CREATING TALENT INTELLIGENCE
How to avoid hiring, firing and promoting at random and boost the bottom line
By Professor Shlomo Ben-Hur and Nik Kinley - July 2013
Organizations succeed when they have the right people in the right roles: hardly a controversial statement these days. We know that companies that outperform their peers at talent management also return significantly more value to their shareholders – around 22 percent more than the industry average. We also know that good hiring and promotion decisions have a bigger impact on market value than creating a customer-focused environment or having good union relationships.
Why, then, do so many companies struggle to get talent management right? Because they simply don't have the talent intelligence required.
Talent intelligence is the understanding that businesses have of the skills, expertise and qualities of their people. It is the basis of every people decision that companies make; without it, they would be reduced to hiring, firing and promoting people at random. The problem is that most organizations' talent intelligence isn't that intelligent.
Gather the right information
Talent intelligence boils down to one simple idea: companies can only make good talent decisions if they know what they need, what they have and what is available. There are a number of impressive talent management software tools that will help companies to clarify this but, like all systems, they are only as good as the data they are given.
This gives the first challenge facing organizations that want to improve their talent intelligence: knowing what data to gather. At the moment most talent data relates to workforce composition – demographics and distribution. This sort of administrative information has its uses but is inherently limited. What is more relevant and valuable is talent assessment data that covers the skills, attributes and characteristics of an organization's workforce.
Know how to use your data
Information and analysis do not automatically create intelligence. The next step – one that many firms struggle with – is knowing how to use it. For example, assessment data can be used for much more than making decisions about individual people, such as whether to hire or promote someone. One of the easiest wins is using the results of talent assessment to inform and support processes such as onboarding and development, but only 19 percent of firms actually do this.
In researching for our new book, Talent Intelligence, we spoke to many companies about how they used their talent assessment data and we found not a single one that was extensively, consistently and effectively using this data to shape their people strategy. This is just crazy. It is like buying a sports car and then only ever using it to drive the kids to school.
All you need is a spreadsheet
Data analysis may sound complicated but it does not require specialist expertise – just a basic comfort with numbers and a spreadsheet. Start by identifying how connected the different types of talent data you collect are, both with each other and with other sorts of information. For example, knowing the average competency ratings of new hires can be useful. Yet if you also know the performance scores of new hires one year after they have joined, you can see which competencies are most predictive of initial success. If you know who is still employed three years later, you can work out which factors are most predictive of retention. And if you know who is later promoted, it can provide insight into the types of talent valued in your business and the qualities predictive of longer-term success.
Talent intelligence in action
A few years ago, a large global business in the energy sector asked us to help it establish assessment processes to support three key people decisions: recruitment, promotion, and the identification of high-potentials. The processes created were not complex, but led to lasting, significant changes in their people strategy.
First, we looked at the competency ratings of new hires in each division to check whether some divisions were attracting stronger candidates and whether the qualities of new hires were aligned with each unit's business objectives. As a result, all three divisions were able to make improvements to their attraction and hiring activities.
Next, we compared the average competency ratings of new recruits with those of current employees. We found that the new hires had an uncannily similar pattern of strengths and weaknesses to the current employees. This kick-started a debate in the business about whether it was "just employing clones," which in turn led to changes in hiring practices.
Then we looked at the qualities that distinguished people who were identified as high-potentials and those who were actually being promoted. We found that the people labeled as high-potential were generally more outgoing, more entrepreneurial and better performers – all characteristics that the business was seeking. When we looked at the qualities most likely to lead to promotion, however, we found that the key factors were good performance and being viewed as team players. For all the encouragement the business was trying to give people with the qualities it thought it wanted, the people actually being promoted were different. As a result of these findings, new criteria for promotion were implemented.
Finally, we looked at the average competency profiles of the various groups measured and fed the findings into the learning and leadership development functions. As a result, specific development programs were created to address key competency weaknesses in particular groups of employees. The measurement data thus enabled better targeting of learning investment.
These were all simple steps, accomplished with simple data and without resorting to expensive systems, but they led to powerful findings that ultimately helped the business deliver its growth strategy.
This is the key, critical difference between talent data and real talent intelligence: intelligence makes a difference, adds value, and helps you to improve the bottom line of your business. Everything else is just data.
Shlomo Ben-Hur and Nik Kinley are authors of the new book Talent Intelligence: What you need to know to identify and measure talent. Ben-Hur is Professor of leadership and organizational behavior at IMD business school in Switzerland, where he is the program director of Organization Learning in Action. Kinley is an independent consultant with more than 20 years' experience in talent measurement and behavior change.
 Axelrod, E.L., Handfield-Jones, H., & Welsh, T. (2001). The War for Talent, Part Two. The McKinsey Quarterly. 2, 9-11. Huselid, M.A (1995). The Impact of Human Resource Management Practices on Turnover, Productivity, and Corporate Financial Performance. Academy of Management Journal. 38(3), 635-872. Combs, J., Liu, Y., Hall, A. & Ketchen, D. (2006). How Much Do High-Performance Work Practices Matter? A Meta-Analysis of Their Effects on Organizational Performance. Personnel Psychology. 59, 501-528.
 Watson Wyatt (2002) Linking Human Capital and Shareholder Value: Human Capital Index. Fourth European Survey Report. London: Watson Wyatt Worldwide.
 MacKinnon, R.A. (2010) Assessment & Talent Management Survey. London: TalentQ.