What not to do when starting out as a Data Analyst
Moving into Data Analytics
If you have an analytical mind without a technical university degree like me, you might be thinking about moving into Data Analytics from a non-technical job. For a long time, I was convinced I needed to go back to college to make the transition but thanks to online resources and very supportive managers I moved into an analytics role without it. This comes with its own challenges and many failures along the way, I hope to save you from some of these by showing you what mine were when I started out.
Learning from my failures
Making mistakes, when you start in a brand-new field, is inevitable and I made many of them in my years on the job. From every analyst’s nightmare of sending the wrong numbers to hundreds of people to unknowingly wasting thousands of euros due to inefficient code, I’ve been there. The good news is that I’ve watched my colleagues go through the same process, in new fields like ours learning by doing is inevitable. Especially as the beginning of my data analytics journey brought some challenges that I wish I had avoided.
Studying the theory
On my first day on the job I was confident and felt very well prepared. I’d spent the last two months taking online courses and learning all about how to create tables and fill them with data. It only took me a few hours to realize that all this time was not very well invested. I hadn’t considered that I will be working with already filled datasets with millions of rows, which meant that all my hard work studying how to deal with data did not apply to the big data my company was dealing with. I was absolutely clueless about how to work with data in practice, even though I had aced all the theory.
This taught me that in Data Analytics the theory will only get you so far. Looking back now I should have studied the basics and then jumped into practical examples right away. This also goes for mathematics and statistics, these skills are useful, and you probably have to catch up on them eventually, but I didn’t need a deep level understanding of them to get started.
Getting intimidated by the language
Over my first few weeks I had many moments when I felt absolutely out of my depth and wondered if I truly belonged in this position. What I called SQL my colleagues called Sequel, what I referred to as ‘the thing that automates things’ my colleagues used technical names for. I thought I could never catch up until I realized that much of the confusing language is company-specific and people in Tech like to use unnecessary technical descriptions to show their knowledge. Now, 3 years into my data analytics career and having experienced different companies, I see that there will always be technical language I won’t understand and that doesn’t determine how good I am at my job, or how much I know about a subject. Try asking your colleague what the terms mean, chances are they have no idea themselves.
Underestimating your experience
When looking for a new job after having worked in Analytics for a year, I made the mistake of underselling myself. In my eyes, I had one year’s experience and the former 3 years of working in HR and Sales were pretty much wasted time. This led me to apply for jobs that were only related to my current role by the coding languages I used.
I soon realized that I was not technically prepared for the tests in the interviews and I got rejection after rejection. Months go by and I was convinced I had nothing to offer due to my lack of data analytics experience. Until one day I get a message from my current employer about a role I wouldn’t have considered as it needed several years of experience. They showed me that my years selling to the same customers they sell to and the time in HR when I actively used their product are experiences they value just as much as the code I wrote. Data Analysts are not working in a vacuum and we are often much closer to the sales teams than other technical teams. I realized that I didn’t have one year of experience, I had many. Moving within your industry or analyzing products you’ve used before can put you years ahead of where you would be if you only look for a match in your analytics skills.
In the end, when starting something new you won’t be able to avoid making mistakes but if you’re prepared by working on practical examples, don’t get held back by your colleagues’ technical language and show off all your experience you’re already three steps ahead of me.
One practical advice I can give is to learn to write clean and efficient code. As boring as it sounds it will make you the most liked person on the team. In my first years, I had the ‘if it works it doesn’t matter how it looks’ mentality and I curse my past self for those decisions. It is incredible how fast you can forget what your code was about and why you did something, and it is frustrating to have to ask a co-worker multiple times to explain their code because they haven’t structured it well. So, if you do want to learn one thing ahead of starting your job – make it clean code writing. You’ll thank yourself for it down the line.