My entry into the world of information technology came by happenstance. While growing up abroad in Asia, I was constantly exposed to new technologies, new ways of doing things and combining long-standing traditions with a modern approach. Nevertheless, the focus of my academic was mainly politics and cultural anthropology, and through a variety of internships and enriching experiences, I entered the world of IT strategy consulting for a worldwide leader in technology research, Forrester Research. That exposure to the world of IT strategy consulting led to ongoing interest and investment in topics such as data strategy and Artificial Intelligence – which in turn led me to Frankfurt School of Finance & Management.
One of the earliest and most effective skills you learn in the world of consulting is how to manage your own time under pressure and how to communicate effectively – not only for the benefit of yourself but also thinking one step further, to the collective benefit of your clients and colleagues. Both of those skills were vital in approaching not only the decision to apply but going through with my Master in Data Analytics & Management – as most of the studying for exams, group projects and the overarching Data Science project happens in a group environment. During that time, students are expected to present, make decisions, work on concepts, code and decide on the next steps, all of which are governed by effective communication and time management.
As with anything in life, you only get what you put in. From my point of view, that means a full-time commitment during the block week and setting time aside afterwards to work on group projects and study. While that requires discipline, hard work and commitment, one would be remiss to not remember how fun technology and leading technological change can be, from deep diving into data management and machine learning to leading innovation initiatives and managing the impact that technological changes have on the organisation. It is also important to not only see each block week as a siloed exercise but rather to have an overarching view of how each element can contribute to future interactions and decisions with colleagues, clients and other companies, and keep an open mind about what your contribution to that interaction can be.
The application of data analytics in any enterprise is not one-dimensional given the viewpoint of the applicant – meaning it is not just about programming, fancy strategic slide decks, buzzwords and long meetings. It is about how data, in any format, can help influence future decision-making while also not forgetting the impact it has on culture, customer and employee experience.
What was an additional added value during each block week and what Frankfurt School of Finance & Management placed an emphasis on is the concept of cross-functional learning and decision-making during each session. For me personally, that meant taking part in everybody’s way of thinking, background and decision-making process, and as a group applying that collective intelligence to business problems and challenges of the future. That, of course, does not mean that everything is rosy and everybody will agree, interact or have the same viewpoint – but that it resembles the real world, opens your eyes to new possibilities and ultimately helps you deal with problems, be it in machine learning, data strategy or people management more effectively.
The applied knowledge I learned at Frankfurt School of Finance & Management helped me to become more open to new opportunities in the space of machine learning and apply those learning. Currently, I am an Engagement Manager at a Start-up called DataSpark* that advises and provides clients with custom machine learning, automation and data engineering solutions, and I look forward to seeing what those new challenges and experiences might bring.