The difference between talent intelligence and talent data?

High-performing companies are twice as likely to have talent programs aligned to organisational strategy, ensuring harmonisation between talent acquisition and overall goals, according to the Project Management Institute (PMI).

So why then are there still inadequate talent management strategies out there?

Part of the problem is confusion surrounding the difference between talent intelligence and talent data. Businesses need to know what skills they have in their organisation, where they are, what expertise employees have and which qualities staff members possess. This is talent intelligence. But the challenge lies in taking masses of facts and figures and effectively analysing them to enable the creation of an optimal talent management strategy.

While talent intelligence and data are co-dependent, they aren’t the same thing and it is the failure to convert data into meaningful intelligence that often leaves businesses unable to harness the skills they need to grow.

What is talent data?

Talent data is the raw facts and figures needed from which to build a talent management strategy. While the data can be meaningless without any analytics or understanding of what to do with it, get the wrong facts and figures and it’s hard to move forward with any programme.

Professor Shlomo Ben-Hur, director of Organisational Learning in Action at the IMD Business School, explained in HR Bullets that most data relates to workforce composition. This includes demographics and distribution. Of course, this is limited in how it can be applied. Consequently, companies need to ensure they have talent assessment data at hand, covering skills, attributes and characteristics of the workforce.

This is often collated using technology and monitoring procedures, enabling businesses to ‘mine’ information to understand what high performance is in their organisation and who is reaching this standard, Stuart Hearn, co-founder and director of HR specialists PlusHR, wrote in HR Zone.

Gathering and using this information is vital to succeed. Michael Capone, chief information officer at software company Automatic Data Processing, told Workforce: “The companies that leverage workforce analytics effectively will win the war for talent.”

Transforming data into intelligence

It’s easy to think that once you start analysing data, it transforms into intelligence. However, this isn’t the case and the process of taking facts and figures and turning them into something meaningful can be complex.

The first thing to do is to know which data to include in your analysis. For this, you need to understand what you want to ultimately get out of the information. For instance, do you want to make decisions about individual people or an entire department? Are you trying to support processes such as onboarding, succession planning or training? If not, should you be?

Without knowing the destination, it’s hard to plan the journey so be clear from the outset.

Once goals are determined, it’s time to begin data analysis. Professor Ben-Hur recommends starting by identifying how connected different types of data are. He explained in HR Bullets that it is important to make links between each data set and other sorts of information.

When data has been analysed, it is time to apply the findings. This is where talent intelligence is truly created and finds its value. Professor Ben-Hur uses a real life example to illustrate how this can be done.

He explains in the news portal that when asked by a large global business to establish an assessment process, he looked at the competency ratings of new hires to understand if some where attracting stronger candidates and if qualities of hires were in line with business goals. From this, different departments were able to see where they were lacking and improve their strategies.

The average competency rating of new recruits was also compared with those of current employees. Assessment then took place to determine the qualities that distinguished people identified as high-potentials and those that got promoted. It was observed that those identified as future talent had different attributes than those actually being promoted, meaning there was a disparity between the people the firm thought it wanted and those it actually valued in reality. Consequently, new criteria for promotion were created.

These examples demonstrate how it is possible to create real business intelligence that has real impacts on talent management. With 77 per cent of global chief executive officers anticipating making changes to their talent management strategies, according to PMI, understanding the difference between data and intelligence and how to apply it will be crucial going forward.