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A 6-Pillar Approach to Teaching Business Analytics

To put statistics into perspective, Michael Ratajczyk, MA, takes a holistic look at businesses in lessons like the one detailed here.

Educator

Michael Ratajczyk, MA

Assistant Professor of Business, Saint Mary’s University of Minnesota

MA in International Business, BA in International Business Management

“My data science career started when I was a cocky little intern,” says Michael Ratajczyk, MA, laughing. “I was at a meeting where everyone was cheering because of the company’s 20% sales growth. I stood up and asked, ‘How do we know that’s good enough? What’s the actual potential for sales?’”

At that point, the division head challenged the Saint Mary’s University business student, saying, “If you’re so smart, you figure out how to collect the data and analyze sales activity so we can figure those things out.” Ratajczyk seized the opportunity, designing some of the company’s first product-specific sales reports, many of which are still used today.

After 12 years working in international business, Ratajczyk was recruited to return to his alma mater—this time to invent a brand-new business analytics program for the school. Just as he did in the corporate world, he questioned the status quo, reflecting on what students need to know beyond the numbers.

True to form, Ratajczyk developed his own set of criteria, coming up with six pillars he feels are of vital importance to anyone entering the business world after graduation. These pillars form the foundation of the many of the business analytics courses, beginning with Data Analysis and Business Modeling during students’ junior year. Below, he shares an overview of those pillars, along with a sample assignment that shows them in action.

Context

“Smart executives of all companies use business intelligence and data analytics to make their decisions. I want students to know all the ins and outs, and to use business analytics to realistically set and achieve a company’s goals.”

— Michael Ratajczyk, MA

Course: BIA 630 Data Analysis and Business Modeling

Description: Get an introduction to advanced concepts in predictive modeling and techniques to discover patterns in data, identify variables with the most predictive power, and develop predictive models. You will also learn about descriptive, predictive, and prescriptive analytics, and optimization models. You’ll use Microsoft Excel and Tableau to engineer and analyze business models and identify the proper use of and complete regression, optimization, and exponential smoothing models.

See resources shared by Michael Ratajczyk, MA

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Ratajczyk’s 6 pillars of business analytics

Ratajczyk developed the following six pillars as the core of his teaching. These pillars allow him to infuse realistic business-world experience into everything students learn. The pillars are:

  1. Business acumen. This means developing an understanding of all aspects of a business, including finance, accounting, marketing, and management, as well as analytics.
  2. Mathematics/statistics. This includes learning how to apply statistics, forecasting techniques, and other types of business math (such as calculating gross vs. net profit margin).
  3. Data command. This involves the ability to “clean” and organize data and spreadsheets manually so information is easier to use.
  4. Computer science. This helps students learn how (and why) to use programming to work with data—for example, automatically cleaning data.
  5. Ethics. This helps students evaluate whether business decisions are morally sound.
  6. Communication. This is about sharing information effectively, both through speaking and writing.

Putting the pillars to use: The Dollar Store Case Study

To understand the significance of the pillars, it helps to see them in action—as in the following “case competition” that Ratajczyk assigns to students in his Data Analysis and Business Modeling course. This case involves the discount convenience store industry (e.g., “dollar stores” such as Dollar General and Dollar Tree).

Its goal is simple: to decide if a retail business owner should raise product prices, based on a full evaluation of existing customer data and statistics. At the end of the project, students come together in self-selected groups of three to create a 15-minute final presentation that synthesizes their findings. They then present their recommendation to a group of 10 judges (two such judging groups divide up all the presentations between them), including data science experts, business executives, and other real-world business decision-makers; the mix varies to offer students exposure to the variety of professionals they will meet in the workplace.

Here are the specific methods Ratajczyk uses to introduce the six pillars within the framework of this project.

Build business acumen with real-world data

“I don’t believe in theoretical textbook examples,” says Ratajczyk. “I use real-world data from actual businesses to put students in the real-world mindset.” He obtains existing data from his personal relationships with companies including 3M and Fastenal. He also uses websites such as Kaggle.com, where educators can access real data.

To kick off this assignment, Ratajczyk provides students with the following:

  • Data on 1,500 separate products, including cost per product
  • 15 months of historical sales data
  • 400,000 rows of data for sales transactions (e.g., who bought which products, when and what they bought, how they paid, loyalty card use, etc.)
  • Upcoming planned and estimated tariffs

Students use the figures provided by Ratajczyk as the basis for estimating demand and product cost, sales, and the impact of any upcoming tariffs—all to help determine profitability. They must also use third-party research (that they find on their own) to evaluate the industry and specific product lines for price elasticity (e.g., what the market will bear and how sensitive the market is to different price points).

Use mathematics/statistics to prompt deep questions

Ratajczyk says that an in-depth look at the numbers often surprises students. Ultimately, he hopes it will lead them to questions they would never have considered, such as, “If we don’t raise our prices, will we actually lose money?”

Other ways students use mathematics in this assignment:

  • Calculating the top 10 customers and products (which is a standard industry measurement)
  • Categorizing sales by product type (e.g., food, supplies) and impact to profit
  • Determining how many customers buy multiple products during each visit to identify patterns between purchases (e.g., do most customers who buy bread also buy peanut butter?)
Improve data command by providing messy numbers

For this project—as in real life—the data set is not in perfect condition. Students must clean the data: They get rid of duplicate entries, deal with blank cells, assess the data with simple charts and graphs, run scenarios and basic statistics (identify the mean, minimum, maximum, mode, median, potential outliers), and more.

Ratajczyk also tells students that they may want to consider obtaining additional data that could improve their ability to make decisions on pricing. For example, looking at income level by household might tell them whether their customer base is affluent enough to deal with a rise in prices.

“Sometimes I give students too much data, such as the loyalty card data and customer birthdays, which have no impact on raising prices,” he adds. “This helps them recognize what they need and what they don’t. Once they learn what they can cut, their work speeds up.”

Use computer science to speed data-cleaning

While it is good for students to understand how to manually clean up data, this is time-consuming and arduous—especially in assignments like this (with 400,000 rows and 30 columns of diverse data). Why do it yourself when a program can do it for you?

For data cleaning, Ratajczyk teaches his students to use Visual Basic, a Microsoft programming language that integrates with Microsoft Office products (including Word and Excel) to identify areas in need of fixing. The programming helps to detect errors and outliers, fix typos, and find blank values in a spreadsheet and plug in correct ones.

Emphasize ethics by grading students on it

In their final presentation for this assignment, students are judged on more than profit maximization. They are also judged on ethics, which includes all the important areas of the business that do not involve the financial bottom line.

Students explore questions such as, “Why do you need to raise prices on essential products like baby diapers in a poor neighborhood?” and “Are there other products we could ethically raise prices on to offset not raising prices on others, to provide more overall benefit to the community?”

Foster communication skills with a high-stakes presentation

Ratajczyk saves this pillar for last, because he thinks it is the most important one.

“I don’t care how good you are with the previous pillars,” Ratajczyk tell his students. “If you can’t explain your work and your position, then it’s as if you didn’t do any of the previous work. Your job is to advise the company on what to do next, so if you can’t clearly discuss it, they won’t have confidence in your work and will feel uneasy about making risky decisions.”

To build their communication skills and confidence throughout the semester, Ratajczyk asks them to rehearse by presenting answers to questions, and he provides feedback on their clarity, delivery, and timing. This practice is meant to provide experience that will support them when they deliver their final presentation.

After the presentations, the students and judges meet at a luncheon. There the judges give students their feedback and also choose one winning team. There is no prize, but the winning group gets the best grade in class—and bragging rights. Some of them even get an internship or job.

“The judges often hire students based on their presentations,” says Ratajczyk. “[The judges] tell me that this final presentation is a great recruitment tool for helping them find talent. At least half the students who take this course in their junior year come back in their senior year with a full-time job offer.”

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