Andy Kriebel, global head coach, The Data School
Describe your career to date
I started working in the data industry in the mid-1990s as a business analyst for a US-based insurance company. There, I developed a keen interest in data analysis and visualisation, which eventually led me to pursue a career in the field.
I went on to create the data visualisation programmes at two of the world’s biggest brands, Coca-Cola and Facebook, that revolutionised the way they analyse and communicate data.
I am also an active contributor to the data community. In 2010, I started VizWiz.com, a website that provides data visualisation advice and tutorials. I run a popular YouTube channel, lead the Makeover Monday project, which challenges participants to create and share visualisations based on a given data set every week, and host The Dual Axis podcast, featuring influential people in data.
I am also a regular speaker at industry conferences and the author of “Makeover Monday: Improving How We Improve and Visualize Data, One Chart at a Time” with Eva Murray.
In 2015, I joined The Information Lab, a leading data consultancy firm, to start The Data School. In my role as global head coach, I have trained hundreds of data analysts to help clients understand and communicate their data effectively
What key skills or attributes do you consider have contributed to your success in this role?
If I had to boil my success into one word, it would be practise. When I started, I couldn’t get enough of building visualisations and learning about data visualisation. And I’m still on that learning path today. You will not improve if you don’t practise, and that practise needs to be deliberate and focused.
What level of data maturity do you see across the data industry and what tends to hold this back?
Data maturity is improving, but not at the pace “promised” years ago. The data industry is still too focused on building reports and not focused enough of analysis and finding insights. Perhaps this is a skills gap, but companies need to learn that getting something from their data is more important than producing dashboards and reports. We also need to move away from a one tool will do it all mentality; there are different tools that are great at different things. Be willing to adopt more tools so that you can improve efficiencies.
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