0. Introduction to the Course

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From the course by University of Colorado Boulder

Communicating Business Analytics Results

68 ratings

University of Colorado Boulder

Communicating Business Analytics Results

68 ratings

Course 4 of 5 in the Specialization Advanced Business Analytics

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.

From the lesson

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.

0. Introduction to the Course4:04

1. Information Action Value Chain Redux5:29

2. Analytics in Classic Business Problems11:24

3. Analytics in Emergent Business Problems13:34

Meet the Instructors

Manuel Laguna
Professor
Leeds School of Business
Dan Zhang
Professor
Leeds School of Business
David Torgerson
Instructor

Welcome to the fourth course of data analytic for business.

In the first course of the specialization,

they've covered how analytic problems emerge in an information life

cycle that begins with events in the real world and ends with business actions.

In his course, you also learned about technologies that enable analytical work.

You examine data storage and

data basis, along with big data and cloud technologies.

And then in the second course,

walk you through explanatory data analysis and prediction models.

In the third course, you and

I made a transition from predictive analytics to prescriptive analytics.

You learn about capturing uncertainty through simulation,

and you also learn about building optimization models.

These three courses have given you a solid foundation of data analytics for

business problems.

Obviously, the topics that we have covered so

far could be explored at the greater detail, which by the way that's exactly

what we do hear at the Leeds School in our Master's Program in Business Analytics.

So you might want to think about coming to Boulder for a year.

Anyway, at this point you do have an overall understanding

of how analytical organizations use data.

You also know about the main techniques and models for data analytics.

However, all the knowledge and the hard work of doing analysis to gain insights,

are not enough to produce actionable items and results.

The missing piece of the puzzle is effective communication, and

this is what Dave, Dan and I are going to discuss in this fourth course.

Tables and graphs are the main vehicles communicate quantitative information.

However, few of those individuals making presentations or writing documents,

have ever taken a formal course on designing effective tables and graphs.

When should a table be used?

When should a graph be used?

If a graph is used, which type of graph should it be?

So as part of this course,

we will discuss simple guidelines that will help you make these decisions.

If you Google data visualization you will get almost 30 million webpages.

Data visualization tools have become very popular because of their

power to communicate quantitative information through visual perception.

A single well designed graph

is able to communicate an incredibly large amount of information.

Graphs are particularly helpful when you're trying to communicate patterns,

trends or receptions.

They are also effective when comparing several series of values.

Data visualization is definitely great, but

there are things that you need to avoid and you will learn about them here.

The end of the day,

communicating analytical resource is about telling a story, with numbers.

Tables and graphs are instruments, but they're not the story.

Your facts and findings are the story, and you need to communicate them well.

Effective communication, it starts with knowing your audience.

Then, you also need to know general principles of effective storytelling.

I am glad that you have made it to this point of our specialization.

Dave, Dan and

I work really hard on creating a comprehensive view of data analytics.

What you experience in the first three courses, and

what you will experience in this one is the result of a coordinated effort.

We did not want to just each of us do our own thing.

This is our collective view of data analytics in the context of

business problems and decision making.

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