11 06 2014
door Sabine Votteler

The HR analytics journey at Maersk: interview with Peter Hartmann

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Male hand holding microphone for the interviewAt a recent HR Analytics Conference in London, we met with Peter V.W. Hartmann, PhD., Business Intelligence Expert and HR Analytics Manager at Maersk Drilling (at the headquarters in Denmark). Peter presented the HR Analytics general framework and the working model in his business unit and gave the conference participants an overview of the roots of the HR Analytics function at Maersk, insights in several specific projects, the required expertise for HR Analytics and his personal advice to HR professionals on how to start with HR Analytics. It was a great presentation and we are delighted to be able to present Peter on the HR Intelligence Blog. Here we go with the interview…

1. Peter, when and why did you start working with HR Analytics?

When I joined the test publishing industry in 2006 I began teaming up with our customers to investigate the return on investment of their use of our tools, as well as what candidates would fit what positions. However, it was not until I joined Maersk in 2012 that I started doing this as my actual job. I have always been inspired by the combination of psychology and metrics (hence also the Ph.D. in psychometrics) – so HR Analytics was a natural next step after working for several years with psychometrics. HR Analytics is also a way of bringing more facts and science into HR.

2. What is the general framework and working model of HR Analytics at Maersk and what is your approach?

Our general working model starts by identifying the key areas of interest: what is our strategy focusing on? What is critical to the business? Who can we team up with internally? Once we have defined these areas, we consult our existing knowledge to build a model. Then we have a look at what the theories say, what research suggests and which experts we have at Maersk itself. A third step is to analyse the internal data, to know what data we have, what our data has to say and what our “existing knowledge” says about this. The final step is to communicate the results by displaying the information in an intuitive way. We make this an easy-to-understand story illustrated with our own data, and provide recommendations on actions. See figure 1 below, HR Analytics at Maersk – General Framework & Working Model




At Maersk we believe that the practical application of metrics, statistics and research methodology provides valid information for better decision-making. We started with descriptive analytics, simple statistics to display key metrics in a user-friendly format for the purpose of tracking progress. From there we went to linkage, in which we use statistics and research methodology to generate new insights and translate these into recommendations. And finally, by using predictive analytics we are able to predict future events and translate them into recommendations. See figure 2 below, HR Analytics at Maersk – Practical Application




3. Were you able to achieve HR or Business results by using HR Analytics?

I do not have any concrete achievements that are directly caused by HR Analytics – but this is consistent with our approach, namely that HR Analytics provides additional information to guide decision-making. So all our projects have pointed towards potential solutions or focus areas – but have not been the sole determining factor in what to do. However, showing a clear link between employee engagement and safety & business performance has surely helped drive an agenda of “employee commitment matters”.

At the end of the day HR Analytics is a helpful tool but not the decision-maker. It’s like going to the doctor: he/she might take a blood test. But ultimately it is the doctor who determines what to do next, not the test result. So HR Analytics cannot stand alone.

4. Could you give us a specific example of a recent predictive HR Analytics project?

I think we have made a lot of great projects – but if I have to highlight one, then I think one of my favourites is a study we did on the relationship between HR Metrics, like engagement, managerial commitment, training and then safety incidents. The reason for this is that the question is clearly business-critical since it is an explicit priority for Maersk to ensure a safe working environment; secondly, we combine “soft metrics” with hard core metrics and in turn demonstrate that HR Metrics matter; thirdly, it leads to recommendations as to what you could focus on to drive safety; and finally, from a personal perspective, it gives me great satisfaction to know that some of the work I am involved in could ultimately help prevent colleagues being injured. On an end note – we are recurrently in the process of finalising a similar project showing similar results. See figure 3 below, Linkage & Predictive HR Analytics – Example.




5. What do you see as next steps in the use of HR Analytics?

If you see HR Analytics as we do – the practical application of research methodology, statistics and metrics to provide valid information for better decision-making, then it is a helpful tool for basically all HR; whether this be general processes or concrete projects. So everything can potentially be in scope – if legally and ethically possible, of course.

6. What would you change if you had to start again?

In hindsight, everyone has perfect vision, so I would probably change a lot to confront some of the road bumps encountered (a number of them I will address later). But the most significant thing would be to think of HR Analytics as just a subcomponent of Functional Excellence & Business Analytics/Intelligence in general – and hence consistently working across disciplines, e.g. Safety, Finance, Operations, HR, etc. Because, in principle, we are less interested in HR Analytics, and even in HR in general, than whether the organisation and all its relevant functions are working optimally and in sync.

7. Which advice would you give HR professionals who are planning to start using HR Analytics?

I think my first piece of advice would be to really consider whether they truly want to, and are ready to do this – and why they want to do it. HR Analytics is super hype now and everybody would like to do it. However, HR Analytics is just a “means to an end”, and not an end in itself. It is like wanting to buy a car, because “it is cool” or “everyone else has one”, but without knowing what you need it for – you will likely be unhappy about your car purchase, no matter what car you actually buy.

Once you can answer the question “why do we do it?” and “what do we want to do with it?” then the lessons I have learned are:

  1. get intimate with your data and process,
  2. HR Analytics requires a lot of time and hard work and provides few clear results,
  3. ask the right business questions and ensure collaboration across experts and stakeholders,
  4. managing expectation and communication is essential, and finally
  5. ensure sufficient accuracy, but prioritise the insight to be business-relevant and ready to give recommendations.

Thank you very much, Peter, for these interesting points of insight!

Peter Hartmann

Peter Hartmann acquired his Ph.D. on the topics of cognitive ability, personality and psychometrics, then worked several years as Chief Psychometrician for the leading test publishing company in Denmark. His interest and expertise in measurement, research and statistics led him to join the Maersk Group where he is currently responsible for the assessment tool & HR Reporting/HR Analytics/HR Business Intelligence at Maersk Drilling.

Other HR Analytics related interviews on our blog: interview with Prof. Cade Massey (what HR can learn from sports analytics), interview with Tom Davenport (why are some firms struggling with implementing HR Analytics?) and finally interview with Patrick Coolen, HR Analytics Manager at ABN-AMRO Bank (the HR analytics journey at ABN-AMRO). Enjoy the reading!

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