5 Things I Wish I Knew Before My First Job as a Data Analyst
I’m five months into my first Data Analyst job, and there are many surprises I wish someone had warned me about! I will list them below, so you are better prepared than I was.
5 things I wish I knew before starting my first Data Analyst job
1. If you’re the only tech person on the team, your coworkers may not understand what you do. This could be fine, or it could be disastrous.
During the job interview, gauge whether you would be the only tech person on your team. If you ARE the only tech person, this could be fine — if your coworkers understand that you are learning and there will be certain tasks outside of your realm of expertise, and your manager is fully present, ready to support you and take your side. However, I still would not recommend “going solo” for your first tech job.
Being the only tech person could be disastrous. You may be roped into every meeting and every project because the department wants you as the sole tech lead. You could be expected to do things outside the scope of your job, whether it’s not your subject of expertise, or it’s above your pay grade. Worst of all, you could be blamed whenever anything “tech” goes wrong, even if you did not set up the system, were not trained properly, or did not know it fell under your purview.
If you are solo, find support by identifying mentors on other teams, or developing online mentors (through LinkedIn or other platforms.) I’ve found the LinkedIn data analyst community, through Maven Analytics especially, to be very supportive.
In contrast, if you are part of a tech or data team, this lets you learn from tech people with more experience, and lean on them as mentors. Also, you can implement or use a ticketing system with the rest of your organization. If other staff members have a tech request, they can submit it via your ticketing system, and you can divvy up tasks with your tech team. This helps prevent burnout because no one person is tasked with all the data/tech requests.
2. You’ll spend a lot of time cleaning data.
In data analytics courses, you (mostly) analyze datasets that are already nice and clean.
As a Data Analyst, you analyze messy datasets — surveys from years ago where fields were not formatted properly, values are not correctly capitalized, and the Excel formulas result in errors. You may be asked to create data models for visualizations when none of your coworkers know the source of the data or how accurate it is.
To prepare: practice, practice, practice cleaning Excel spreadsheet data. Review articles and YouTube videos about cleaning data. Ask ChatGPT to generate dummy Excel spreadsheets with errors, and practice using Excel formulas to clean them up.
3. People won’t always be happy with your numbers.
Everyone in business says they want “data-driven insights.” What this means is that they want data points that support their arguments, make them look good, or help them sell their product.
Sometimes your coworkers or clients will not be happy with your numbers. They may ask you to calculate the average customer satisfaction rate, and then be upset when the number is not higher. It does not matter if you tell them to not shoot the messenger — they may still be disappointed, or search for any other metric to report on that will paint their product in a favorable light.
To prevent a “shoot the messenger” situation, establish clear specs with your boss before performing a data project — which values you’ll calculate, how you’ll calculate them, etc. Get your boss or relevant stakeholders to sign off on these specs and emphasize to them the importance of standardizing reports for accurate year-over-year analysis. For example, if you are reporting on averages that you calculate differently every year, you won’t be able to tell if the organization is doing better, or worse.
4. The job description could be very different from what you end up doing day-to-day.
When I interviewed for my first Data Analyst role, the job description said I would be using SQL and Microsoft Power BI. Day-to-day, however, I mainly use Excel to answer ad-hoc data requests and perform administrative tasks that do not require a tech background.
This is more likely if #1 is true — if you’re the only tech person on your team. If there was a gap in time between you and the previous tech person, there will be a backlog of admin/tech tasks assigned to you.
This could be fine — if you are still learning, working on projects you find stimulating, and keeping track of any metrics for your resume. It could also be bewildering and disappointing if you thought you would gain certain advanced tech skills but are working in Excel every day instead.
5. Just because there are other tech people on the team, does not mean they have the time or expertise to train you.
To figure this out, these are questions to ask during the interview:
- “Are there other tech people in this team/this department/this organization?”
If yes:
- “What are their everyday tasks?”
- “Would their scope of work overlap with mine?”
- “How did the previous person in my role interact with them? How often did they meet?”
- “Will they lead my onboarding and get me up-to-speed on how to use these platforms?”
- “Is there an opportunity to be mentored in this role? How can I find a mentor in this organization?”
In sum: Google, ChatGPT, Stack Overflow, and Reddit will be your best friends. This is true of any tech job! So get comfortable Googling.
I’m the only tech person on my team, and it’s my first tech role. I’ve learned a lot, but it’s overwhelming trying to find time for training, figure out how to run proper analysis, and figure out the validity of the data sources without someone there to guide me (at least a little bit!)
In any tech job, there will be curveballs. You are the designated “smart person” on the team. This means you are the “master Googler.” You will not necessarily know more than anyone else, but you have the tenacity to Google until you figure it out!
