Data And Decision Analytics Assessment: WLOS 1-4

Data And Decision Analytics Assessmentwlos 1 2 Clos 1 2 3 4

Data and Decision Analytics Assessment [WLOs: 1, 2] [CLOs: 1, 2, 3, 4, 5] Prior to beginning work on this paper Review all chapters of the course textbook, Business Analytics: Communicating with Numbers, 2e. Review the infographic “Three Types of Analytics Techniques”. This final paper is the major summative assignment of this course. It is designed to allow students to reflect on and apply the knowledge of data-based decision-making learned during the course to real-world scenarios. Assessment Guidelines For your workbook: Respond to the following five (5) questions related to one of the learning objectives covered in this course. For each question, confirm your answers with examples of data sets and/or visualizations. While you may choose these from the sample data sets provided in the resources listed for this course, It is strongly recommended that you search for new data sources to use as examples. Questions: Differentiate between various types of data an organization may use to assess organizational performance. Provide an example for each data source. Highlight the purpose of the data sources, the metric(s) it explains, and what kind of decision it would help justify. Create a data visualization graphic that incorporates appropriate data sets. Consider one of the data sets you have shared in question number 1 of this workbook. Evaluate the benefits of at least two different data analysis methods. Share an example of each. Explain how, when, and why these methods have been used in a business situation. Justify a strategic choice based on a data analysis method. Use the data analysis method in Week 3 or another example of your choice. Assess how big data can influence organizational performance. You may consider using an example if you find that helpful to support your argument. Consider how data can create insight into a business problem and provide a sense of decision-making justification. The Data and Decision Analytics Assessment paper must be five to seven double-spaced pages in length (not including title and references pages, charts or tables), and formatted according to APA Style. It must include a separate title page with the following: title of paper in bold font, space between the title and the rest of the information on the title page, student’s name, name of institution (The University of Arizona Global Campus), course name and number, instructor’s name, due date. The paper must utilize academic voice. Review the Academic Voice resources for additional guidance. It must include an introduction and conclusion paragraph. Your introduction paragraph needs to end with a clear thesis statement that indicates the purpose of your paper. For assistance on writing introductions & conclusions, as well as writing a thesis statement, refer to the Writing Center resources. It must use at least one credible source in addition to the course text. The Scholarly, Peer-Reviewed, and Other Credible Sources table offers guidance on appropriate source types. To assist you in completing the research required for this assignment, review Quick and Easy Library Research tutorial, which introduces the University of Arizona Global Campus. While sharing examples for data sets, visualizations, consider referring to one of the recommended websites for this week. These include: ILOSTAT, DataBank, Business Ready (B-READY). If you have questions about whether a specific source is appropriate for this assignment, please contact your instructor. Your instructor has the final say about the appropriateness of a specific source for a particular assignment. Must document any information used from sources in APA Style as outlined in the Writing Center’s APA: Citing Within Your Paper guide. Must include a separate references page that is formatted according to APA Style as outlined in the Writing Center. Review the APA: Formatting Your References List resource in the Writing Center for specifications. Skills Learned: This activity helps you practice the following industry-required skills: Data Management, Graphical Representation, Data Analysis, Strategic Decision-Making, Big Data, Research Methods, Market Analysis.

Paper For Above instruction

This comprehensive assignment provides an opportunity to synthesize and apply the core principles of data and decision analytics learned throughout the course. It emphasizes understanding different data types, visual representation techniques, analysis methods, and strategic decision-making facilitated through big data insights. The following paper is structured to address all five questions, integrating real-world examples, visualizations, and scholarly sources to support the discussion.

Introduction

In today’s data-driven environment, organizations leverage various data sources and analytical techniques to improve performance and make informed decisions. The purpose of this paper is to explore different types of organizational data, illustrate the use of data visualizations, evaluate analysis methods, justify strategic choices based on data, and discuss the influence of big data on organizational effectiveness. Rooted in the principles outlined in "Business Analytics: Communicating with Numbers," the paper provides practical insights into how data enhances organizational decision-making.

Differentiating Organizational Data Types

Organizations collect multiple types of data to monitor and evaluate performance. These include operational data, customer data, financial data, and external data. Operational data, such as production volume or machinery uptime, helps evaluate internal efficiency. For example, a manufacturing plant may track machineDowntime to assess operational performance and identify maintenance needs. Customer data, such as purchase history or feedback, informs marketing strategies and customer satisfaction assessments. Financial data, including revenue and expense reports, guides budgeting and investment decisions. External data, like market trend reports or economic indicators, help inform strategic planning and competitive positioning.

Each data source serves a specific purpose: operational data measures efficiency, customer data evaluates market responsiveness, financial data assesses economic viability, and external data provides contextual insights. The metrics derived from these sources—such as throughput rate, customer satisfaction score, profit margins, or market share—justify various business decisions, from process improvements to market expansion strategies.

Creating a Data Visualization Graphic

Using the operational data example, I created a visualization depicting machine performance metrics over time. The graphic uses a line chart to show machine uptime percentage versus maintenance schedules. This visualization clarifies how maintenance intervals impact operational efficiency and aids in scheduling predictive maintenance. The visual facilitates quick identification of periods with decreased uptime, prompting targeted interventions. Such visualizations enable decision-makers to quickly interpret complex data and act accordingly.

Evaluating Data Analysis Methods

Two effective data analysis methods include regression analysis and clustering. Regression analysis is used to identify relationships between variables; for example, examining how advertising spend impacts sales revenue. This method helps businesses forecast outcomes and allocate marketing budgets more effectively. Clustering, on the other hand, segments customers based on behaviors or preferences. For example, a retailer might cluster customers into groups based on purchase patterns to tailor marketing messages. Both methods, when appropriately applied, offer tangible benefits: regression supports predictive analytics, guiding strategic investments; clustering enhances targeted marketing, increasing customer engagement and retention.

Justifying a Strategic Choice

Applying regression analysis, a company might decide to increase digital advertising to boost sales. The analysis shows a strong positive correlation between online ad spend and sales figures, indicating that increased investment in digital channels could generate revenue growth. This strategic choice relies on data-driven insights, ensuring that resource allocation aligns with predicted outcomes. Such analysis helps organizations minimize risks by basing decisions on empirical evidence rather than intuition.

The Impact of Big Data on Organizational Performance

Big data significantly influences organizational performance by enabling real-time insights, predictive analytics, and enhanced decision-making capabilities. For instance, retailers like Amazon harness big data to personalize customer experiences, optimize logistics, and forecast demand accurately. These capabilities lead to increased sales, customer loyalty, and operational efficiencies. Big data’s volume, velocity, and variety provide organizations with a competitive edge by uncovering hidden patterns and trends that traditional data sources cannot reveal. Therefore, integrating big data analytics into strategic planning can substantially improve organizational agility and performance.

Conclusion

In conclusion, effective utilization of various data types, visualization techniques, and analytical methods can empower organizations to make well-informed decisions. The strategic application of big data amplifies these benefits, driving performance and competitiveness. As demonstrated in this paper, understanding how to differentiate data sources, visualize data effectively, analyze using suitable methods, and justify decisions with data insights is crucial for success in the modern business landscape. Organizations that leverage these analytics tools will be better positioned to adapt and thrive.

References

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