The action: Get familiar with Excel and consider learning R or Python/Pandas to gain a firsthand understanding of data and facts.

The long form: Being comfortable around data and analytics gives valuable insights into your company’s reality. In every organization, there are assumptions and inherited knowledge, some of which may not even be accurate. By being capable of exploring data on your own, you gain the ability to question and challenge these assumptions.

While there may be benefits in involving dedicated specialists for advanced analytics or creating permanent dashboards, it’s also useful for you to be able to gather information and visualizing it yourself.

By getting familiar with tools like Excel, you can perform basic fact-finding tasks efficiently. Additionally, consider expanding your skill set to include programming languages like R or Python, along with their respective data analysis libraries such as Tidyverse and Pandas.

Excel: You Suck at Excel with Joel Spolsky – YouTube for intermediate inspiration.

R and Python (with Pandas) allow you to write scripts to repeat data cleaning, analysis and visualization efficiently and with better traceability if something goes wrong. If possible, try to replace some of your excel-work with one of these.

My experience has been mostly with the friendly R-community. Here are some useful resources:

• R for Data Science (book) is Hadley Wickham and Garrett Grolemund’s excellent introduction to R and the Tidyverse. I wish I knew about the Tidyverse sooner!
• R Programming 101 (YouTube-channel) Greg Martin’s friendly tutorials.
• Albert Rapp (blog, course, YouTube-channel) makes useful and concise tutorials.
• Tidy Tuesday (YouTube), watch an expert analyse a new data set, and pick up some tricks on the way.

Becoming self-sufficient in data analysis allows you to uncover insights, identify trends, and make informed decisions based on concrete evidence. It also enhances your ability to communicate effectively with data professionals and stakeholders within your organization.