“People may be our ‘most important asset’ and even our most expensive asset, but they are rarely our most measured asset”

 Davenport, T.H. & Harris J.G. (2007) Competing on Analytics:

The New Science of Winning Harvard Business School Press

 

Client Brief

The Executive team in a global services firm could not understand why profitability in certain project teams was greater than in others.

They were also concerned about increasing dissatisfaction among members of the workforce and loss of headcount.

Their response was to put increased pressure on employees in loss making areas. This approach seemed to worsen the problem.

Our brief was to investigate the source of the problem and develop an approach that would address the problem.

 

Method

For this client, we built a predictive model that related business performance to business outcomes – specifically, the profitability of projects and other human resource variables such as the risk of employees leaving the company and the factors that drive this

Probably the greatest opportunities to achieve competitive advantage lie in the effective management of people – precisely because so many businesses neglect this aspect. However, the more data savvy organisations are starting to relate investment in human capital to financial return. This trend has resulted in the emergence of a new specialised area of Business Intelligence that we call Human Capital Analytics.

Human Capital Analytics is a subset of Business Intelligence and employs statistical techniques to analyse, among other things, the relationship between business performance and people’s skills, characteristics, thoughts, beliefs and behaviour in the workplace.

Routine employee surveys provide an historical view of employee attitudes, beliefs, and level of engagement at a particular time point. This is a static, descriptive view of the business that does not reflect how employee perceptions change dynamically. It results in reactive decisions to address problem that have already occurred.

It is more effective to measure employee beliefs dynamically, using statistical techniques that enable the leadership team to predict where problems are likely to occur. This way, leaders can be proactive and deal with problems before they arise.

 

Outcomes

 The leadership team found that the HR Predictive Model enabled them to make a measurable connection between the thoughts, beliefs, and characteristics of the workforce and the bottom-line. This learning has given them a greater degree of control over their business helping it to manage through the downturn.

 pecifically, the model gave them a much better understanding of why some project teams were more profitable than others were.

Most importantly, they knew what they had to do to improve team profitability.

The model helped them to identify a leadership development initiative that would target the underlying problem. Furthermore, they had a basis for gauging the improvement that would likely result from this initiative.

They also found that the process of defining the data that they needed to support the HR Predictive Model revealed large gaps and inefficiencies in their information systems as well as gaps in their understanding of how their business works.

As a by-product of developing the HR Predictive Model, they now have a more complete information system and a better appreciation of the dynamics of their business.

This was a breakthrough for the leadership team. They realised that without the HR Predictive Model they risked treating the symptoms rather than the real underlying problems. The HR Predictive Model helped them to approach business management in a more strategic and proactive way.