PRIORITIZE WHICH DATA SKILLS YOUR COMPANY NEEDS
by Chris Littlewood
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 (see figure 6-1).1
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 skill set. But which skills should we 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 two-by-two matrix with four quadrants, as depicted in figure 6-2:
- Learn: high utility, low time-to-learn. This is low-hanging fruit that will add value for you and your team quickly.
- Plan: high utility, high time-to-learn. While this is valuable, acquiring this skill will mean prioritizing it ahead of other learning and activities. You need to be sure that it’s worth the investment.
- Browse: low utility, low time-to-learn. You don’t need this now, but it’s easy to acquire so stay aware in case its utility increases.
- Ignore: low utility, high time-to-learn. You don’t have the time for this.
In order to help you decide where to focus your development effort, we have plotted key data skills against this framework. We long-listed 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. Figure 6-3 shows 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.
When it comes to growing data capabilities within your company, you can’t learn everything at once. Every organization has unique needs, and thinking about the time each skill takes to learn and its usefulness will help you decide which areas to focus on first. You can plot these on a two-by-two matrix and sort them into four groups:
✓ Learn: These skills have high utility and low time-to-learn. Skills in this quadrant will add value for you and your team quickly.
✓ Plan: These skills also have high utility but require time to learn. Acquiring these skills will mean prioritizing them over others, so you need to make sure they’re worth the investment.
✓ Browse: Skills in this quadrant have low utility and low time-to-learn. You likely don’t need these immediately, but they’re easy to acquire later, if necessary.
✓ Ignore: These skills have low utility and take time to learn. You don’t have time for these and can skip them.
1. World Economic Forum, “Employment Trends” in The Future of Jobs Report, http://
reports .weforum .org /future -of -jobs -2016 /employment -trends /.
Adapted from “Prioritize Which Data Skills Your Company Needs with This 2×2 Matrix” on hbr.org, October 18, 2018 (product #H04KOL).