The Gap between Academia and Business Sectors!

I participated in a one-week “From Educators To Entrepreneurial Facilitators” workshop with colleagues from different European Universities. That was an exciting debate about the gap between #academia and #industry. I want to share my experience regarding this gap and the possible reasons in this short article.

I started my professional career over two decades ago as a full-time professor at a public university. My research interest was modelling and stochastic processes in different applications. After five years, I preferred to add industry experience to my academic activities by working for business sectors and public institutions. So, I had this opportunity to see both sides of academia and industry for many years. During these years, I saw some academic colleagues believe that work in the industry is not well organised and not necessarily scientific. Conversely, colleagues from the industry complain about the lack of a mutual language with academia, and they usually prefer to work with academic people with industry experience.

From my perspective, I can empathise with the reasons behind it as follows. Well, at least, that's my belief!

1. The industry always has a request. To know the risks and necessary processes and regulations for cutting-edge techniques and new methodologies, they might need to apply or replace the old ones. For instance, modelling in the industry is indeed less rigorous than in academia. Still, it is also a more time-consuming and complicated process in the industry and differs from academic work focused only on scientific concerns. Why? Because allocating resources, logistics, time, and change management means the cost for the industry and a manager’s first question is, what if we do not do it? Conversely, for academia, the cost of testing and changing models is very low, and professors can do it daily to find the best possible one for their scientific publications.
2. Applying advanced models and machine learning to industry problems does not necessarily need high accuracy but needs high profit and performance. In calculation, we must consider all the costs and model risks the industry tolerates. The industry usually needs a good model builder who understands internal and external regulations, rules, laws, and ethical codes. For example, sustainability concerns, risk appetite, strategic plan and risk plan. Most of them are not a priority for academic research work.
3. Simplicity is another concern for the industry. In academia, you can combine different complex methods and show how your idea is glorying without paying a penny. However, an industry manager must present an understandable model covering all industry ethical concerns, regulations, and costs.
4. The focus of managers in industry and business sectors is on selling products and services, and the focus of professors is on research and teaching. So, selling is not a priority for academia, but it is for sure one of the first concerns of managers in business sectors.

So, data science, data mining, machine learning, or any other academic courses need different approaches and concepts to fit the industry’s needs, and the curriculum and teaching methods might need to be different according to industry sectors. High AUC and model accuracy measures are important performance measures for academic models. However, they do not necessarily represent a successful model for industry sectors. To create a tasty dish for business sectors, we need skillfully blend the art of industry experience and the precision of science in academia.

Previous posts:

• From Educators To Entrepreneurial Facilitators https://www.dhirubhai.net/posts/activity-7067485279015583745-_xsb?utm_source=share&utm_medium=member_desktop
• Blended Learning Award for promoting Excellence in Higher Education https://www.dhirubhai.net/posts/activity-7055289793890459648-IOxR?utm_source=share&utm_medium=member_desktop