People Analytics – Avoiding Project Failure: Part One

By Max Blumberg, Founder, Blumberg Partnership

People analytics is only effective when data collection is focused on achieving a particular management objective – yet despite this core concept of people analytics, many companies simply analyse the data nearest to hand – with the results being anything but insightful.

People analytics project failure usually boils down to just one thing: it simply means that hardly any significant correlations could be found in the data.

In order to avoid people analytics project failure, a systematic, cost-effective methodology must be followed. For this, two tools are needed: the People Metrics Definition Process and People Metrics Definition Workshop for Operational Managers.

The Four-Block People Analytics Model

The People Metrics Definition Process methodology has a notion that the primary - and perhaps only - reason for investing in people programmes – such as recruitment, development, succession planning, and compensation - is to deliver the workforce competencies required to drive the employee performance needed to achieve specific organisational objectives.

This can be visually expressed as follows: 

If any link in this model’s chain is broken, it means that investments in people programs are not delivering the organisational objectives aimed for.

The strength of a link between any two blocks in the model is referred to as the statistical correlation. When two blocks are correlated, a change in the values of one block can be predicted from a change in the values of the other.

Let’s put this into a real world example - a training programme improves employees' competency scores, which in turn results in a predictable, corresponding increase in employee performance ratings. This would show that competencies and employee performance are correlated. However, where there is a poor correlation between competencies and employee performance, then training programmes which increase competency scores will not result in increased employee performance, resulting only in a wasted budget.

How to create robust people data sets with strong correlations

1. Organisational objectives metrics

Organisational objectives data reflects the extent to which business objectives are being achieved. This data is often expressed in financial terms, although there is an increasing drive towards the inclusion of cultural and environmental measures. Workforce objectives (such as retention or engagement), should not be confused with organisational objectives.

Create organisational objectives for people analytics projects as follows: First ask: "What organisational objective(s) need to be addressed?" Where possible, choose high profile objectives such as those which appear in the annual report (such as revenues, costs, productivity, environmental impact, and so on). Then narrow down this list to those metrics which, for example, reflect targets that are being missed. This approach ensures your people analytics has relevance.


2. Employee performance metrics

Employee performance data is typically generated by managers in the form of a multidimensional ratings obtained during performance reviews. An employee performance rating should simply reflect the employee's potential to contribute to organisational objectives. Note that the term potential is used deliberately to emphasise that employees who do not fully contribute to organisational objectives today, may do so in the future if they are properly trained and developed. A common error here is confusing employee performance measures with competency measures.


3. Competency metrics

Competencies are observable employee behaviours hypothesized to drive the performance required to deliver organisational objectives. The word "hypothesized" is used to emphasise that the only way of knowing whether the company is investing in the right competencies is to measure their correlation with employee performance. If the correlation is low, it can be assumed that the company is working with the wrong competencies (or that there is a problem with performance ratings).

There are four issues usually associated with competency data. First, competency ratings are often based on generic organisation-wide competency frameworks.

Second, competency frameworks are often created by external consultancies lacking full insight into the real competencies required to drive employee performance in a particular sector and organisational culture.

Third, many companies confuse employee competencies with employee performance – presenting competencies as part of an employee's performance rating. As noted above, competencies are merely hypothesized predictors of employee performance. They are not stand alone measures of employee performance.

Finally, global competencies usually exhibit far lower correlations than role-specific competencies. Competency definition is something I also expand on in the next part of this blog.


4. People programmes metrics

Programme data usually reflects the efficiency (as opposed to effectiveness) of talent management programmes such as the length of time it takes to fill a job role, the cost of delivering a training program, and so on. Programme data is usually sourced via the owner of the relevant people process.

In People Analytics – Avoiding Project Failure: Part Two, I dive into the People Metrics Definition Workshop for Operational Managers and explain how the Talent Management Lottery can be avoided.

 

The CIPD HR Analytics Conference & Workshop is taking place on 22-23 November in London. This HR analytics conference will help you to connect your people data to your organisation’s objectives, and ensure you have the knowledge and understanding for effective reporting and the delivery of clear insights.

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