When working with psychometric and performance data, the real value emerges from how the data is sliced, applied and interpreted. By aggregating data to explore through a broader, macro-level lens, we can pinpoint differences within a cohort and identify trends and patterns in the data.
What do these patterns mean for your organisation?
This level of data exploration can:
- Inform targeted interventions for talent acquisition and management.
- Help shape L&D strategies and inform strategic talent development, especially when common gaps or strengths are identified.
- Drive more intelligent hiring decisions – whether to fill capability gaps with diverse thinkers or prioritise cultural fit and team cohesion.
- Uncover challenges in how talent is identified, particularly if certain competencies are consistently unmet.
- Reveal insights when correlating personality data with performance, retention, or engagement data – enabling predictive modelling.
Benchmarking against the global average using Hogan data
Comparing your cohort’s Hogan data to a global benchmark provides critical context. For example:
- Seeing that your team scores significantly differently to the global norm – whether below or above average – could highlight key areas for development.
- Similarly, a key point of difference on a scale or scales could be an area to reinforce or even celebrate.
This helps build a clearer picture of what ‘good’ looks like – not in isolation, but relative to a meaningful external standard. However, it’s important to always consider context; higher than average isn’t always ‘good’ and lower than average isn’t always ‘bad’. For instance, below-average Interpersonal Sensitivity might actually be a strength for a leadership team driving change, where directness and accountability are crucial, and higher than average Prudence might actually be a problem for fulfilling the role successfully if adaptability and flexibility are required.
Strategic talent development insights you can gain from this data analysis
Explore distribution skews
Beyond simple averages, looking at distribution patterns deepens understanding. For example:
- On the Adjustment scale, if nearly half the cohort falls in the low range, this is a skew – not an even distribution. This suggests potential struggles with stress and composure, indicating a need for targeted development.
- In contrast, a well-distributed Interpersonal Sensitivity scale tells a story of diverse communication styles within the group. This diversity can be leveraged – knowing when to collaborate with more agreeable vs. more forthright colleagues.
Spotting shared derailers and values
Using the Hogan Development Survey (HDS), shared derailers are flagged when more than 50% score above the 70th percentile. For instance:
- A cohort with shared derailers in Sceptical and Bold may need trust-building interventions, as these traits can amplify under pressure and influence team dynamics.
On the values side (MVPI), when more than 50% fall below the 25th percentile or above the 75th percentile, it suggests a shared value:
- A cohort with low Altruism may not respond strongly to charity-focused incentives, instead preferring self-reliant or commercial rewards.
- In contrast, a more diverse Hedonism distribution indicates differing motivations and potential conflict when it comes to finding opportunities for pleasure at work, useful for shaping culture or aligning purpose.
Deep-dive differences: Intergroup analysis
Segmenting data by region, function, or gender reveals nuanced insights:
- The Sales team valuing Commerce more than the People Team might skew overall results, hiding diverse values amongst the cohort.
- This layered approach prevents overgeneralisation and helps tailor initiatives more effectively.
Success vs. unsuccessful hires: Predictive insights
Comparing successful and unsuccessful hires can reveal preferred characteristics in top performers:
- Successful hires scoring higher on Learning Approach and Inquisitive suggests value placed on continuous learning and strategic thinking.
- Lower Prudence and Dutiful scores in successful hires might indicate a preference for spontaneity and delegation, possibly even a blind spot in leadership style (e.g., lack of attention to detail).
Even minor score differences in HDS traits like Excitable, Sceptical, and Reserved offer valuable clues. These may reveal preferences for emotionally expressive, questioning, or tough-minded individuals in certain roles – vital knowledge for both selection and development.
Building data stories
Our data team at PCL can work with you to combine multiple layers, averages, distributions, derailers, values – to build a holistic picture. For example:
- A cohort may appear ambitious and creative, but weak in planning and structure, leading to challenges in execution.
- This cohort might make space for bold and creative strategic thinking as they want to be seen to make an impact and drive change. Whilst their ambition might be fuelling their momentum to lead the charge, a lack of focus could lead to unfinished initiatives and inconsistent execution. To unlock their full potential, they need structure, clear priorities, deadlines, and defined accountabilities. Supporting them with tools that channel their determination and creativity, but not at the expense of contingency planning, will be crucial.
This storytelling approach makes the data come alive, turning numbers into narratives that influence strategy, culture, and strategic talent development.
Correlations & advanced mapping
You can also examine intra-candidate correlations:
- Are people who are detail-focused (Prudence), for example, also likely to be big-picture thinkers (Inquisitive)?
- In many cases, the correlation is weak, but still present. If you’re seeking both these characteristics in one person, you may be accidentally narrowing your candidate pool. This highlights the importance of prioritising the characteristics that really make the difference in a successful hire.
More advanced correlations with performance data (e.g. sales, retention, engagement) provide a route to predictive analytics. For instance:
- In a sales cohort, Planning & Organising was the most predictive competency for performance.
High potential trends
Using the Hogan High Potential model, you can spot patterns like:
- Shared strengths in strategic thinking (e.g. ‘Thinking Broadly).
- Common development areas such as Influencing Others.
- Or concerning trends – like high scores in ‘Getting Noticed’ – raising the question: are we overvaluing extroversion in identifying high potentials?
These insights not only inform development priorities but also challenge and refine your talent management processes.
Additional considerations
- Weighting data by role (e.g. weighting leaders more than direct reports) can offer more accurate organisational insights.
- Psychological measures like resilience, resistance to change, or psychological capital can add another layer of value.
- If you’re going through change, personality correlations can highlight likely resistors vs. champions – helping you tailor support and communications.
In Summary
Whether you’re using Hogan or other assessments, meaningful data analysis can:
- Unlock patterns you didn’t know existed.
- Sharpen hiring and strategic talent development strategies.
- Align organisational culture with individual values.
- Drive performance with evidence-based insight.
Our data trends analysis service will help you turn data into decisions, and assessments into actions. Book a discovery call and find out how our bespoke macro-level data analysis will support your talent development strategy.