Communicating Business Analytics Results

About this course: The analytical process does not end with models than can predict with accuracy or prescribe the best solution to business problems. Developing these models and gaining insights from data do not necessarily lead to successful implementations. This depends on the ability to communicate results to those who make decisions. Presenting findings to decision makers who are not familiar with the language of analytics presents a challenge. In this course you will learn how to communicate analytics results to stakeholders who do not understand the details of analytics but want evidence of analysis and data. You will be able to choose the right vehicles to present quantitative information, including those based on principles of data visualization. You will also learn how to develop and deliver data-analytics stories that provide context, insight, and interpretation.

Created by:  University of Colorado Boulder

• Taught by:  Manuel Laguna, Professor

  Leeds School of Business

• Taught by:  Dan Zhang, Professor

  Leeds School of Business

• Taught by:  David Torgerson, Instructor

Basic Info Course 4 of 5 in the Advanced Business Analytics Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.2 stars Average User Rating 4.2See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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University of Colorado Boulder CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

Syllabus


WEEK 1


Introduction to the Course



In this module we’ll briefly review the Information-Action Value Chain we introduced in Course 1. Then we’ll see how analytical techniques are applied in business problems, first by looking at some “classic” business problems that have been around for a long time, then by looking at some “emergent” business problems that have resulted from more recent advances in technology.


4 videos expand

1. Video: 0. Introduction to the Course
2. Video: 1. Information Action Value Chain Redux
3. Video: 2. Analytics in Classic Business Problems
4. Video: 3. Analytics in Emergent Business Problems
5. Discussion Prompt: Types of Analytical Problems
6. Discussion Prompt: Is This a New Type of Analysis?

Graded: Week 1 Quiz

WEEK 2


Best Practices in Data Visualization



In this module we’ll learn about a variety of visualizations used to illustrate and communicate data. We will start with the different vehicles used to present quantitative information. We will then look at a set of examples of data visualizations and discuss what makes them effective or ineffective. Finally, we discuss Excel charts and why most of them should be avoided. After completing this module, you will be able to better understand the characteristics of good data visualization and avoid common mistakes when creating your own graphs.


7 videos expand

1. Video: Module Introduction
2. Video: Vehicles to Present Quantitative Information
3. Video: Data Visualization Examples
4. Video: Graphs in Excel and ASP
5. Video: Graphical Excellence
6. Video: Techniques to Display Multiple Variables
7. Video: Excel Charts to Avoid
8. Discussion Prompt: Share an Example of Effective or Ineffective Visualization
9. Discussion Prompt: Annual Energy Review Graph

Graded: Week 2 Quiz

WEEK 3


Interpreting, Telling, and Selling



In this module we’ll cover a number of topics around interpreting data, gathering additional data, and pitching our recommendations based on our analysis. First, we’ll discuss ways in which we misinterpret or misrepresent data and how to avoid them, such as mistaking correlation with causation, allowing cognitive biases to influence how we see data, and visualizing data in misleading ways. We’ll also learn how experimentation can help us obtain more data, including compromises we may need to make in measurement. Finally, we’ll discuss how we communicate our results and recommendations, with a focus on knowing our audience, telling compelling stories, and creating clear and effective communication materials.


7 videos expand

1. Video: Correlation vs Causation
2. Video: Common Cognitive Biases
3. Video: Misleading With Data
4. Video: Market Experiments: When the action is the question
5. Video: Know thy Audience
6. Video: Telling compelling stories
7. Video: Making It Real
8. Discussion Prompt: What Examples of Correlation vs. Causation Have You Recently Encountered?
9. Discussion Prompt: CNN Article about Four-Day Work Week. Is This a Good Data-Based Argument?

Graded: Week 3 Quiz

WEEK 4


Acting on Data



In our final module we’ll walk through two case studies and illustrate the ideas we’ve covered in the course and in the specialization as a whole. The first case shows how experimentation can be used to create data, sometimes with surprising results. The second case presents a comprehensive analysis that illustrates the entire analytic lifecycle, and shows how different methods and both quantitative and qualitative analysis can be brought together to solve one strategically important analytical problem.


4 videos expand

1. Video: Case Study 1: Experiment Driven Analytics and Customer Churn
2. Video: Case Study 2: Multidimensional Analysis for Customer Acquisition - Part 1
3. Video: Case Study 2: Multidimensional Analysis for Customer Acquisition - Part 2
4. Video: Case Study 2: Multidimensional Analysis for Customer Acquisition - Part 3
5. Discussion Prompt: Communicating Control Group Results
6. Discussion Prompt: The "Science and Art" of Analytics

Graded: Week 4 Quiz
Graded: Final Course Assignment