Practical Tips to Beat the Data Skills Shortage for Today and Tomorrow

Given the pace at which data has become such an important part of our lives and business (never forget, 90% of all data was created in the last two years), it is unsurprising that the growth of talent in this area has been outstripped by the growth in demand. I regularly hear Chief Data Officers (CDOs)understandably bemoaning the skill shortage, and so I wanted to share with you a few suggestions on how to alleviate the problem, that I have picked up over the course of these conversations.

Different Sources for Difference Skill Sets

Use different talent ‘sauces’

All talent comes from two places; internal and external. The skill set you are looking for should be a major consideration in choosing which pool you draw from. On the governance side, it may often make most sense to look internally; taking people from the operations side, ensuring they are familiar with the wider business, and identify those who have a natural affinity for data who can then be moved into the desired role.

Conversely, data science skills are usually a limited resource within any company, and so the only option is to look externally. Of course, this is easier said than done in a highly-competitive market with data scientists commanding a premium, but looking inwards to fill some roles will allow you to prioritize your external efforts on these positions.

Use different talent ‘sauces’[/caption]

All talent comes from two places;

The Early Bird Gets the Worm

Be sure to act early to get the talent worm

One way to give yourself an advantage when looking to secure talent externally is to get in early. A top data scientist with 15 years experience may be hard to find, and even harder to secure. If you had secured him or her at the start of their career, however, you would now be reaping the rewards. So, get in early, and identify talent young.

One CDO working in the Financial Services industry that I spoke with recently has had great success using this tactic. He hires people early, as their first or second job out of college (even earlier in some cases!). He puts them on the payroll young, and then builds them up over the coming years. The program he has developed so far has a 100% retention rate of these hires staying with the company, which really is hugely impressive.

Be a Match-Maker

Play Cupid and be a Match-Maker

Like me, he is a firm believer that if you are not happy, you won’t be productive, and so he tries some ‘human engineering’ to ensure that everyone fits well with their projects. As with any date, sometimes the match does not work out, and so he seeks to identify mismatched talent, and move them onto a new project.

Another CDO echoed this idea and told me that people must be paired based on their interests, and matched to the right part of the business to interest them. This helps to ensure they take the time to under the business problem, and leads to better results.

Another CDO recently told me that he views managing his talent as similar to match-making in the dating world. He tries to match personalities to projects, so that, for example, extroverts work on ‘aggressive’ projects, and introverts focus on the more ‘delicate’ projects.

Like me, he is a firm believer that

Widen the Talent Pool

How can we widen the talent pool?

My final tip is more for the data leadership as a whole, and is more long term, but ultimately I think the most important solution. The shortage of talent isn’t created at graduation ceremonies across the country each summer, it is created long before that. We, as a community, need to do everything possible to enlarge the talent pool to meet demand (which is only going to grow) in the future.

One major way I believe this could be achieved is by increasing representation of women in data. Currently, only around 25% of people working in data are women. If overnight, the number of women increased to the same as the number of men, we would have a 50% increase in the total amount of people working in the field. This may sound fanciful, but even though we can’t wave a wand and make this happen over night, it should be our goal to achieve this over the next decade. Of course, there are many other important reasons to do this such as diversifying the talent pool, but making use of ‘wasted’ talent is crucial.

I really hope that you find at least one of these suggestions useful – do let me know your thoughts on them, and any other useful advice you can share.

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Comments

  1. aaaa says:

    Good points. I have worked in data/information most of my 20+ yr. career. The companies that seem to now be struggling the most treat technology, and data, as a liability, a commodity, an overhead cost. I don’t believe there are many positions, including tech, are ‘widget’ positions where one can move a ‘resource’ in or out without repercussions. With data, especially, experience counts.
    It seems from what you wrote that the company you referred to develops and nurtures their talent. In my experience, talent having a job they want to do and being appreciated for it retains productive workers over money almost every time.
    Finally, as the creator and curator of an over 5,000 member LinkedIn group focusing on Data Management (Master Reference Data Management – https://www.linkedin.com/groups/1637247) I have seen a marked increase in female membership in the last 5 years. Additionally, with more recent Data Science endeavors I have seen an encouraging increase in female participation. I agree this trend would do well to increase and should continue. A more rounded data/information environment is best served with diversity.

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