Hissu Hyvärinen is part of Vincit’s AI Works team. She’s a champion of data science and is especially passionate about text analytics. In her free time, Hissu enjoys badminton, climbing, and playing the bass in Vincit’s own house band. Her work at Vincit involves all kinds of unique customer projects that always have something to do with data.
How I became a data scientist
I’ve always been interested in languages – their structure and the ways they can be used to express things. After upper secondary school, I wasn’t yet sure what I wanted to do with my life but studying language technology seemed like an interesting place to start. I learned about how machines can be taught to understand human language, either spoken or written. After finishing my Master’s degree, I decided to give it a go and applied to a PhD program on Information Systems at the IT University of Copenhagen. A local research group was already working on a project related to social media, and it felt like a good place to direct all my interest in analyzing language data. In my doctoral dissertation, I study the ways in which topics of discussion and expressions of emotion develop in social media in different areas after a terror attack. My research helped me understand how useful it can be to process data in a smart way. In a crisis situation, it can even save lives.
What’s so interesting about data science?
In today’s world, we’ve reached a point where the importance of collecting data is acknowledged, and we know how it should be done. However, it’s not always clear why the data is collected and what we should do with it. There’s an endless amount of data and information available, but there’s still a lot to learn about using it. In my work, I’m particularly interested in the ways we can increase our understanding and ability to make good decisions based on smart use of data. What can we make better, more efficient, and easier by using data? For example, how can more efficient processes and use of resources support sustainable development? I see a lot of potential for very creative problem-solving in data science.
How can we create a better future through data?
It’s not easy to predict the future because it’s not always clear what kinds of technological innovations will be available to us. Big breakthroughs are rare – more often it’s the case that significant change happens through the combined impact of many small improvements. Just like in research, improving something through data means small ideas that eventually expand our understanding of the big picture.
However, I firmly believe that better information will help improve our wellbeing both now and in the future – not just at the level of decision-making but in small, everyday things. The more we know, the less we’re affected by the fear and uncertainly of not knowing. For example, if we can use statistics to find out what the biggest sources of carbon dioxide emissions are, how these have developed over the years, and how they’re dispersed geographically, we can make more informed decisions on how to reduce emissions. Or if we know more about how pandemics spread, we can make more informed society-wide decisions on which precautions and restrictions will genuinely be the most effective. Naturally, the availability of information as such doesn’t guarantee good decision-making, but the decisions we make tend to be better if we at least have the option of using information to support them. I find it important that, in my current job, I get to work to improve our future through tackling such significant issues.
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