The Accidental Data Analyst

I wanted to be a project manager/web developer


The few people I’ve told about my journey into data analysis have found it strange, and at the same time amusing. And the truth is I never thought I’ll be in the business of data analysis given the fact that I’d always hated anything that had to do with calculation or mathematics, mostly because our relationship just wasn’t working (LOL). I was indifferent about statistics on the other hand because it really did not stress me, neither did it excite me. In fact, right until I got my job in a somewhat data analysis role, I was training, studying and gearing up for any role in project management, and at the same time involved in an online web development boot-camp.

Just a few weeks before I resumed in my data analyst role, I wrote and passed my PRINCE2 certification in hope and preparation for a role in project management. I was really just all over the place. I had been without a job for almost a year so I was trying my hands at different things, learning and improving on different skills just to up my advantage. But in all of these, data analysis was not one of them.

Up until the summer of 2017 during a 3-month internship program as part of my master's degree program, I’d never really used Microsoft Excel to do any major data analysis. The most I’d done until that point was organize data into tables, perform simple addition and subtractions and create basic charts. It was during the internship I went further in the use of the tools by using simple pivot tables to analyze and summarize a fairly large amount of data. Even at that point, the use of formulas was difficult to me. Apart from the basic formulas such as sum, average, etc, I did not know any other. I had no idea how more complex formulas like Vlookup, If, Index & Match, etc, worked (complex to me at the time).

During the series of interviews, I did for the data analyst role, one question that kept coming up was how good I was in the use of Microsoft Excel, particularly if I could use the Lookup functions proficiently. Although I had a basic knowledge of the use of the tool, I had no idea how these functions worked, my default answer to the question, however, was “yes”, after which I’d go back home to study and practice these functions or formulas. Luckily for me, I was never asked to do any onsite test using these functions (although I strongly believe I would have passed, LOL). The phrase that kept coming to my head whenever these questions came up was the one by Richard Branson that reads;

“if somebody offers you an amazing opportunity that you’re not sure you can do, say yes-then learn how to do it later”.

The journey so far

On my first day (which was basically getting acquainted with my team), my manager asked me once again if I could use the Lookup functions, after explaining more about the role and tasks I’ll be performing. I’m sure you already know my answer, lol. At the end of the first, I went back home as usual to practice the functions. Thirty minutes into my second day and my manager gave me a report to prepare using Vlookup as it involved primarily, aggregating data from different tables. Of course, I had little difficulty preparing the report given it was my first implementation of the formula on a real dataset without actual time pressure attached.

From then on it has been a story of consistent and gradual improvement. At first, I didn’t see myself as a data analyst as it wasn’t contained in my job title. However, as my manager continued to give me tasks that involved analyzing data and writing periodic reports, I began to identify as such. My next few months were spent getting better at using Vlookup (never really had the course to use Hlookup). Then I graduated to using logical functions such as IF, IFNA, IFERROR, etc. In hindsight, these consistent improvements all compounded into my current skill level within the space of 2 months or less. In other words, I was putting in little improvements everywhere for more than 8 months which seemed like I was improving at all, and then “suddenly” I became really good at using Excel. This is further evidence that the best way to learn a skill is to make little but consistent improvement every day and let compound interest do its work.

The two biggest lessons I’ll say I have learned so far however would be firstly, always believing there’s a more efficient way to do any particular task/report. This line of thought has led me to into learning more tools necessary for the efficient delivery of my job. For instance, data cleaning for many of my reports became too draining as I had to do it manually, every time. The belief that this could be made more efficient thereby increasing my productivity led me into Microsoft Access, Excel Power Query, and Power Pivot. With these tools, I was able to automate my data cleaning and cut down the overall time in writing many reports by over 50%. This line of thought also led me to Python Programming Language which is even a more powerful tool for data analysis, with super-efficient automation and data visualization capabilities.

The second thing I have learned is the ability to find a more efficient way to do my job. This is basically the ability to Google my problems. So after agreeing within myself that there must be a more efficient way to do a particular task, the next step is to find actual ways to do them. I’ve gotten better over time at finding solutions to problems I might face when doing a particular task. So the first thing I learned is having the mindset that there’s always a more efficient way of doing things, and the second is the ability to Google problems.

What the future holds

Like I mentioned earlier, I tried my hands at many things such as web and project management before I got a job as a data analyst. With over 2 years of experience as a data analyst, I have realized I can gain more skills and still pivot into areas I initially picked interests in. Surprisingly none of the skills and knowledge I gained from the above-mentioned fields have been a waste. They have helped me to further excel in my role as a data analyst. I have been involved in a couple of company projects where I’ve had to developed requirements for the project. My project management knowledge came to the rescue there. My web development knowledge has made my journey into dashboarding, particularly with Python much easier. Data science seems to be the next logical progression but I do not know if I want that. For now, I want to continue in this path as a data analyst and probably move more into business intelligence, all with the aim of solving problems.

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