Guest Blog: Balancing Expectations with Abilities when teaching introductory business analytics to non-specialists

This month we are proud to present a thoughtful blog post from one of early members, Dr Sam Buxton. Sam's words resonate with our presentation earlier this year on what business analytics skills companies want and what tools they use. That talk covered the results of a survey that we ran with over 200 participants. The results showed that companies most value the ability to use spreadsheets, i.e., Excel, a solid knowledge of statistics and the ability to visualise and present data well. 

Best, Chris, Chair of BAEF

 As an educator of business analytics, it is important to consider the environment in which we teach. Over the summer, there have been some interesting talks on AI in education, embedding business analytics in accounting programmes and what skills and tools do companies require of business analysts. This post focuses on students’ perspectives of the tools they think they need.

Teaching business analytics to non-specialists at both undergraduate and master’s levels brings its own set of challenges from how students think they can use AI to get around completing the analysis to wanting to learn to code in Python and R or use PowerBI.

The challenge I find that presents itself most often comes from students with the question, why are you teaching business analytics in Excel when we could be using Tableau, Alteryx, PowerBI, Python or R? Now, having taught analytics for 10 years my response is well can you do analytics in Excel? The general response to this question from both undergraduates and postgraduates alike is no. As an educator, I then like to delve into their understanding of software like Tableau, PowerBI and Python with the simple questions of:

·         Can you code?

·         Do you know how you would import data into Tableau and PowerBI?

Well, when answering the first question of Can you code students will say isn’t that what you are going to teach me? No, I will teach you how to run analytics and interpret the analytics. Teaching you to code is a separate thing all together. Teaching them both at the same time for an introductory module would be quite a heavy task.

The second question of knowing how you would import data often seems like a confusing one to students. I then must explain that most software uses Excel as a base, especially PowerBI and Tableau. These software packages are data visualization tools. The data must be input into these packages in a basic form. Often as an Excel file. Most students do not seem to realise this.

I have had several students thank me for teaching them to use and complete data analytics in Excel because that is what most companies use as a base. I have some excellent students going on to excel in the analytics field by gaining additional training once they have a proficient understanding of how to complete this in Excel.

When a company is strapped for cash and software packages are expensive, most computers come with a Microsoft Excel package with free or additional purchasable add-ins that will allow a company to complete rigorous data analysis at a fraction of the cost of a software package such as Tableau or SAS. Therefore, knowing the basics has a greater employability advantage for our non-specialist students to gain work in an analytics field.

The key message from me is while we do need to develop more technical aspects, we need to make sure that our courses and books are aimed at making students proficient based on their entry level to the course. This may be teaching non-specialists using Excel but developing more coding and specialist content for students that already understand coding or statistics.

Dr Sam Buxton, Senior Lecturer in Business Analytics & Programme Director for MSc Management (and pathways), Swansea University.