People Analytics – Avoiding Project Failure: Part Two

By Max Blumberg, Founder, Blumberg Partnership

Leading on from People Analytics – Avoiding Project Failure: Part One, here I describe techniques for avoiding people analytics project failure due to poor correlations.

The People Metrics Definition Workshop for Operational Managers: Avoiding the Talent Management Lottery
One of the most common reasons for poor correlations in the Four-Block People Analytics model is the use of inconsistent employee performance data, which most often results due to managers not knowing what good performance looks like in their teams.

By far the best (and easiest) way of transforming a performance management lottery into an analytical programme is the People Metrics Definition Workshop for Operational Managers, the key deliverables of which are:

  1. A set of people programme, competency, employee performance and organisational objectives metrics
  2. Increased engagement between operational managers and the data that they will be using to manage their teams. You simply cannot get operational managers to engage with HR processes and data if the model’s metrics definitions are provided by non-operational parties, such as external consultants or HR. Even if these external metrics are of high quality, operational managers will still tend to treat them as tick-box exercises because they do not believe (probably correctly) that non-operational parties can truly understand the business and its culture as well as they do.

    The role of HR and/or external facilitators in the People Metrics Definition workshop is therefore not to provide content but to expertly facilitate the gathering of people metrics and helping managers reach a consensus.

Restricted Range, Babies and Bathwater
Another cause of poor correlations that comes from not properly distinguishing between high and low performing employees is known as Restricted Range, which means that team member performance ratings tend to be clustered around the middle rather than using the full performance rating range.

There are many possible reasons for restricted range. Sometimes it's because managers simply do not know what good performance looks like. Another common cause is that in order to maintain team engagement and unity, they avoid low scores; on the flip side, they may avoid high scores so as to avoid feelings of favouritism.

Restricted range carries two serious implications:

  1. Restricted range not only restricts employee performance ratings, by definition, it also seriously restricts the possibility of decent Four-Block People Analytics Model correlations
  2. If everyone in a team has similar ratings, then managers must be using some other basis - some other scale even - for making promotions and salary decisions. Secret scales cannot be good for team morale or guiding employee development, compensation and succession planning investments.

This typically cultural issue must be carefully understood before attempting intervention. One remedy usually involves explaining to managers that more differentiation between their high and low performers will result in the right team members getting the right development which in turn will result in higher team performance for the manager.

Perhaps this is the time to raise the thorny topic of employee ranking – a little reflection reveals that avoiding employee performance ranking is a case of 'throwing the baby out with the bathwater', because if employees are really no longer measured, then on what basis are promotions, salary increases and development investments made? Presumably it means that the 'real' employee performance measures have been pushed underground into secret management meetings and agendas where favouritism and discrimination cannot be detected. This cannot be good for employer brand.

If additional proof is required that employee ranking will always exist, consider what would happen in the event of a serious downturn and these companies were forced to lay off employees as was recently the case with Yahoo! If employees are not ranked in some way, then any layoffs would appear to be random and are a return to the talent management lottery scenario.

It must therefore be reasonable to assume that no matter what a company's public relations department says, employee performance ranking still exists everywhere even if it has been temporarily pushed underground in the past few years.

An important role of people analytics professionals - and HR in general - is to contribute positively to an organisational culture where such scales are part of the analytics mainstream and not hidden in secret meetings. This ranking approach can be extended - and is indeed already used by many companies - by asking multiple managers to rank the employees and/or using a team 360.

Technical reasons why data may not hang together
Finally, there are some technical statistical reasons why the Four-Block People Analytics Model data may not correlate, such as:

  • The data set may not be large enough (e.g. you need a lot of data for proper analysis)
  • If you're using non-machine learning/parametric techniques, the data may not be sufficiently normally distributed and/or may not be linear. This is another good reason for companies to consider migrating to the use of machine learning techniques.

Poor correlations in the Four-Block People Analytics Model are a stark reminder that people analytics data needs to be collected with specific business objective outcomes in mind. Using any other form of data ultimately results only in wasted time and resources. This approach must be one that involves operational managers – who are each critical to the defining of metrics to be used. Only then can people analytics truly deliver on all that it promises.

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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