@jessedo81My first job as a Data Analyst: Expectations vs RealityOne of the most important lessons of my career…Andres VourakisBlockedUnblockFollowFollowingApr 2After almost 5 years in school, graduation was finally around the corner and I was more than ready to put my skills to the test.
I wasn’t completely sure what job title I wanted, all I knew was that I enjoyed working with data and solving complex problems.
I later realized that I wanted to pursue a career in Data science, but that’s a story for another day.
Shortly after I graduated, I was offered a position as a Data Analyst at a local company.
It seemed to line up perfectly with my skills and my passion for working with data, so I accepted right away.
I could already picture myself sitting on my desk, doing sales forecasting and creating amazing reports and visualizations.
You could say that I was blinded by the euphoria of being finally pushed out into the real world and I never took a second to, slow down, breath, and “lower my expectations”.
My first couple of months as a Data Analyst involved very little data analysis and consisted mostly of long hours wrangling data.
These are some of the phrases I used more than once, that perfectly summarize those first few months.
“Where can I find that again?”Although I received training on the project I was being assigned to do, there was more than a lot to take in.
I found myself struggling to remember where every file lived and the different processes I needed to follow.
The lack of documentation available was a huge setback and I ended up having to rely on my co-workers to get around.
“That would take too long”One of the biggest challenges was not having access to the “right” infrastructure or tools to process and analyze large data sets.
Excel was used for almost all reporting company wide even though it couldn’t handle all of our needs.
Everyone in the company was demanding more and we simply couldn’t deliver.
“We shouldn’t be doing that manually”Some of our our day-to-day tasks involved downloading CSV files from different websites and refreshing Excel reports.
These were tasks that were wasting too much of our time and needed to be automated.
“We can’t the trust the data…yet”Since our data was collected from so many different sources, and reports were not always only handled by the data team, it was difficult to trust the accuracy of the figures that were constantly being thrown around at our company meetings.
My company was growing fast, and so was our need to rely on data to make decisions.
The problem was that our current infrastructure wasn’t meeting our needs, and there had been too many band-aid solutions along the way and the cracks were starting to show.
My team consisted of 3 Data Analysts (including myself) but, to some degree, we were also required to play the roles of data engineers and data scientists.
We simply didn’t have the budget to bring in more talent, and so it was up to us to lay the ground work to ensure we could meet our stakeholder’s expectations.
But despite my job not being what I expected (at least during those first few months), I learned more than I could have ever imagined.
I got a chance to experience what is really like to start a project from scratch and deal with different obstacles along the way, and believe me, there were many.
You see, in order to turn data into insights or predictions, there is a lot of work that goes into getting that data ready.
I remember learning that in school, but it never truly sunk in until I experienced it for myself.
When I was in school, I never really spent much time asking questions like “Can I trust the Data?” or “What is the most efficient way of storing data?”.
I was a lot more consumed with learning the exciting parts of data analysis, like, visualizations, predictions and so on.
All of the buzzwords you’ve probably seen all over the internet.
But this job, with all of its frustrations and set backs, taught me an incredibly important lesson that I have no doubt will be very valuable as I advance in my career:Always be willing to do the “dirty” workI don’t mean dirty as in, useless or unpleasant (although for some people it may be), I mean the kind of work that often gets pushed aside because it may not be as exciting as predicting the future, or as flashy as creating visualizations.
But make no mistake, doing the “dirty” work is an essential part of working with data.
No matter if you an aspiring Data Analyst or Data Scientist, if you’re willing to clean all the data, test it, write all the documentation, and clean up all the code, then you’ll always have a spot on a data team.
You’ll not only set your team up for success but also become someone they can rely on.
If you are curious about how my team and I tackled some of the obstacles I mentioned earlier, then check out this other article I wrote: When Excel isn’t enough: Using Python to clear your Data, automate Excel and much more…I hope you found this article useful.
if you have any questions or thoughts, I’ll be happy to read them in the comments :).