Case Study – Data Doesn’t Lie

Well, data doesn’t lie unless the data is wrong. I was once delivered a task that seemingly no one else wanted: create quarterly reviews for our top fifty clients, plus a company-wide edition. At the time, the reviews were simply snippets cut out from a report auto-generated by the company’s online database and slapped into a PowerPoint. No one ever double-checked the data and not much thought was out into the presentation.

Personally, I feel PowerPoint is best used for presentations only. I found it restrictive for my purposes. And it didn’t take long for me to notice several inconsistencies in the data presented on the auto-generated report. By the way, this is a key skill for data analysts: the ability to recognize patterns and identity errors. If you enjoy puzzles and patterns, this is the job for you.

The first thing I focused on was getting access to the raw data in the form of Excel sheets. Once I properly organized and sorted the sheets, I went row by row until I was able to identify each and every error within the datasets. Mastering Excel is fundamental for data analytics.

For the first couple of years, I transitioned the final product for reviews from PowerPoint to Adobe PhotoShop. Of course, that was only a stopgap. After I learned the basics of Tableau, the goal now is to generate all quarterly reviews, and other reports, in Tableau itself. This requires me learning Python and R programming, which I am in the process of achieving. Albeit, slowly but surely.

My work on this project redefined the role of data, in general, and quarterly reviews, specifically, for not only the company, but our clients as well. It impacted marketing, sales, and internal review processes. I was able to generate specific charts and data that were used in large sales meetings, outside of just quarterly reviews. For example, I could generate weekly assignment turnaround times or chart usual, but vital statistical information.

We live in the Age of Information, and data is king. Three crucial duties a data analyst must fulfill: gather and interpret data, identify errors, and present the data in a visually pleasing manner. I know it sounds boring at first, but I love it. The raw data helps you get to the root cause of issues and can assist in helping to improve an organization from the inside out. I literally learned this job in the fly, with no assistance or guidance.

So the lesson is simple: never be afraid to take on new challenges. The risk of failure is worth discovering if it’s something you might love. It also reinforced my past experience in journalism with regard to fact checking. I am constantly “fact checking” the data I run to ensure accuracy. It’s crazy to think just how versatile the writing skill set really is…

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