Differentiating Data Analytics Techniques for Strategic Business Decisions

Prior to beginning work on this assignment:

· Review all chapters of the course textbook, Business Analytics: Communicating with Numbers, 2e. Jaggia, S. (2023). Business analytics: Communicating with numbers (2nd ed.). McGraw-Hill Higher Education.

· Review the Three Types of Analytics Techniques infographic below

A diagram of data analysis AI-generated content may be incorrect.

Long description

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 question 2, 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 (Descriptive, Predictive, or Prescriptive) 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 for one of the three types.

· 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.

· ILOSTAT : https://ilostat.ilo.org/

 DataBank : https://databank.worldbank.org/home.aspx

 Business Ready (B-READY) https://www.worldbank.org/en/businessready?economyid=194

The Data and Decision Analytics Assessment paper

· Must be two to five to six double-spaced pages in length (not including title and references pages, charts or tables), and formatted according to APA Style

· must include a separate title page with the following:

· title of paper in bold font

· Space should appear between the title and the rest of the information on the title page.

· student’s name

· name of institution

· course name and number

· instructor’s name

· due date

· must utilize academic voice

· 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

· must use at least two credible source in addition to the course text

image2.png

image3.png

image4.png

image5.png

image1.png

Struggling with where to start this assignment? Follow this guide to tackle your assignment easily!

Step-by-Step Guide for Students

Step 1: Understand the Assignment

Read all chapters of Business Analytics: Communicating with Numbers, 2e by Jaggia (2023).
Review the Three Types of Analytics Techniques infographic (Descriptive, Predictive, Prescriptive).
Understand that the paper is a summative assignment reflecting knowledge of data-based decision-making.

Step 2: Introduction

Begin with a paragraph introducing the importance of data in organizational decision-making.
Include definitions for the three types of analytics.
End the introduction with a thesis statement that clearly indicates the purpose of your paper.
Example Thesis:
“This paper analyzes the application of descriptive, predictive, and prescriptive analytics in organizational decision-making, demonstrating how data-driven insights support strategic business performance.”

Step 3: Differentiate Between Types of Analytics

Descriptive Analytics
- Explains what has happened.
- Example Data Source: Company sales reports, website traffic logs.
- Purpose: Measure historical performance; identify trends.
- Decision Justification: Helps management decide which products to discontinue or promote.
Predictive Analytics
- Explains what could happen.
- Example Data Source: Customer purchase history, market trend datasets.
- Purpose: Forecast future outcomes.
- Decision Justification: Supports decisions like inventory management or staffing levels.
Prescriptive Analytics
- Explains what should be done.
- Example Data Source: Optimization models, simulation results.
- Purpose: Recommend actions based on predictions and constraints.
- Decision Justification: Guides strategic decisions such as pricing, logistics, or product launches.

Step 4: Data Visualization

Choose one type of analytics (e.g., descriptive).
Use a dataset (from ILostat, World Bank DataBank, or Business Ready).
Create a chart or graph illustrating the dataset.
Include a caption explaining the insight the visualization provides.

Step 5: Evaluate Data Analysis Methods

Identify at least two methods, e.g.:
1. Regression Analysis (Predictive)
  - Example: Predicting sales growth using historical sales data.
  - Used to anticipate future demand.
2. Data Clustering (Descriptive/Prescriptive)
  - Example: Segmenting customers by purchasing behavior.
  - Supports marketing strategy and personalized campaigns.
Explain how, when, and why these methods are used in a real business context.

Step 6: Strategic Justification

Select a method from Step 5 and justify a strategic business decision.
Explain how insights from the method improve performance or efficiency.

Step 7: Big Data Assessment

Discuss how big data influences organizational performance.
Include examples such as real-time customer analytics, supply chain optimization, or predictive maintenance.
Highlight how big data creates actionable insight and supports data-driven decisions.

Step 8: Conclusion

Summarize key findings.
Restate how descriptive, predictive, and prescriptive analytics contribute to better decision-making.
Suggest next steps for applying these analytics techniques in real organizational scenarios.

Step 9: References

Include at least two credible sources in addition to the course textbook.
Use proper APA formatting for in-text citations and references.
Examples of sources:
- Jaggia, S. (2023). Business analytics: Communicating with numbers (2nd ed.). McGraw-Hill Higher Education.
- Davenport, T., & Harris, J. (2017). Competing on analytics: Updated, with a new introduction. Harvard Business Review Press.

Step 10: Formatting

2–5 to 6 double-spaced pages (excluding title, references, charts).
Include a title page with:
- Bold paper title
- Student name
- Institution
- Course name and number
- Instructor name
- Due date
Follow APA guidelines for headings, spacing, font, and page numbers.

Step 11: Optional Resources for Datasets

Remember! It’s just a sample. Our professional writers will write a unique paper for you.

WRITE MY ESSAY

youressaygenius