Dynamic Conversations: Business analytics in management education through professional development modules

In this written piece, Dr Anabela Soares of Bristol Business School considers the necessity of ingraining business analytics across Business & Management programmes, to prepare students to cope with data usage to make informed decisions in business contexts.


Practice makes perfect so it is important to consider business analytics throughout students’ training to give them an opportunity to practice these skills frequently. Nonetheless, scaffolding digital literacy skills throughout an entire programme raises concerns about students’ readiness and relevance. This short opinion piece suggests the consideration of personal and professional development or placement modules to bridge the gap between these skills and their relevance from a student perspective.

The more you practice, the more you learn so that means that you would need to train students over an extended period in business analytics. However, the reality of scaffolding skills means that students are probably only able to really grasp how to get around business intelligence/analytics in year 2 or 3 because in year 1 they will be too worried about understanding how everything else works, including critical writing, which is fundamental in any discipline. Hence, a discrete module looking at business analytics or intelligence in years 2 or 3 would work. Nothing new there. The question is how? And what would be the best way to deliver this? A core module? An optional module (that no one ever chooses due to fear of failing)?

I believe these modules would need to be core modules and as embedded as possible with the remaining modules in the programme (maybe even spine modules). The only way we can show how relevant this knowledge will be is by linking to the theory and content they are learning in other modules, so they understand the possibilities in the field they want to gain expertise in (e.g., marketing, HRM, operations, etc).

Universities have included a lot of placement, employment, or personal/professional development modules in their programmes to exhibit their link to practice. So why not use these modules as a bridge to business analytics modules where appropriate? How can you truly prepare students for employment if there’s no acknowledgment of the digital data literacy implied in the positions they are applying for?

Truly preparing students with employability skills means helping them understand the value of all of their skills including data literacy which seems to be commonly blurred with their analytical thinking, critical analysis, or some version of this in their assessments. This is not to say that those are not important, on the contrary, they are crucial! But they need to also be linked to data literacy going one step further than just focusing on theory analysis or application.

The importance of business analytics in management education is not a new topic. However, it is certainly more prominent than ever as data literacy has been established as a necessary skill. Over the years the majority of universities have reduced the amount and depth of analytics training in management degrees. This has been done for several reasons but mainly to remain competitive and exclude the obvious calculations/numeric fears students seem to carry from their younger education experiences.

The problem is that, without an understanding of business analytics/intelligence, management students are at a disadvantage, particularly in a world where data visualization/interpretation has become the differentiating capability among employees. This brings us back to the role of business schools in the world and the responsibilities we have in educating students for the world that is here and the world that is coming.

The truth is that whether we like it or not, unfortunately in the majority of cases, graduates leave the university without an adequate level of analytics skills because they do not recognize their value until they leave.

More and more the “real world” requires multidisciplinary individuals that can be grounded in the different languages of different disciplines in an increasingly complex digital era. Yes, that is challenging, and it seems daunting. But business analytics or business intelligence modules allow you to train students in those multidisciplinary skills in a much simpler and integrated manner.

So first and foremost, we need to reclaim business analytics and intelligence into management programmes and show students the relevance and the potential of the different tools to practice (truly honing employability skills).

I think this requires a shift in mentality and approaches not only from students but more importantly…from us.

There is also the not-so-small matter of ethics in the way business analytics and data are handled. Digital data literacy raises concerns associated with ethics in digital data management and artificial intelligence-generated data (such as the concerns raised by Mo Gawdat in his interesting book “Scary Smart”). Ethical considerations in data literacy can only be considered in the context of a module that makes the information analysed relevant to the future job students want to have.

So, the problem is really three-fold:

  • How do we demonstrate the added value of data literacy in employability-related modules that convince students and senior managers?
  • How do we train students in the art of developing, designing, and interpreting relevant data using the right tools?
  • What are the ethical responsibilities in managing and disseminating this data?

So, the question is, should we embed business analytics in programmes throughout the different modules or should this be a standalone module that prepares students to deal with the increasing amount of data at their disposal to make informed decisions in business contexts?

If it is embedded in different modules, or in one large spine personal development module, shouldn’t we make it a more salient skill that students can promote in their job applications given the current market requirements?

Moreover, how do we combat the negative perspectives and perceptions associated with data analysis? How do we sell this as added value unless linked to future employment?

And how do we include what are the ethical responsibilities in managing and disseminating this data?