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TURNING TALENT DATA INTO TALENT INTELLIGENCE

January 20, 2014

Earlier this month, I requested Nik to guest-blog for me and he kindly agreed to reproduce an HBR blogpost he had written. I found the points around consistency and connecting it with other relevant data, very relevant. Even in my career, I have seen these becoming the major contributors to lack of Talent Insight.

Talent IntelligenceNik Kinley & Shlomo Ben-Hur

Big data is all the rage in HR recently. But more immediately promising is the talk of small data — of more effectively managing the data we already have before we start thinking about analyzing more complex datasets.  And nowhere is this more pertinent than with talent assessment data. For here, sitting right under organisations’ noses, is a huge, easy, and yet almost always overlooked opportunity to fundamentally transform the impact of their talent management.

Every year, companies spend in excess of US$3 billion on talent assessment – on trying to identify the right person to hire, promote, or select for talent-development programs. Companies do this in all sorts of way, generating all manner of data about which candidates are the best or most suited to a particular position. And this is just fine.

The problem, however, is that most stop right there, only ever using their assessment results to inform decisions on individuals. Too many firms, then, are missing the opportunity to start using their aggregate assessment data for something more ambitious. Because when you build and use your talent intelligence effectively, development processes can be targeted, recruitment processes can be adjusted to bring in certain types of talent, and retention processes can be better aimed at specific talent populations. This may sound complex and difficult, but it need not be.

For an example of just how much you can achieve relatively simply, consider a large, global company we recently worked with. We were able to transform their selection processes by performing just three, simple analyses using no more than a simple spreadsheet:

  • We compared the average competency ratings of new hires 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 further changes in hiring practices.
  • We compared the qualities distinguishing high-potentials with those actually being promoted.   On the one hand, we found that those labelled high-potential were more outgoing, showed greater entrepreneurial spirit, and were generally rated by their managers as performing more highly. This was certainly reassuring to the business, as it was trying to adopt a faster-paced and more edgy approach. But when we looked at promotion processes, we found that the people being selected were those who performed well but were viewed as team players. As a result, new criteria for promotion were developed.
  • Finally, we looked at the average competency profiles of the various groups measured to identify capability gaps and fed the findings into the learning and 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.  More broadly, to put yourself into a position to turn your talent data into talent intelligence requires three commonsense steps:

Collect it. Collection should be centralized and include all your talent-measurement data– interview ratings, psychometric scores, competency ratings. It may be possible to use an HR IT system to do this, but a large spread-sheet will do, as well. The centrality of the database is key here, because without central collection, businesses cannot build up a picture of the talent across the organization. Talent data is a valuable resource and it should be managed as such.

Make it consistent.  By “consistent” we mean, make sure that as far as possible you’re collecting the same data for everyone’. For example, if you measure one person’s intelligence and another’s personality, bringing the two pieces of information together will not tell you much. But if you know the personalities of both people, then you can compare them. And if you collect these data consistently for enough people, you can compare individuals to the average profiles of a group, or you can compare the qualities of different groups. It is therefore critical that as far as possible you know the same information about different employees. Without this, meaningful talent analytics is simply not possible.

Connect it. Just collecting the data isn’t enough; you then need to do something with it. The critical step here is to connect it with other types of data. For example, knowing the average competency ratings of new hires can be useful. Yet if you can then connect this to individuals’ annual appraisal performance ratings after they have joined, you can see which competencies are most predictive of initial success. And if you can connect it to records of who is subsequently promoted then you can see which competencies are most valued in the business. It is only through connecting assessment data with other types of information such as these, that its full value can be realised.

Succession plans and talent pools and managing talent “on demand” may get all the headlines and be genuinely good and desirable.  But none of it stands a chance of making any real difference unless it is built upon good talent intelligence. And for that, a few simple steps can go a long, long way.

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Nik Kinley is a London-based Director and Head of Talent Strategy for the global Talent Management consultancy YSC, whose prior roles include Global Head of Assessment & Coaching for the BP Group and Head of Learning for Barclays GRBF. He has specialized in the fields of measurement and behaviour change for over twenty years, and in this time has worked with CEOs, factory-floor workers, life-sentence prisoners, government officials and children.

Shlomo Ben-Hur is an organizational psychologist and Professor of Leadership and Organizational Behaviour at the IMD business school in Switzerland. He has more than 20 years of corporate experience in senior executive positions including Vice President of Leadership Development and Learning for the BP Group, and Chief Learning Officer for DaimlerChrysler Services.

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