Hello Nevena, thank you very much for being here, it is a real pleasure to have the opportunity to have a chat with you and get some insights on one of the biggest FinTech companies in the World. We would like to kick off by getting to know you better, how did your journey in Bloomberg begin?
I understood the power of data during my MSc studies and started considering a career in that sector. Bloomberg is a leader in the financial data industry and was a natural career choice. I joined the Global Data department as a Capital Structure Analyst, focusing on researching some of the largest companies in the energy industry in EMEA. After three years in that role, I moved to be the Team Leader for the same team. Then late last year I moved to the role of Team Leader to cover Structured Products which are also part of the Fixed Income space. What I realised over the past few years is that Bloomberg gives you the opportunity to pursue high-profile initiatives very early in your career and gives you the ability to see the impact of your work on the product and clients.
Would you give us a quick introduction on what the Global Data department at Bloomberg does. What are your tasks on a daily basis? Do you usually have a routine or is it different every day?
The Global Data department is in charge of sourcing and acquiring all the data for the Bloomberg Terminal and enterprise products. The work and the routine inside the department itself can however vary significantly according to the type of data that is being acquired.
In general, an analyst’s typical day starts by understanding what is happening in the market, ensuring that our processes for acquiring and processing the data are running smoothly, ensuring high level of customer support, and working on various projects to capture new data points or increase the timeliness and quality of our data.
As a Team Leader I am involved in supporting my team in their tasks, as well as providing opportunities to develop the skills needed to perform in their role and reach their career goals.
How does the data collection process take place? What are the most important technologies that you leverage?
This depends a lot on the type of data that we are dealing with, as some is easier to acquire than others. For publicly available data we can utilize technologies like web crawlers or APIs while for private data, we first need to to establish relationships with market-players to source it before applying any kind of technology for processing this data.
After the procurement and acquisition, we run quality checks and, to do that, we use business rules or algorithms to identify potential errors. At this stage, manual data checks are also very important. For certain types of data, such as time series data, it is easier to identify outliers. For other more complex datasets we need the help of other technologies such as machine learning to help us ensure the quality of our data.
Within my team specifically, which deals with structured products, data is provided to us
directly via feeds and then automatically processed and created on the Terminal.
Data from heterogeneous sources are increasingly used to analyze complex phenomena. What do you think about alternative data such as social networks and satellite images to predict financial market trends?
The demand for alternative data has soared over the past year and we had to be very quick and agile to be able to satisfy clients.
Let’s take an example: this year everybody was interested in various statistics related to Covid-19 and on how it was affecting people’s habits. In order to provide additional value to our clients we acquired data from Open Table (a popular table booking app) to try to understand people’s sentiment and see how confident they were about going out.
However, we need to understand what this data tells us exactly: for instance we did not know how much people were spending and how well restaurants were doing. I like to say that alternative data is an valuable supplementary dataset, but should be interpreted carefully in conjuction with other data.
How is the shift towards sustainable finance changing data collection? Have you seen an increasing request of ESG data in the last year?
This year we reached a cumulative 1 trillion dollars green bond issuance and the wider sustainability linked debt market increased to over $ 2 trillion. Bloomberg coverage of green related securities only this year increased signifcantly, in line with the overall market increase. So yes, ESG and sustainability is definitely something that our clients are very interested in.
What are the markets where It is harder to collect data? Why?
There are several reasons why iit might be difficult to collect data. For instance, certain markets still rely on outdated technologies such as faxes or sometimes do not have any digital copies of their documents.This is especially true for certain developing countries where Bloomberg has to work closely with local partners in order to acquire data.
Moreover, when there is a lack of transparency in data and governance, it is more difficult to convince local players of the benefits of transparency and persuade them to provide data to Bloomberg. This is particularly true in some frontier and less developed economies.
What is the relationship between human contribution and the technology that you use during the data collection?
In the past manual data collection was the industry standard. Now, the advancement of technology represents a significant opportunity to optimise the process and our analysts to develop their skills in order to be up to date. Daily routines and responsibilities have thus evolved and now the focus is to improve, develop and optimize the collection of data in the fastest and most efficient way possible using the latest technology.
Have you noticed any difference in terms of availability of data and demand for data in a time of crisis like the Covid-19 pandemic?
Definitely. Significant events, such as the current pandemic, caused a spike in the demand for new types of data. In these cases, we need to be agile and quickly acquire the data that the clients are looking for. Generally, data is available and the difficulty lays in the ability to to differentiate good vs bad quality data. The other challenge is using the data well: there are huge quantities of data available in the market but we need to help our clients get valuable insight out of it.
What role does Bloomberg play in the FinTech revolution? Aside from being one of the biggest FinTech companies worldwide, how do you collaborate with this evolving ecosystem and its cutting-edge companies?
Bloomberg prides itself on keeping the innovative start-up mindset throughout the company even after becoming a mature industry leader in financial data. We are constantly aware of the latest technologies and developments in the industry. This means that we also need to keep sourcing the data, processing it and building tools for our clients to analyze data in the best way we can. We take calculated risks while ensuring that this doesn’t compromise the successful business that we have. These are probably the primary differences between us and other companies that work in the same sector: the focus on sustainability and long-term growth of the company.
Where is the future of data analysis and forecasting heading?
Development and technology allow us to analyze huge quantities of data more easily than ever before and the key to getting the most out of the data is our ability to interpret and link different datasets.
As an example, Bloomberg now offers a python-based tool that integrates with the Terminal and gives our clients the possibility to analyze, interpret and draw conclusions by weighting the immense amount of data present on the Terminal.
Our goal in Global Data is to provide the data without giving advice or recommendations, but at the same time make it as easy as possible for clients to draw a conclusion out of that data.
This chat that we had gave us numerous insights and room to think. What advice would you give to our readers about their career? Is there a right path to follow?
I don’t think there is one unique path that is certain to be the right one. I believe the key is to develop your skills and expand your knowledge and then pursue various opportunities available to you. The job market has changed so much over the past year that there is no ‘’one’’ unique model to be successful in your career. Therefore, what I would advise is that you accept the opportunities that come your way and as long as you keep developing yourself and your skills, you will have a successful career.
Bocconi Students Fintech Society