Developing HR professional's skills in data analysis and data visualization using Tableau

Tableau Desktop is a data visualization tool for exploring data - and is a great tool if you've never used a software application like this before. At New England College of Business, where I teach a course called HRM560 - HR Metrics and The Value Chain, we teach HR students the craft of data analysis and visualization using Tableau. Students read through a unique case study that presents a variety of information such as the organization's mission, goals, and challenges that some of the managers are experiencing across different departments. These challenges are indicative of an organization that has challenges around data and staffing issues. I don't want to give away the farm, since some of my students may be reading this as well :)

HR students are given a data set containing real-world data (the data has been significantly altered and masked to protect the guilty and innocent) to explore using the Tableau software. HR students are given the task of exploring the data and then they discuss the various insights they've uncovered with their peers via a group project. Students work through a series of tutorials to learn the software, and immediately apply what they've learned to the case study.

The data set was co-created by Dr. Carla Patalano and yours truly specifically for this case study. The data set contains information about employee pay, their gender, race, position, start date, performance score, their manager's name, department, and employment source. The data also contains some productivity data for the production department. There are a wide variety of analyses that one can conduct using just this basic information.

The data set was created in Excel and we ensured that the data was structured appropriately to allow for ease of analysis in Tableau. Everything is lined up perfectly in rows and columns. We ensured that all data was standardized in certain fields, such as ensuring that the Gender field only contained either "Male" or "Female" (and not "M" or "F", or "m" or "f", or "1" or "0", etc). Titles were also standardized as well as Department Names. Essentially, we took a lot of time to tidy up the data. That way, Tableau has an easy time ingesting the data. Plus, students wouldn't have to worry about data quality issues when running their analyses. Data should be relatively high quality to make your visualizations more worthwhile.

We begin the course by having students create simple visualizations, such as the one that you see here. I ask the students "what story is emerging from exploring this data"? This organization is obviously production-centric (they manufacture widgets), and the production department is very female dominated. Interestingly, the IT department has an equal number of females and males, which is interesting in itself, because if you work in IT, you know that this finding is atypical. Thus, a story begins to emerge about this company. By the time they conclude the course, students will have completed at least 30 visualizations of varying complexity, and several visualizations use calculated fields, filters, and other advanced features of Tableau.

Here are some of the questions that we ask students to create visualizations for:

1. Where do our employees live? (create a map visualization of active and future start employees).
2. What is our best recruitment source for improving the diversity of our organization?
3. Conduct an analysis of the reasons why employees were terminated, organized by their performance scores. Was pay rate an important factor when employees terminated their employment? Why or why not?
4. Create an analysis that explores various performance scores across the various departments. Are there departments that have employees with overall better performance scores than others? How is this analysis tied to the organization's overall strategy, and is this a useful metric to measure?
5. Are there any issues with employees leaving certain managers? Are there more employees leaving certain managers over other managers? (not a loaded question, but rather does the data show that certain managers seem to have a lot more people leaving them than other managers).

The entire idea is to have them explore the data, and generate even more questions. In fact, I encourage students that are new to data exploration to ask a LOT of questions. That's what data exploration is all about!

Here are the key take-away's that I want to share with you:

1. A simple Excel-based data set is enough to get started with data visualization. Our data set only contains 209 rows and 16 columns of data.
2. Tableau has a lower learning curve than many other visualization tools. Students create their first visualization within 5 minutes of using the software.
3. Tableau can be used to help you answer challenging HR-related questions, and help you uncover interesting insights about your organization.
4. HR professionals should develop skill in analytics and visualization. Tableau is a great way to get started.
5. Let the data speak to you and tell you a story about what's going on.

Disclaimer: I do not work for Tableau, nor do I have any financial interest in their company. I am not a salesperson for Tableau either ;). The main point here is that any visualization software would be helpful to learn for HR professionals. It could just as easily be Qliq, or Spotfire, or PowerBI. We chose Tableau because of its low learning curve and Tableau's generosity of providing free licenses to students and faculty.

Do not hesitate to reach out if you have questions about the case study or data set that Dr. Patalano and I created, and how we are helping HR students develop new skills in visualization and data analysis.