Why Leadership Advisory Hasn't Had its Data-Driven Revolution

Why?

We’ve found two main reasons. Let’s explore.

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#1. Because it’s hard.

Nobody wants their newly appointed CEO to be a costly mistake. So it’s always in the board’s interest to remove as much guesswork from the decision-making process as possible, and ensure evidence is guiding their choices.

But there are a number of sizable hurdles to getting the right data and interrogating it well.

For one, much of the available data is at an individual CEO level. That makes it difficult to prove causation between the performance of the individual leader and the performance of an organization. Many factors beyond the CEO's control, such as industry trends or economic conditions, can influence these outcomes—making performance attribution difficult.

A well-performing company doesn’t necessarily equate to a high-performing CEO; the company could just be in a good state, or the market could be very favorable.

Likewise, a drop in company performance doesn’t necessarily mean the CEO is underperforming (contrary to high-profile media reports). Market conditions could be particularly bad, or the drop could have been so much worse but mitigated by the CEO’s actions.

Then of course, at other times there is a direct correlation between CEO and company performance—which is exactly what we’re aiming to identify through research and collaboration. Great CEOs have that market-moving capacity to make savvy decisions instead of ones that might be short-sighted, to innovate instead of maintaining the status quo, and the ability to guide the company through tumultuous times.

As the frequency and consistency with which psychometric tools are deployed in CEO evaluations, we now have the opportunity to look beyond the individual CEO’s data and look for patterns across the dataset from a large number of other CEOs.

#2. Performance itself is changing.

The other major reason it’s been challenging to revolutionize how we link predictors to performance criteria is because performance is a moving target.

The world is changing under our feet. Leadership performance is no longer just about stock price, but involves DE&I and ESG, soft skills, culture, building leadership teams, and more. In turn, what makes a ‘good’ CEO today is much more nuanced than it was 50 years ago. So any data collected over that time may be less practically useful now.

Furthermore, every organization will have a different definition of "performance" based on their specific strategy and goals. One company might want a turnaround CEO, whereas another might want a sustainability-minded CEO. So a truly successful CEO must arbitrate, as well as drive, the unique qualifiers of performance for their own organization.

In any case, as CEOs become more fluid and nuanced in their leadership styles, so must the ways we measure predictors of their performance.

The industry has amassed a hoard of psychometric data—data captured from assessments, interviews, and surveys—but we need a new approach to capturing data that mirrors the multi-faceted, intricate ways that leadership itself is changing.

To this end, we’re pioneering a new way of applying data to predictive performance—an approach to using data that mirrors the multi-faceted, intricate ways that leadership itself is changing.