Are Grandma’s the answer to the Data Talent Shortage?

It’s a very common generational stereotype that all grandmothers can sew, knit, crochet, or embroider – in fact, in my own life, I don’t know a single grandmother who can’t do some kind of needlecraft. If you’ve ever had the privilege of watching someone craft, it can look like some form of magic. How they can take basic materials and weave and stitch them into something truly wonderful. The next time you visit your grandmother, look at the bookshelf and I bet you’ll find some form of pattern/instructions for needlework. What you’ll find is nothing short of amazing in terms of language, diagrams and essentially coding.

So, what does this have to do with the data talent shortage?

I can’t help but feel like we’re continuing to dig on the spot that is well and truly mined. We know where the most traditional forms of talent come from, which universities and courses. There is only so much talent in these spaces to meet the demands of the data and analytics space. With the growth of GenAI, I think we can all be certain this demand is only going to increase. Of course, there will always be a huge need for these traditional routes into data science and engineering etc, but why don’t we apply some creative thinking to these challenges?

Tell me why a textile student who can follow these complex patterns couldn’t be taught Python?

Why couldn’t a sociology student, who is labelling their research with semantic code, be brought into a team to work on data governance models?

A psychology graduate who has a deep understanding of ethics, be brought into a team to work on the latest AI regulation and research?

A lawyer who has spent their career honing their communication skills, leading a data literacy programme?

You see the point I am labouring.

I think too often we have such a traditional view of who a data person is, even now in 2024, that we exclude so many other routes into data. Very often when we have this discussion we talk about the soft skills, but as I’ve mentioned above, I see no reason why the traditional “hard” skills of data can’t be taught and transferable, too.

It would be amiss of me to not mention the DEI element, too. The more routes into the data and analytics field, the more backgrounds we’ll have access to. From ethnicity to gender and social class, suddenly the talent pool reflects the society we serve far better. Which can only be a good thing for competition and social good.

We need to review our hiring practices, and what we need in our team vs what are we in the knee-jerk habit of asking for. So, whilst I don’t expect data teams to suddenly rush out and hire our Grandmas I’d like them to consider what we could learn from them.