Prioritize Which Data Skills Your Company Needs with This 2×2 Matrix
Data skills — the skills to turn data into insight and action — are the driver of modern economies. According to the World Economic Forum, computing and mathematically-focused jobs are showing the strongest growth, at the expense of less quantitative roles. So whether it’s to maximize the part we play in data-driven economic growth, or simply to ensure that we and our teams remain relevant and employable, we need to think about transitioning to a more data-skewed skillset. But which skills should you focus on? Can most of us expect to keep pace with this trend ourselves, or would we be better off retreating to shrinking areas of the economy, leaving data skills to the specialists? To help answer this question, you can use a 2×2 matrix to plot how long it would take to learn a skill, versus how useful it would be, to help determine which would make the most sense to learn now. The skills that are highly useful, with the lowest time-to-learn are the low hanging fruit that will add value for you and your team quickly.
Data skills — the skills to turn data into insight and action — are the driver of modern economies. According to the World Economic Forum, computing and mathematically-focused jobs are showing the strongest growth, at the expense of less quantitative roles.
So whether it’s to maximize the part we play in data-driven economic growth, or simply to ensure that we and our teams remain relevant and employable, we need to think about transitioning to a more data-skewed skillset. But which skills should you focus on? Can most of us expect to keep pace with this trend ourselves, or would we be better off retreating to shrinking areas of the economy, leaving data skills to the specialists?
To help answer this question, we rebooted and adapted an approach we took to prioritizing Microsoft Excel skills according to the benefits and costs of acquiring them. We applied a time-utility analysis to the field of data skills. “Time” is time to learn — a proxy for the opportunity cost to you or your team of acquiring the skill. “Utility” is how much you’re likely to need the skill, a proxy for the value it adds to the corporation, and your own career prospects.
Combine time and utility, and you get a simple 2×2 matrix with four quadrants:
In order to help you decide where to focus your development effort, we have plotted key data skills against this framework. We longlisted skills associated with roles such as: business analyst, data analyst, data scientist, machine learning engineer, or growth hacker. We then prioritized them for impact based on how frequently they appear in job postings, press reports, and our own learner feedback. And finally, we coupled this with information on how difficult the skills are to learn — using time to competence as a metric and assessing the depth and breadth of each skill.
We did this for techniques, rather than for specific technologies: so, for machine learning rather than TensorFlow; for business intelligence rather than Microsoft Excel, etc. Once you’ve worked out which techniques are priorities in your context, you can then work out which specific software and associated skills best support them.
You can also apply this framework to your own context, where the impact of data skills might be different. Here are our results:
At Filtered, we found that constructing this matrix helped us to make hard decisions about where to focus: at first sight all the skills in our long-list seemed valuable. But realistically, we can only hope to move the needle on a few, at least in the short term. We concluded that the best return on investment in skills for our company was in data visualization, based on its high utility and low time to learn. We’ve already acted on our analysis and have just started to use Tableau to improve the way we present usage analysis to clients.
Try the matrix in your own company to help your team determine which data skills are most important for them to start learning now.
Chris Littlewood is the chief innovation & product officer of filtered.com, an edtech company that uses AI to lift productivity by making learning recommendations. Find him on Twitter @filtered_chris.
Prioritize Which Data Skills Your Company Needs with This 2×2 Matrix
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