What to do when you don’t like data
How to become more data driven when data is not your best friend or even a close acquaintance
You hear it everywhere — People are screeching from the rooftops about the need for everyone to be “data driven”. When I say everyone, I mean everyone. Companies are throwing the term into every job posting and PR plug they can find. Even employees that get lightheaded when the topic turns to data are being asking to interpret and utilize data in their decision making. But…
What if I don’t like data?
That’s cool, you don’t have to love data. I don’t particularly love working out but it’s a pretty big factor in my overall health — so I lace up my running shoes and trudge out the door with at least half a dozen expletives. You may need to do the same thing with data. Fight the urge to punch your computer every time you open Google Sheets and instead block off a few hours a week to walk through high level analytics topics to deepen your understanding. The more you learn, the more interesting it will become — I swear.
Some resources to help you get started:
The Ultimate Guide to Google Sheets — Google sheets is free, easy to use and has a ton of resources to learn more. Spend some time learning the various tools your teams use, it will make your life A LOT easier.
A Beginners Guide to Understanding Data Analytics — High level overview of key topics and definitions.
Ask your data team more questions
Every so often you may come across a data person that revels in the fact that they know more about data than you — those people suck. More commonly, you will run into translation problems. We have all been there. You are looking for a specific number, you find that number and it appears off. Annoying. You then ask a data person that doesn’t understand exactly what you’re talking about. You go back and forth for hours or days with no solution. Woof.
Problems like these are not the result of an analyst not knowing how to do their job (usually), it’s about the art of translation. The more experienced an analyst becomes, the better they tend to be at probing you for more information around your question to get to the root of the problem. You can speed up this process by giving what probably feels like excessive context to the issue you’re facing:
“Hi [insert data human’s name] — I am looking at this specific number on this report [insert direct link to said report]. I am trying to accomplish X and will ultimately be sending it to Y by next [insert reasonable timeframe] but I’m having trouble understanding how you got to this number. Can we chat for 5 minutes today about this?”
While it may seem unnecessary, little steps like this help facilitate conversations around the problems you are trying to solve and give data teams much needed context to help you solve them. Good analysts will want to walk you through an analysis and teach you as much as they can so you can work through these problems more quickly in the future or avoid them all together. Its a win-win. So ask for help.
How exactly does this help me?
I hate to be the one to tell you this (no I don't) but the trend toward more roles and companies focusing on data is not going to slow down. Getting more comfortable with data will allow you to:
Spend more time on things you like to do and less time struggling with data
Communicate using actual numbers and results when quantifying the impact the of a project you are working on — “It’s going well” isn’t going to cut it
Approach all problems that you face (even outside of work) in a more logical and process oriented way — ooh sounds efficient…and it is.
If anything, you should be open to learning more about the data your business is using. This will help you communicate across teams more efficiently and effectively. #winning