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

By: Dean Stamoulis, Tom Handcock, and Amy Scissons



This article is part of research carried out under Leadership Analytics, a pillar of Leadership Labs.



It’s a pretty reliable formula: take just about any industry on the planet, introduce Big Data, advanced analytics and business intelligence tools—add a little time—and you’ve got the recipe for transformation.

Almost every major industry has become data-centric, operating faster and smarter, and with more certainty. So, why hasn’t the field of leadership advisory been able to harness the same transformative power from the data revolution?



The data revolution across sectors

When an industry embraces big data, seismic change soon follows. Advanced analytics have emerged as critical drivers of change, sparking a revolution that's reshaping the very foundations of diverse industries.

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Financial Services

Few industries generate as much raw data as financial services. And big data analytics has managed to transform not only individual business processes, but also the entire financial services sector—helping it stay ahead of market trends and price movements.


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Big data has swept over the retail industry like an efficiency tsunami. Cloud data solutions have changed the ways companies track inventory levels and sales figures in real time, and predict future demand more accurately. And being able to crunch a massive volume of shopper data means that retailers can get hyper-personalized when it comes to offers.


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Logistics & Supply Chains

After big data analytics got introduced to the supply chain industry, an entirely new slew of optimizations quickly became the standard. Companies now rely on data-driven decisions to optimize routing, improve factory processes, and create razor-sharp efficiency across the entire supply chain whilst increasing transparency and proactivity.


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Patient studies, health records, and medical devices are creating petabytes of data. Thousands of hospitals, private practices, universities, and pharmaceutical labs are now collecting, analyzing, and using this data to make daily breakthroughs in diagnostics, medicine, and patient care.



There’s certainly enough reason for the leadership advisory industry to pursue a data-driven approach—more specifically, producing predictive links between CEO attributes and company performance.

The motive is there. And many have made progress down this path (including RRA). Yet a true data-driven breakthrough hasn’t yet transformed the industry.



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

#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.


  Leadership team

A new approach for a new era of predictive performance in leadership

There’s a huge opportunity here.

The industry needs a new way to measure predictors of leadership performance— one that builds on the multi-faceted nature of success in leadership and that embraces the shifting, company-unique nature of performance.

Succeeding here can bring about a marked impact in how leadership advisory makes effective use of data—and perhaps even a revolutionary leap forward.

One step in this direction is Leadership SPAN, an assessment model developed in conjunction with Hogan. SPAN is based on the premise that the best leaders are not defined by a single skill but by their ability to move across “competing competencies.” As a result, the most effective leaders can be both disruptive and pragmatic, risk taking and reluctant, heroic and vulnerable, galvanizing and connecting.

Another is Top Team Effectiveness, an assessment model that allows us diagnose challenges facing C-suite teams—and how to overcome then. Through our research in this area, we found that the key to building successful top teams lies in the ability to balance four essential tensions—Adaptability, Perspective, Atmosphere, and Team Leadership.



But, we want to do more—and we believe we can do more.

We want to prove the link between CEO psychology and performance—to help organizations better identify which CEO candidates will suceed in the top job. 

As Einstein said: "You can’t solve a problem with the same mind that created it.” So, we are opening the doors to clients, communities, academia, and non-profits to start a conversation, drive our mission forward, and ultimately think differently about the future of leadership.

Join us as we press toward a data-driven breakthrough in leadership advisory.


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Find out more about what we’re doing in the data leadership space with Leadership Analytics.