The Talent Intelligence Collective Podcast
The one with Megan Buttita from IDC
Welcome to the second episode of the Talent Intelligence Collective Podcast!
This is what Alan Walker - the editor and host of #Chat Talent had to say about the latest episode of the TIC Podcast.
In this episode, apart from being joined by my amazing co-hosts Alison Ettridge and Toby Culshaw, we welcomed the incredible Megan Buttita from IDC.
Throughout the episode, we found out quite a bit Megan.
Did you know she started off as a librarian? She then spent 16 years as a practitioner before becoming an analyst. Now Megan is the Research Director for emerging trends and talent acquisition.
Quite a bit was happening in the world of Talent Intelligence when we recorded this episode. But a key element that Toby wanted to highlight was a presentation by Tom Davis on effective talent analytics.
Which then, obviously led me to ask: What actually is the difference between talent intelligence and talent analytics? It turns out that it’s quite the chunky question (who knew!?).
But I think Toby summed it up perfectly.
DATA AND ANALYTICS ARE LIKE KNOWING A TOMATO IS A FRUIT. THE INSIGHT PIECE IS KNOWING NOT TO PUT THAT TOMATO IN A FRUIT SALAD. AND THE ACTUAL INTELLIGENCE, IS THAT CONTEXTUAL PIECE AND THINKING IF THAT’S THE CASE – IS KETCHUP A SMOOTHIE?
We then moved into a discussion about where Talent Intelligence should sit. And the overall general importance of it being its own function.
We already give recruiters so many hats. Copywriting, print marketing, process change experts, headhunters… To impose the hat of talent intelligence experts as well - it’s a lot of hats to ask someone to wear.
Shifting back to general worldly updates we discussed using talent intelligence as a form of talent mapping. How using data can be used to paint a picture. Where are the gaps? What are the future trends? What skills are required?
Alison and Megan then addressed the importance of using Talent Intelligence to shine light on diversity & inclusion. How should we go about capturing data for diversity? Especially when we need to take into consideration not just gender or ethnicity. But also the different types of backgrounds and experience that an individual can bring.