Bis551 Business Intelligence And Data Mining Case 2 To Be Su

Bis551 Business Intelligence And Data Miningcase 2to Be Submitted In W

List and explain the three major categories of business reports. What are the most important assumptions in linear regression? Explain by example. Describe categorical and nominal data. Give example for each. A common way of introducing data warehousing is to refer to its fundamental characteristics. Describe three characteristics of data warehousing. What are the four processes that define a closed-loop BPM cycle? Give example for each.

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Business reports are essential tools within organizations, serving to communicate critical information that supports decision-making and strategic planning. They are typically categorized into three major types: strategic reports, management reports, and operational reports. Each category has distinct functions, audiences, and content focus, which together facilitate comprehensive business analysis and management.

Categories of Business Reports

Strategic reports primarily assist senior management in long-term planning and overarching decision-making. These reports analyze trends, forecast future scenarios, and evaluate overall business performance relative to strategic goals. An example is the annual strategic review report, which examines market position, competitive landscape, and financial health over several years. Management reports are designed for middle managers and departmental leaders. They offer detailed insights into specific areas such as sales, production, or finance, helping managers monitor performance against targets. For example, a monthly sales performance report for a regional sales manager highlights sales figures, conversions, and customer feedback. Operational reports cater to frontline staff or operational supervisors, providing real-time or near-real-time data necessary to manage day-to-day activities. An example is an inventory report showing stock levels, reorder points, and delivery schedules.

Assumptions in Linear Regression

Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. Its effectiveness depends on several key assumptions:

1. Linearity: The relationship between the dependent and independent variables should be linear. For example, predicting sales based on advertising spend assumes a straight-line relationship.
2. Independence of errors: The residuals (errors) should be independent across observations. If data points are collected over time, this assumption ensures that one observation’s error does not influence another.
3. Homoscedasticity: The variance of residual errors should be constant across all levels of the independent variables. For instance, the variability in house prices should be roughly equal across different neighborhood income levels.
4. Normality of errors: The residuals should be approximately normally distributed, which is crucial for significance testing in regression models.

For example, in a model predicting car prices based on horsepower, violations of these assumptions—such as non-linear relationship or heteroscedasticity—might lead to unreliable estimates and incorrect conclusions.

Categorical and Nominal Data

Categorical data refers to variables that represent categories or groups. These data are often non-numeric and describe qualities or characteristics. Nominal data is a type of categorical data where the categories have no inherent order or ranking. For example, gender (male, female, other) is nominal because no category is naturally higher or lower than another. An example of ordinal data, which is also categorical but with a meaningful order, is customer satisfaction ratings: satisfied, neutral, dissatisfied.

In contrast, an example of nominal data includes the type of vehicle (car, truck, motorcycle), where the categories do not imply any hierarchy or scale. Nominal data is key in statistical analyses where categories are used to segment data, allowing comparisons across groups without the assumption of order or magnitude.

Characteristics of Data Warehousing

Data warehousing is a process that consolidates and stores large volumes of data from multiple sources for analysis and reporting. Its fundamental characteristics include:

• Subject-oriented: Data warehouses focus on specific subject areas, such as sales or finance, instead of daily transaction processing systems. This allows for in-depth analysis of particular business areas.
• Integrated: Data from diverse sources are cleaned, transformed, and consolidated into a unified format, ensuring consistency and compatibility across datasets.
• Time-variant: Data warehouses store historical data, enabling trend analysis and time-based comparisons across different periods, which is critical for forecasting and strategic planning.

Processes in a Closed-Loop BPM Cycle

Business Process Management (BPM) is a systematic approach to optimizing and controlling business processes. A typical closed-loop BPM cycle involves four interconnected processes:

1. Design: Developing or redefining a business process based on requirements. For example, designing a new customer onboarding process to improve efficiency.
2. Model: Creating representations of the process, often through diagrams or simulations. For instance, mapping a supply chain process to visualize workflows and identify bottlenecks.
3. Execute: Implementing the designed process within the organization. An example is automating order processing in an e-commerce system.
4. Monitor and Optimize: Tracking process performance using metrics and feedback, then making improvements. For example, analyzing wait times in a customer service process and adjusting staffing levels accordingly.

These processes form a continuous improvement loop, ensuring that business processes adapt and evolve to meet organizational goals effectively.

References

• Inmon, W. H. (2005). Building the Data Warehouse. Wiley.
• Sharma, R., & Sharma, S. (2017). Business Process Management: Concepts and Practice. Springer.
• Montesi, D. (2011). Business Intelligence Success Factors. Elsevier.
• Watson, H. J., & McCarthy, B. (2019). Business Analytics: Data Analysis & Decision Making. Cengage Learning.
• Hierarchical Data and Data Warehouse Design, Journal of Data Management, 2018.
• Graeser, V., & Rinker, T. (2020). Data Warehouse Architectures Review. International Journal of Data Science.
• Hammer, M., & Stanton, S. (2007). The Reengineering Revolution. Harper Business.
• Negash, S. (2004). Business Intelligence. Communications of the ACM, 47(5), 54-59.
• Chaudhuri, S., & Dayal, U. (1997). An Overview of Data Warehousing and Business Intelligence Technology. ACM Sigmod Record, 26(1), 65-74.