We recently advertised a Data Analyst role, which had the following desirable skills listed:
- creating and querying data models using SQL
- working with both structured and semi-structured data
- exploring and visualising data
- using probability and statistics to perform and support analyses
- drawing insights from large and complex datasets
- using hypothesis tests to create rigorous insights from data
- working in an agile manner to continuously deliver work
- articulating results to a broad range of audiences
We didn’t expect any candidates to tick every single box, but the most compelling applications at the first stage ticked a number of them.
An oft neglected skill
Most data jobs will measure some element of data engineering and data analysis, as I talked about in part 1 and part 2 of this series of blogs. This blog is more concerned with a (so-called) soft skill, which I believe is absolutely essential: “Data Evangelism”.
Data Evangelism
You’ve shaped and analysed your data – now you need to get someone to take notice! This requires good written and oral communication skills, and the ability to work closely with people on problems they face, so you can help identify where data might help solve or illuminate a problem. Amazing and insightful analysis is worthless if there is no action taken off the back of it.
How does it relate to desirable skills?
In our job ad, the last two bullet points were the Data Evangelism skills we were interested in:
- working in an agile manner to continuously deliver work
- articulating results to a broad range of audiences
How to learn Data Evangelism
This one is harder to learn in a concrete way, and to a large extent you will get better with experience and practice. That being said, there are definitely concrete things that you can do in order to develop your skills in this area:
| What do you need to know? | How can you learn? |
|---|---|
| How to tailor your language and presentation to the audience | At FreeAgent, we speak to lots of different audiences about data and concepts, from small focused meetings of 2 or 3 people, to Town Hall meetings of over 200. The key is to be able to tailor your language to the audience, and don’t bombard people with thousands of charts and images – try to make your data “tell a story” – only show what is necessary, and avoid jargon where possible. If you search online for “data storytelling”, lots of blogs and articles come up, and also a few books. Storytelling with Data, by Cole Nussbaumer Knaflic isn’t one I’ve read (yet!), but has received excellent reviews and endorsements. |
| How to work collaboratively | Working with data is about more than sitting on your own “crunching numbers”. You need to be able to understand “what”, “how” and “why” in order to have a meaningful impact. That means talking to people and listening to what they say, asking the right questions and translating the answers to something you can work with. The Art of Statistics by David Speigelhalter has some fantastic early chapters on how data analysis starts with understanding the problem and then planning, before even looking at data. It’s fantastic reading for understanding how to, erm, understand. |
I hope you enjoyed reading about Data Evangelism and why it is important. If you didn’t already, check out my blogs on the skills needed to become proficient in Data Engineering and Data Analysis – happy job hunting!