Selvin: My Data Analyst Career Journey

Hi Selvin, thanks so much for taking the time. Please introduce yourself to our readers.

What’s up everyone! My name is Selvin Quire, and I’m a Data Analyst at Wintrust Financial Corporation in the Chicagoland area. This is my first data analyst role that I’ve been in for almost a year. While I’m relatively new in my data career, I’m supported by a background that consists of 9 years in higher education (academic advising, financial aid, and admissions) and 3 years in financial services. I’m also one of the co-founders and current CEO of Inquire Higher, a nonprofit that supports the success of Black students through college and career preparation and networking resources.

Can you share with us how you entered the data analytics industry, and tell us about your first data analyst role?

Within the last couple of years of my higher education career, I began thinking about how to transition out of the industry. While I loved advising students and helping them navigate college, I was not happy with the amount of opportunity in my career relative to my years of experience. My challenge was finding a way to transition where I did not have to sacrifice time (additional education) and money (potential salary decrease).

With the help of some deep thought about my transferrable skills and reconnecting with a friend from my hometown, I started working for a financial services company within one of their investment departments. While this role did not start my data career, it gave me my first exposure to data analytics. I worked within a large team that helped open investment accounts for the company’s customers, and within that team was a data analyst who provided analytical support for its account production. After seeing a dashboard created to provide the managers with insights on productivity, I was hooked!

I spent the next two years finding opportunities to shadow analysts, work alongside them, work data-related tasks into my duties, take on additional duties outside of my primary roles, and network with other data professionals in the company. As I continued to learn what was possible in the world of data, I finally built the courage to start crafting my own portfolio. My limited experience in Microsoft Excel and growing knowledge of the company and financial services industry became my best tools as an aspiring data analyst. After getting my portfolio to a point of satisfaction, I landed my first data analyst role after four months of applying.

What I do as an analyst today is gather insights from the loan and investment data of several banks to understand how well they are reinvesting in their surrounding communities. I help compile this data monthly that helps many levels within the banks and the corporate office that supports them make decisions. It also serves as a guide to show how the banks may score on their regulatory exams.

What is it that you're personally finding most exciting about being a data analyst?

I would say there are three parts about my role that I genuinely enjoy:

a) I like to take time to get lost in the details of the data. The process of exploring the data gets me excited about the unknowns that are waiting to be discovered.

b) Gathering insights is exciting because it’s like finding pieces to a puzzle without knowing what picture you’ll end with.

c) Presenting your findings to your stakeholders provides the challenge of translating technical information to an audience in a way they can understand. I believe that is one of my strongest professional traits, so building this skill is fulfilling for me.

In your current data analyst role, you’re working in banking, could you tell us a bit more about what your day to day looks like, and share how what you do drives insights and business decisions in a banking organisation?

To put this in context of our monthly report, I start with gathering the necessary data from a variety of sources within and outside of the company. Then, I help transform the data to a format that fits within our report. In between report submissions, I work with our program officers, members on our team who consult the banks on their activity progress. When we have some downtime, I am getting myself more familiar with regulations by reviewing them and asking more experienced employees questions. This last point is one of the things I do to drive insights. I must know enough about the rules and industry to know what my stakeholders would be looking for. This also helps me catch any data that appears to be unusual.

How did you start improving your skillset as a data analyst? What are you currently doing to keep up with the ever changing data analytics industry?

The easiest way I improve my skill set is on-the-job practice. Simply doing my job and thinking of ways to complete a task more efficiently after grasping it has proven to be valuable. Asking my colleagues questions is another way I improve as an analyst. The act of asking questions improves my communication skills. If it’s something more technical, I’m usually reaching out to a more skilled analyst on how they solved a particular problem.

Another way I improve my skills is finding ways to apply what I learn to aspects of my life outside of my job. For example, my son has a huge toy monster truck collection. Since the end of 2022, I’ve been keeping track of them in a spreadsheet. I keep track of the trucks’ names, colors, series, brands, etc. If I ever want to analyze his collection or create a database out of it for fun, I have his data organized and cleaned in a way that would make either of these tasks easy to do.

As far as keeping up with the data analytics industry, LinkedIn has been my primary source. I go about this by following other data professionals who regularly make posts. That’s followed by consuming their content on a daily basis. I’ve found this helpful because I’m constantly exposing myself to data-related information. If it isn’t news I’m making myself aware of, it’s a best practice or concept that I’ll retain simply from the constant exposure. This method of consumption also allows me to connect concepts together, even it’s well after I initially consume them.

Can you share what factors are most important to you in a data-focused career and why? Do you have a vision of what you'd like your next career growth to look like?

It boils down to three factors that I value the most. The first is curiosity. A large part of the data analyst role is asking questions. You could be asking stakeholders about the company’s business requirements to help you collect the right data. You could be asking the database you work with questions in the form of SQL queries. Knowing that there are many ways to solve the same problem, you could also be asking other analysts how they analyzed something or built a dashboard. Exercising curiosity daily has been helpful in my career.

The second factor is constant and focused learning. The vast number of new technologies and features within existing technologies that aid an analyst in their work keeps the field exciting but can be overwhelming as well. Maintaining the mindset that there is always something new to learn or a more efficient way of doing a task will keep your skills sharp. With that being said, it’s easy to get too excited by jumping from course to course or technology to technology. I’ve done this before, and it resulted in scratching the surface with a lot of concepts without gaining deeper knowledge in any of them. Also, the number of unfinished projects began to grow in the process. With any learning you plan to embark on, have a plan and stay on course until you reach whatever goal you established. Once you’ve reached that small level of success, rinse and repeat.

The third factor is business acumen. Understanding how your company or office works will direct you to applying the best methods to your work. Some companies or industries may have nuances which may require a more creative or flexible way to collect and analyze their data. Building this non-technical skill and using it as foundation for the technical work itself is another recipe for success in my book.

Something that a lot of people are wondering and asking about - What recommendations would you give to someone who is looking to join the data industry and get their first full-time data analyst position?

I would say the first step is to confirm why you want to become a data analyst. Two starting questions someone can ask themselves are, “Beyond the income potential, what excites you about being a data analyst?” and “Can a data analyst career lead into future opportunities that align with your goals?” You don’t have to know every single aspect about a data analyst role before deciding, but being able to confidently answer those two questions is a good start.

Next, I recommend connecting with data professionals at your current workplace. These are great individuals to learn from as they have first-hand experience they can share with you. That information may also be easy to understand as it’s in context of the company you’re familiar with. If you don’t have access to data professionals where you work, turning to friends and family to see if they know anyone you can connect with is an option. Reaching out to people on LinkedIn is a place to start as well. As intimidating as it can be to message someone whom you don’t know, most data professionals I’ve connected with in this way are more than happy to share information if they see a genuine message with the connection request.

Another thought is to determine if you already have skills you can build from. I come from a non-technical background, and the closest technical skill I had when starting out was Excel. Even when I figured out that I wanted to pursue a data analyst career, I had a very beginner-level skills in Excel. However, it was something I could build from. Once I started exposing myself to LinkedIn or YouTube content on how Excel can be used as an analytical tool, I began seeing what was possible and the software became less intimidating. This confidence grew as I started doing hands-on work and projects on my own. Like with a lot of journeys, starting is half of the battle.

Once you do get started, focusing on one area or course at a time is key as I’ve mentioned before. As you learn about the possibilities and different routes you can take as a data analyst, the desire to try out so many tools will increase. I highly recommend not only putting any technologies you want to learn to the side, but also ordering them in a way that make sense with your learning goals. For example, starting with Excel allowed me to understand how to organize data into tables and build charts from Pivot Tables. Those concepts and the common ground of Power Query made a smooth transition into learning Power BI. Separately, getting used to using functions and the tabular structure of data made learning SQL and relational databases easier than jumping straight into it.

Lastly, I would recommend creating a portfolio that has a mix of “standard” and “passion” projects. What I mean by standard is using datasets or answering business questions that you could typically see in the workplace such sales data from a restaurant or figuring out the most popular songs on a streaming service. Passion projects could include data from something that you enjoy outside of your career. I believe this helps with learning data concepts as it’s tied to a topic you’re passionate about. For example, let’s say you’re an avid video game player, specifically role-playing games. You could find (or create) a dataset that shows all of the RPG games and the number of sales they’ve has for each console over a certain period of time. You can come up with a question or hypothesis, play around with the data, and analyze it to develop your own insights. Putting these kinds of projects into one portfolio will show your data knowledge, ability to learn, and your personality and professional branding to help you stand out.

Anything you'd like to highlight, or add? Something that not specified above but you hear a lot? Or would be helpful for people to know?

Working to become a data analyst will expose you to different career paths within the profession. As you begin learning, identify which area(s) you genuinely enjoy (i.e. visualizations, database management, exploratory analysis, communication with stakeholders, etc.). These could be the area(s) where dive deeper and develop expertise that’s supported by your foundational knowledge.

An extra one: How do you see the increased availability of AI tools such as ChatGPT, Bard etc, impacting the typical data analyst role? Are you using AI tools to augment your thinking, analysis and overall work?

While I’m not currently using AI tools in my current work, here’s how I have seen it impact others in the analytics space:

a) Allows for faster workflows. Simple analyst tasks can be aided with AI, giving the analyst more time and capacity for lower level or more complex tasks.

b) Increased importance of skill demonstration. I don’t think AI will phase out data analyst role because there are components of the role that needs a human to manage. The non-technical skills of asking questions to get the right business context and speaking both technical and business languages will become more essential to demonstrate as AI continues to develop and improve.

c) Heightened awareness of data security. Companies are becoming more aware of the help and harm AI tools like chatbots can do to them and their data if not used responsibly, so analysts should also become more aware of their responsibilities as it related to data security and using AI tools.

This has been great, thank you again, Selvin. Where could people connect with you?

LinkedIn. My personal LinkedIn page. Feel free to connect with me here!

Data analysis portfolio. A bit outdated, but this is the portfolio I put together when I started seeking my first data analyst job.

Github. I’m new to Github, so it’s a gradual start. I hope to include more coding and projects here in the future.

Inquire Higher. My nonprofit’s website. Feel free to visit and connect with me if you have any questions about it!