Marianna Imprialou, Director of Data and Analytics at EY, explores how the data science sector is set to shape different parts of our society
Q. How has the Data Science sector been impacted by the advent of GenAI?
I would say that right now the day-to-day life of a data scientist has not yet been greatly impacted by the ‘Big Bang’ event that is GenAI however we can see and predict that changes are coming, specifically as the technology becomes more widely available offering the potential to streamline certain processes.
Data scientists working mainly with textual data have certainly started extracting insights in a much easier and more comprehensive way already. The rest of us have benefited mainly from the ability to write code faster but we are still at an early stage and are yet to see exactly what those changes will look like in the future. Currently, there is a lot of training going on in this area and people are certainly keen to embed GenAI in their work.
Q. What Gen AI applications excite you most in terms of transforming society?
Applications that are related to knowledge sharing and digesting excite me the most. Of course, there are numerous commercial applications that can lead to commercial gains which are exciting however for me I think back to my past in academic research. In the past, the number of books, papers or other resources someone could read to conduct a literature review for example was limited. In the future, the sheer volume of that information will change, and people will have access to an amazingly huge knowledge base. In that regard, when it comes to the important aspects of society and science, there can be no excuses for not being more informed about specific topics. Obviously, this has its own dangers as information must be checked, verified and be accurate but I think and hope that this will be achieved over time.
Q. As demand for Data scientists increases how can we best develop these skills for the future?
Data scientists have always needed to be very good problem solvers, flexible thinkers and be able to adapt to any type of commonly used tool. Even before data science was even called data science people performing the data analysis work have always been quick to adapt, learn and problem-solve and that will never change.
Beyond these core skills and in a more practical sense I think that data scientists today should begin to focus their training on all these new GenAI tools and learn how to use them to their benefit. This means adopting the technology to enable them to do more efficient work and complete more meaningful tasks at speed.
The next generation of data scientists will therefore need to adapt to a large variety of tools and software and focus on having a good knowledge of more than one programming language, database querying as well as multiple cloud infrastructures in order to remain flexible and versatile in their roles.
Q. As Assistant professor at a leading UK university, how easy was it to transition from academia to industry?
For people who specifically wish to transition from academia to industry wanting to follow a data science path it’s more straightforward than in other sectors. While aspects of my role as Assistant Professor involved teaching Data Science and Statistical methods a larger part of my time was involved in managing my own research projects as well as managing people who were working on data science research projects. This part of my previous role was not dissimilar to a principal data scientist role in a team within the industry and I personally found the transition quite easy.
Typically, when a data scientist moves from academia to industry many will already have been exposed to and experienced a large and wide spectrum of technologies, methodologies, statistical models and algorithms across various sectors. Academia helps to develop technical skills and provides access to many tools aiding the transition into industry.
For those who have spent a long time working in academia, the transition can sometimes be a bit of a shock in terms of how fast things move in the industry however the satisfaction that you get from the real-world application of what you do is something that is extremely motivating and to be honest, kept me going even at the most difficult of times at the very start of my career within industry.
Q. In what is often perceived as a male-dominated industry, from the perspective of a woman and a mother, what has been your personal experience?
In a male-dominated sector like data in both academia and consultancy I can't say that there haven't been moments that I sensed, without having any proof of that, that because of my gender and sometimes even my age and gender in combination, certain people initially doubted or questioned my ability to perform specific tasks. Even today in a team when someone meets me, they might assume that from a technical perspective, I might be doing the ‘lighter’ tasks on a project. They might think that the person who is writing the code is not me and I’m simply managing it and that really isn’t the case.
In many ways, women do sometimes have to wait a bit longer and must prove themselves a bit more in the job to be trusted by our teams and our clients. I see this as a common theme and I’m not the only person to have experienced this. Equally, I have to say that I have always found a lot of support from colleagues, male and female, and typically this does not become a persistent problem.
When I was younger, I did not think too much about this and took my position within the sector for granted however as a mother of a young girl my perception on the subject has certainly changed. Ideally, I wouldn’t want this to be the experience for my daughter 20 years down the line and I feel it is my and our generation’s responsibility to erase this belief that women are less likely to be good at Maths, Physics or Coding. I think we need to encourage more girls to cultivate these skills but also to go into the workplace without feeling that they have to prove themselves more than others in order to succeed.
Today there are a lot of workplace initiatives, and many companies address this area in a positive way, so we are going in the right direction. For me personally, I feel a responsibility to work harder on encouraging others to feel more included, more so than I did at the beginning of my career.
This article appears in the Big Data edition of Sync NI magazine. To receive a free copy click here.
