Turn Talent Data Into Real Information

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 improve the way companies select and develop their talent.

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.

But most companies stop right there, only ever using their assessment results to inform decisions on individuals. Too many are missing the opportunity to use their aggregate assessment data for something more ambitious — to assess the effectiveness of their hiring processes, for example, or to track the outcome of employee-development initiatives. When you build and use your talent intelligence databases 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 groups. This may sound complex, 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 that was able to transform its 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. When we did, we found that the strengths and weaknesses of the new hires were uncannily similar to those of 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 labeled high-potential were more outgoing, showed greater entrepreneurial spirit, and were generally given higher performance ratings by their managers. 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 also viewed as team players. As a result, new criteria for promotion were developed.
  • We compared the actual strengths of members of groups or business units to those needed to fulfill strategic goals. This might sound daunting but it is simply a matter of taking the competency ratings from individuals' annual appraisals or recruitment interviews, and averaging the scores of members of a particular group or department. This enables you to see which competencies they are strongest or weakest in as a unit. For example, we worked with a business in which the CEO said that he needed his senior leaders to "stand up and make tough decisions" in order to drive change. Yet when we looked at the average capability ratings of new hires into this top-tier of business leaders, we found that that this capability was the one on which they were rated lowest during the recruitment process. So for all the CEO was saying that he wanted tough leaders who would champion change, the types of people the company was actually recruiting were individuals who wouldn't rock the boat.
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 spreadsheet will do, as well. The centrality of the database is key here, because without central collection, you cannot build up a picture of the talent across the organization.

Make it consistent. By consistent we mean, make sure that as far as possible you're collecting the same data for everyone. If you measure one person's intelligence and another's personality, for instance, 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 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.

Keep collecting and comparing it over time. Initial data are powerful; longitudinal data more powerful still. For example, if you connect the average competency ratings you've collected for new hires to the individuals' annual appraisal performance ratings the next year, you can see which competencies are most predictive of initial success. And if you can connect those to records of who is subsequently promoted then you can see which competencies are most valued in the business.

Succession plans and talent pools and managing talent "on demand" may be sexy — and good and desirable. But none stands a chance of making any real difference unless it is built upon good talent intelligence. And for that a little diligence will go a long, long way.

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