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Data analytics in banking: what’s so cool about it?

Živilė Vajegaitė, the Lead Data Scientist/BA Chapter Lead at Danske Bank

Data analytics is definitely a noticeable field nowadays: the demand for professionals there is high; also, many aspects of business development or tech disruptions are closely associated with data-driven solutions exactly. It may seem that data analytics is just another trendy buzzword in tech, yet this is so far from the truth. As banking and finance is one of the key industries for smart analytics, we spoke to our colleague data scientist Živilė Vajegaitė, Chapter Lead in Retail Investment Tribe at Danske Bank. Having spent many years in this particular area and seen its quick development, Živilė has a lot to share!

You started your career as a trainee programmer in one of the largest pharmacy brands worldwide. Why did you decide to move to banking and finance? 
Decision to move from pharmaceuticals to finance was related to my relocation: I moved back to Lithuania from the UK and started looking for a job that involves mathematical modelling. The most suitable positions that I found were offered by several big international names in banking and finance exactly. Therefore, I gave it a shot and started working at Danske Bank. It’s been nearly 6 years and this job is still thrilling me daily!
I’ve never regretted moving to finance. To a certain level, both industries I’ve worked at really help people. One helps to stay healthy while another one helps to stay economically stable and make your dreams and aims come true. 
 
Why data analysis? What's the most attractive aspect of this field?
When choosing my undergraduate degree, I went for Applied Mathematics simply because I was good at Maths back in high school. At university, I found data modelling fascinating: instead of doing all hard calculations yourself, you just program a computer to do it for you. What is more, it does it for million data points in seconds. So it seemed absolutely great and disruptive!

For me, the most exciting part of data analytics is hypothesis testing. As a human, having very limited data storage, I may risk to draw completely opposite conclusions to what the actual reality is. Data analysis allows me to consider as much data as it is needed to draw the conclusions that are representative and accurate. 

A good example is guessing how many people own a car: while a student, you would probably guess a much lower percentage compared to a middle-aged adult. Data analytics simply reduces the bias and helps to form a realistic view.
 
You've been doing data analysis in banking and finance for a while. In your opinion, what changes have been the most important during the last few years?
Some time ago, the vast majority of strategic business decisions were made based on human observations and discussions with the experts in that particular field. Now, most strategic decisions are 100% data-driven.
In other words, biased guesses have been completely eliminated there. Once a business has an idea, it will be evaluated with the help of data analysis prior to any final approval of the strategic initiative. 

Traditional banking is competing with fintech startups quite extensively. How data analysis can help traditional banking to stand out?
In my view, fintech startups and traditional banks are mainly competing on specific and small retail products, such as credit cards. Beside that, such lending products and services as mortgages give a huge advantage to  traditional banks due to capital regulatory requirements and internal risk-based framework.
Both: banks & fintechs are using data analytics for the same purpose. It helps to produce custom-made and faster solutions for their customers. In other words, the smart use of data becomes an essential part of understanding the company’s customer base, identifying pain points and evaluating responses to implemented changes. 
 
As a data analyst in one of the largest commercial banks in the Nordics, what's your day-to-day routine in the workplace?
When we talk about any data analyst, we may imagine a typically geeky mathematician working on their computer all day. However, the reality is so different: today, an excellent data analyst is required to have a solid understanding of their business domain, good stakeholder management abilities and even have some IT skills. 

As a result, I would say that an ordinary day for a data analyst would be a mix of stakeholder management, data engineering work, actual analysis work and maintenance of automated analytic deliveries. Variety is surely the key here!

I noticed that some data analysts are shifting here from a completely different background (e.g., arts, humanities...). What do you need in order to succeed in this field?
Regardless of their background, junior data analysts should have a wide range of skills.  Of course, having any tech-related background is advantageous, but it also gives you a great kick-start if  you know specific business domain already.

When it comes to shifting data analysts, most of them usually complete short courses online or offline to make this career turn. It is really great that such a progress and change can be achieved in a few months only. However, most of these courses focus on the usage of tools and how-to steps while skipping such essential topics as the introduction to basic statistics. 

For an aspiring junior data analyst without any technical education, I would suggest going through some material on basic mathematics and statistics initially as well. Trust me, this helps a lot in providing trustworthy data-driven insights!