A webinar hosted by The HR World and sponsored by Cognisses Talent Analytics, part of the DeepLearn Group, recently delved into the use of talent analytics to drive more value for HR from early careers and general talent management.
The expert panel explored how HR which could make the most of existing data to generate smoother talent management practices. The webinar is packed with great ideas and examples to inspire HR leaders to pursue their own paths on data led talent management, and is well worth a view. Meanwhile, two of the panel members, Johanna Reategui, Client Services Director, Cognisess and Cian Short, Talent and Development Manager, Kepak Group answer further questions on the topic.
Q: What example can you give of the most value driven from talent analytics for an organisation?
Johanna: One of the most valuable applications I have seen is in internal mobility and talent visibility.
Many organisations assume capability gaps and move quickly to external hiring. However, when they start connecting different data points such as performance, skills, learning activity and behavioural indicators, they often uncover internal talent that was not previously visible.
This shifts the conversation from “we don’t have the talent” to “we didn’t know where to look.”
The real value comes from moving beyond descriptive metrics such as hiring volumes or turnover and instead understanding capability and potential more deeply. This leads to better and more targeted decisions, and often reduces unnecessary external hiring while improving the use of existing talent.
Cian: Over the past couple of years I have gained the most value from Talent Analytics in supporting the recruitment of Graduates & Apprentices to our Early Careers programmes.
EC recruitment has historically relied on academic achievement and then the assessors intuition during assessment centres and/or interviews, but that isn’t always the best indicator of success.
We worked with each of our functional leads to create a functional profile for our programmes, using our Values, our behavioural framework and the JDs for the roles these people would perform. We then mapped cognitive and personality assessments against these profiles to indicate potential to succeed in the roles we are recruiting for, it gives us a clearer idea of future success.
By analysing the performance of candidates in assessment centres, during their programme, then post programme progression and retention data, we can ensure that we recruit the best suited people to our programmes and then proactively guide graduates and apprentices into roles where they are most likely to thrive in the future.
Q: Are talent analytics reasonably easy to understand and start using, or does it require significant training?
Cian: Getting started with Talent Analytics is often easier than people think it will be, the key is not trying to analyse everything or add more data points in, but by being selective about using what data you already have available to you.
Start with a specific challenge you have, whether role, function etc. then pick a couple of data points to help you build your picture, you can always add more data points in later, but it is important to build your confidence in using the analytics and build credibility in the data before going too wide.
The important thing is to start building your, and the wider teams, capability around using the data, recognising patterns and what they potentially mean, and then what to do as solutions, once you have done the interpretation. We have huge amounts of data available to us, but the key is picking what to use when, and then what to do with what the data tells you.
Johanna: In my experience, getting started with talent analytics is simpler than many organisations expect.
You do not need a fully mature data infrastructure to begin. The most effective approach is to start with a clear business question, for example understanding attrition in a specific role or improving internal mobility, and then connect the data you already have around that question.
Where organisations tend to face more challenges is in interpretation.
Much of HR data is either perception-based, such as performance reviews and engagement surveys, or descriptive, such as turnover or absenteeism metrics. On their own, these do not explain root causes.
This is where capability becomes critical.
When organisations complement qualitative insight with more structured signals such as skills, cognitive ability, behavioural tendencies and emotional intelligence, they can move from simply observing outcomes to understanding what is driving them.
So while starting is relatively straightforward, building the capability to generate meaningful, decision-focused insights does require a shift in mindset and, in some cases, upskilling HR teams to think more analytically.
Overall, it is less about technical complexity and more about asking the right questions and connecting the right signals.
