Syllabus For The Due

he first part of the project. Please refer to the syllabus for the due date. Each week you will write a minimum of 6 pages excluding the cover page and the reference page for each section of the project. The paper must be in APA format, 12-point font, double-spaced.

Description: The main project involves developing a Business Intelligence Development Plan for a local corporation or an existing company. You will follow a specified process and format, creating individual sections weekly. The final document should be in Word, following the APA template, including a title page, table of contents, section headings, and references.

The project sections include: Business Intelligence Justification, Business Performance Plan, Business Performance Methodologies, Data Classification and Visualization Assessment, Data-Mining Methods and Processes, and an organizational rough draft. You will incorporate the conceptual foundations of decision-making, including Simon’s four phases—intelligence, design, choice, and implementation—and relate this to Decision Support Systems (DSS). Additionally, you will review DSS classifications, components, and support for decision-making in practice.

Each weekly submission will address part of this project, culminating in a comprehensive business intelligence plan supported by scholarly sources cited in APA style. The first section, to be submitted during Week 1, focuses on Business Intelligence Justification: a 6–8 page analysis discussing the background, business environment, organizational decision-making problems, responses utilizing the pressure-response-support model, and how BI can support problem-solving and decision-making.

For the assignment, you will analyze and describe examples of media portrayals of bullying, including one TV show/video and one news report. You will evaluate how bullying is depicted, considering realism, stereotyping, the role of bystanders, and societal impact. This analysis will include detailed descriptions and critical assessment of each example, comparing media portrayals and discussing their influence on societal perceptions of bullying.

Paper For Above instruction

Developing an effective Business Intelligence Development Plan (BIDP) for a local or existing corporation requires an intricate understanding of the organization’s environment, decision-making challenges, and how BI tools can be leveraged to support managerial decisions. This paper comprehensively addresses these areas within a structured framework, anchoring the discussion on a real-world or hypothetical case, supplemented by scholarly insights into decision sciences and BI methodologies.

Background and Business Environment

The foundation of any BI plan is understanding the organization’s background and business environment. Taking Fannie Mae as an illustrative example, the housing finance giant has historically played a pivotal role in fostering affordable housing for nearly 80 years. Its mission underscores the significance of stable and accessible housing options, which are intertwined with broader economic and social factors. Over time, the dynamics of the housing market have shifted—from a period of steady growth emphasizing supply expansion to an era characterized by skyrocketing housing costs and limited supply in high-opportunity areas. These shifts directly influence housing affordability, impacting low- and moderate-income populations, and pose complex challenges for policy makers and corporate leaders alike.

In this environment, several organizational decision-making problems emerge, compelling the need for robust BI solutions. Chief among these are issues related to insufficient information, data overload, decision delays, and organizational misalignments. Addressing these issues requires a nuanced understanding of how BI can facilitate timely insights, streamline data processing, and support strategic alignment.

Identified Decision-Making Problems

  1. Insufficient Information: Decision-makers often lack complete data, hampering their ability to understand issues thoroughly. For example, a lack of real-time data on market trends can delay response times.
  2. Data Overload: Excessive data that is difficult to analyze can overwhelm managers, leading to paralysis or reliance on gut decisions.
  3. Committee Decision-Making: Over-reliance on group consensus might cause delays and misinterpretations, especially if technological advancements are not well understood or accepted by all members.
  4. Missed Targets and Delays: Failure to meet project or strategic goals within planned timelines affects overall organizational efficiency and morale.
  5. Managing Large Teams: Excessive personnel involved in decision processes can cause fragmented communication and inconsistent information flow.
  6. Emotional Attachments: Emotional bonds to decisions or stakeholders can cloud objectivity, impacting rational decision-making processes.
  7. Lack of Communication: Insufficient information sharing among employees creates silos, leading to duplicated efforts or inconsistent information.
  8. No Emotional Investment: A lack of emotional engagement can result in apathy, reducing accountability.
  9. Vested Interests: Personal gains influence decisions, possibly at the expense of organizational objectives.
  10. Problem Fragmentation: Viewing issues as monolithic hampers effective problem-solving; breaking problems into manageable components is crucial.

Organizational Response Using the Pressure-Response-Support Model

The pressure-response-support model offers a comprehensive approach for managing decision-making challenges. For example, to counteract data overload, organizations can invest in advanced BI tools that filter and prioritize information, aligning responses to operational needs. Enhancing staff training supports a culture of technological acceptance, addressing committee resistance to innovation.

Financial pressures, such as the costs associated with litigation or staffing, require strategic responses like process automation and data consolidation. Support structures, including leadership commitment and employee involvement, are critical for embedding BI practices effectively.

For issues like emotional attachments and vested interests, organizational responses might involve transparent communication strategies, fostering a culture of objectivity, and aligning incentives with organizational goals. These responses reduce biases and facilitate objective decision-making.

Impacts on Managerial Decision-Making

Quantitative impacts include improved accuracy in forecasts and resource allocation, driven by data-driven insights. Qualitative benefits encompass heightened managerial confidence, strategic agility, and organizational resilience. For instance, BI tools enable managers to monitor real-time housing market conditions, assess policy impacts promptly, and make more informed decisions that align with organizational missions.

Leveraging Business Intelligence for Problem Solving

Business Intelligence plays a central role in fostering informed decision-making through data analytics, visualization, and predictive modeling. For example, BI systems aggregate housing market data, demographic information, and economic indicators into accessible dashboards, enabling managers to identify trends and opportunities swiftly.

In the context of affordable housing initiatives, BI facilitates the evaluation of various intervention strategies, enabling stakeholders to simulate outcomes before implementation. Furthermore, BI supports continuous monitoring, helping organizations adapt to market shifts or policy changes proactively.

Key BI benefits include cost reduction, enhanced decision accuracy, improved responsiveness, and a competitive advantage in strategic planning. As Turban (2015) notes, effective BI systems encompass data collection, analysis, visualization, and decision-support capabilities, all crucial for addressing the complex challenges faced by housing finance organizations.

Conclusion

The integration of Business Intelligence into organizational decision-making processes equips companies to navigate complex, dynamic environments with greater precision and confidence. By systematically identifying problems, responding adaptively, and supporting decisions with accurate insights, organizations can enhance their operational efficiency, strategic positioning, and societal impact—particularly in vital sectors like affordable housing.

References

  • Turban, Efraim. (2015). Business Intelligence and Analytics: Systems for Decision Support. 10th ed. Boston: Pearson.
  • Turban, E. (2008). Decision Support and Business Intelligence Systems. Pearson Education.
  • Bert Brijs. (Year). Business Analysis for Business Intelligence. Publisher/Source.
  • Negash, S. (2004). Business Intelligence Journal, 9(2), 3-16.
  • Sharda, R., Delen, D., & Turban, E. (2020). Business Intelligence, Analytics, and Data Science. Pearson.
  • Power, D. J. (2002). Decision Support, Business Intelligence, and Analytical Processes. Decision Support Systems, 33(2), 111-119.
  • Laursen, G. H., & Thorlund, J. (2010). Business Analytics: From Data to Knowledge. Wiley.
  • Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An Overview of Business Intelligence Technology. Communications of the ACM, 54(8), 88-98.
  • Few, S. (2009). Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press.
  • Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.

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