Lessons from my first year as a data analyst

At the end of my last post, I mentioned I would write about LATERAL JOINs. I still plan on writing that post one day, but before that I want to write my first non-technical blog post about some career milestones of mine.

On Jan 11, 2022, I had my one-year anniversary working at DailyPay as a data analyst. It’s been a wild ride, in so many good ways. When I joined DailyPay, I was the third member of the data science team. Now I’m a member of a larger data team that’s 18 members strong and growing. The data team has four subteams (analytics, data science, analytics engineering, and data products), and I work in the analytics team. I’m incredibly lucky and grateful to work in this data team. I’ve been able to contribute an outsized amount to my team even though I’m junior: for example, in addition to working on high profile projects, I played a large role in developing the team’s culture and best practices. Every day I learn a ton from my teammates, and I feel like I could continue growing for a long time in this team.

My latest milestone is less than a year after I joined, in September 2021, I was promoted from data analyst to senior data analyst. And there’s a good chance if I continue to perform well that I could get promoted again in the fall of this year. My star is rising at DailyPay, and I’m going to ride this wave as long as I can.

In this post, I want to reflect on the valuable lessons I’ve learned in my first year. I arrived at DailyPay with close to 5 years of work experience, but working as a data analyst has taught me a lot of new professional lessons. I wish I could go into length on how I learned each of these lessons and what projects they applied to, but I will be keeping my explanations general in nature to protect the sensitive nature of my work. You’ll still get the main ideas, so here I go!

1. Give stakeholders what they need, not what they want: As a data analyst, it’s easy to get bombarded by data requests left and right. What’s important, though, is to resist the impulse to just read the request and start working on it. More times than not, requests are unclear, may ask you for way more data than is needed, or may not get at the root of the problem. My job as a data analyst is not to follow orders blindly, but to ask questions, truly understand what the stakeholder needs, and then clarify and re-interpret the request so that it tackles the heart of the problem. I then need to use my expertise to provide the best possible solution while weighing factors like time, efficiency, and simplicity.
2. Avoid working on TBU (true but useless) analyses: Have you ever started to work on something and wondered what’s the point of what you’re working on? This happens more often than you think in the data world where a stakeholder is asking you for data, but isn’t clear about why they need the data—when you ask them, they say it’s because they’re curious for XYZ reason or because they want to be able to explain something that has already occurred (i.e. an ex post facto analysis). If you figure out the answer to their question, it will usually yield a true but useless (TBU) analysis (this term was popularized in the well-known change management book Switch) because though the answer may be true, it isn’t important/relevant to driving a significant business outcome. Working on these TBU analyses takes you away from working on analyses that will move the needle for your business, so try to negotiate away these TBU analyses while educating your stakeholders on why it’s not the best use of your time.
3. Understand the why: Similar to the first two points above, it really helps to understand “the why” behind the data request. Many times, stakeholders just ask for data without providing a reason or context. Without knowing why, you don’t know what problem the stakeholder is trying to solve, and it may be that the data they’re asking for is not going to solve their problem. I make it a point to ask as many clarifying questions as I need to before I get started on a particular analysis.
4. Think long-term: Which do you think is a better solution? Creating one-off reports for stakeholders that answer the same question 5 different ways or creating a dashboard that answers these one-off questions in one fell swoop? It’s the latter. Your time is valuable. Be as efficient as possible in answering questions by creating scalable, long-term solutions like dashboards rather than answering one-off questions around the same topic for eternity.
5. Be proactive by solving problems you discover yourself: I think one of the main things that distinguishes a good data analyst from a great data analyst is how proactive they are in solving problems. It’s easy to solve problems given to you. That’s what most junior data analysts start doing. It’s more difficult, but much more rewarding, to solve problems that you discover yourself. These are the problems that people may not be aware of or maybe they’re aware of, but they’re not sure how large the problem is and so no one is working on solving that problem. As a data analyst, you have access to an incredible amount of company data AND you have the skills to analyze that data, two things that set you apart from most people at your company. That means by doing exploratory data analysis and thinking through possible avenues of analysis, you can find and solve problems that could add millions of dollars of value to your company. Better yet, because no one is breathing down your neck to solve the problem, you have more freedom and time to solve it.
6. Build relationships and get buy-in: At work, I only spend about 60% of my time doing actual analysis. The other 40% I spend talking with my data team colleagues and stakeholders so I can build my relationships with them, listen to what’s going on in their areas, and identify problems that I could help with. When you have strong relationships with the people you work with and they trust you, your work becomes infinitely easier because they have seen you do incredible work already and they understand who you are, how you do things, and how you communicate: you’ll put them at ease anytime you tell them “I’ll take this on. Let me get back to you.” Getting buy-in is intertwined with relationship building. When you are making recommendations to leadership based on your analyses, you’ll need to get their buy-in, and buy-in is much easier to get when you already have strong relationships with those very people. People skills on top of strong analytical skills is how you build influence as a data analyst.

I hope these valuable lessons I learned in my first year as a data analyst at DailyPay can help you on your data journey. Until next time!

Photo by Brandon Jacoby on Unsplash