Written on 05 March 2021.
For the release of our brand new EDHEC Online MSc in Data Management & Business Analytics, we decided to speak to Tristan-Pierre Maury, the Academic Director of the programme and Economics Professor at EDHEC.
We took the opportunity to go over the notion of big data and its use in business today.
TPM: I have been a teacher at EDHEC for 13 years, now. I taught at ESSEC before that, and I have also worked at the Bank of France. I mainly teach courses about economics and data at EDHEC.
I also give lectures in undergraduate economics courses at the University of Lille as part of the EDHEC global MBA in data analysis at the Nice and Paris campuses. I give classes for the MSc International Business degree too, which focuses on applying economics to business.
The disciplines of data and economics are constantly moving closer to one another and they sometimes even overlap. There is a real opportunity for future generations to learn to make the most of the former to supplement the latter.
TPM: Big data is a bit of an umbrella term today which is still relatively unclear in the collective subconsciousness. I think it is important to be able to differentiate between all the big concepts linked to data today, such as big data, data analysis, business intelligence and machine learning.
Big data, firstly, is related to very large volumes of data which are typically being continuously collected. As the volumes are so large, there are techniques to process and interpret the data, known as the domain of big data.
Data analysis refers to using digital data as efficiently as possible. This discipline, which consists of making sense of mass data, is used all over in very different sectors of activity.
Then we have the concept of machine learning, which is still in the field of data analysis. Machine learning involves training the algorithm responsible for processing large volumes of collected data to refine its predictions and data interpretation skills. For example, if the sales teams at a company wanted to make predictions based on past information, machine learning would consist of feeding this data into the algorithm. Once the data has been correctly integrated into the initial model built, the algorithm will gradually become trained, as it is constantly being fed data. It will intelligently refine its model and its predictions will become ever more accurate.
Finally, we have business intelligence, which I think is the vaguest term out of all of these ideas, but which is still important to be able to understand. When algorithm models to interpret and process data have been implemented, the moment of the results arrives. Business intelligence completes the interpretation process of big data, as it consists of transforming the data into a clear and understandable model for decision-making. This discipline is used after heavy data processing. We can see it as the final layer of information processing, which is mainly based on visualising and simplifying complex data. This is all with a view to helping corporate decision-makers to make informed economic and strategic choices.
TPM: Many businesses today are sitting on a treasure trove of information, benefiting from phenomenal resources, amounts of data and information. Nevertheless, several sectors are still lagging behind when it comes to integrating data analysis into their strategies,
with some sectors being a lot more advanced than others. I am thinking specifically of marketing, which is a real pioneer sector in data usage. Many marketing teams make decisions based on quantified digital information drawn from their user databases. This is an intelligent way to create sales pitches and actions adapted to the behaviour and expectations of their potential customers!
Nevertheless, there are still a large number of sectors undergoing transformation:
We also need to know that not all companies are alike in this regard, as data is private in some industries and rare in others. Everything is compartmentalised in the sector of defence, for example, making it difficult to set up rich and varied data panels!
Furthermore, young companies also experience more difficulties. More established companies have access to their data history and can process and directly benefit from it. However, access to data is typically more difficult when you arrive on the market (it is conditioned by your ability to pay companies who provide this type of data and services, and young companies often lack resources).
Age and maturity thus play a significant role for companies in the efficient use of data. Younger companies must learn to seek funding to access data in order to gain a competitive advantage.
TPM: There are several professions in data and I am sure a lot more will appear! The two main roles we prepare our students for in the MSc in Data Management & Business Analytics are Data Scientist and Manager.
Firstly, Data Scientists are responsible for constructing models to process the data collected and fuelling the decision-making process at their company. I think this is one of the big professions of the future for company digitalisation.
Then we have Managers. The MSc does not prepare for management in the broader sense of the term, but it helps managers who are not specifically trained in data to understand what the operational staff in their teams do. The role of the Master’s degree is to bring everyone up to the same level of understanding and language by providing all students with precise elements of comprehension.
The EDHEC Online courses take place entirely online and are intended to focus on business practice with case studies. This Master of Science is both complete and non-specialist, as it focuses on all of these potential aspects of data processing.
The course includes both compulsory core modules and optional modules that students are free to choose according to their interests:
If you would like to apply, do not hesitate to visit the dedicated programme page directly!
*churn rate: This term is used to refer to the loss of clients or subscribers. In human resources, it refers to the amount of employees that leave a company.