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WHY MOST OF US (BUT NOT ALL) ARE RUBBISH AT SPOTTING TALENT

Updated: Jun 14

On the face of it, spotting rising talent and improving performance is easy.

Yet for many organizations, this is a complex and elusive quest. Martin Belton thinks some of the new business books may have hit upon why.

“Knowing what we don’t know is better than thinking we know what we don’t,” says Philip Tetlock in Superforecasting: The Art and Science of Prediction. This book was first published a couple of years ago. But it is now enjoying a new round of publicity which led me to its contents. This was a timely thought for me while I’ve been working on establishing our new model, the Talent Transformation Pyramid. More on that later because it’s also worth looking at the book’s underlying theme. That is, while most of us are quite poor at forecasting the future, there exists a small merry band who fare so much better than the rest of us. That cohort is far and few between. But statistics show it exists. The book goes on to reveal how these ‘superforecasters’ manage this.


Another popular business press read at present is Malcolm Gladwell’s latest, Talking to Strangers: what we should know about the People we don’t know. Interesting as always, Gladwell points out how bad we are at assessing strangers. In one observation, he cites the case of Judges assessing bail applicants in New York. They believed that face to face assessment was crucial. That was until a young researcher fed bare-bones information about applicants into a computer. It transpired the machine was superior to the judges at spotting re-offenders.


Misleading subjective assessments


I read them one after the other. I imagine that’s why I was struck by the similarities of their premises. The first similarity is hardly a revelation: both say that, when predicting future events, the more objective data you gather, the more chance you have of getting it right. But secondly, and more surprisingly, both point out how misleading subjective assessments can be and just how easy it is to be deceived and deceive oneself.


Going back now to talent and performance measurement, it’s not hard to see how such issues matter. Gathering, applying, and managing data for these functions can be onerous. But without it, we're relying on subjective judgments. The Talent Transformation Pyramid model was created to enable us to counteract this. It provides a solid view of what we know, and what we don't know. Designer Eric Shepherd set out to address these issues after hundreds of conversations with professionals in the sector. They revealed the lack of a recognizable model that could pull together all the relevant factors that enable us to identify and grow talent.


12 separate factors


The Talent Transformation Pyramid recognizes as many as 12 separate factors to enable us to do that. It clarifies the relationships between those factors. That enables us to document them within competencies and group them into a competency model. The model helps us describe what is needed to be ready to deliver performance. If we are to create an effective talent and performance system, it is to this kind of detail we must turn.


Of course, the model also admits that it may not always be possible to gather all the data we would wish to. But as Tetlock noted ‘knowing what we don’t know can be useful as well. That alone can give us a far stronger platform to evaluate our Talent.

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