An approach for implementing and embedding HR analytics

13 September 2017 Consultancy.uk

The interest in HR analytics is growing. Directors expect their HR teams to anticipate developments in the HR field and advise on future HR policy. HR teams, in turn, hope that HR analytics will support them in realising this, and given the possibilities HR analytics has to offer, they are quite right to do so. Unfortunately, many organisations struggle with embedding HR analytics – Petra Schrijver, a Consultant at Improven, looks into the adoption barriers in the realm and provides tips for how organisations can improve the roll-out.

The advantages of HR analytics are substantial. It provides an insight into the efficiency, effectiveness and impact of HR activities. This allows the HR organisation to better quantify its contribution and advise the organisation on a strategic level. Especially in today’s world, where the war for talent continues and organisations are faced with personnel shortages in crucial functional areas, this is more important than ever.

Recent research conducted by Deloitte shows that 71% of the organisations surveyed give HR analytics a large priority. Looking at the current trends and developments in HR, this is not a surprise. HR processes are increasingly optimised and digitalised and E-HRM allows managers and employees to be in control of their own HR matters. As a result, the role of HR is changing from an administrative and operational function to a more strategic and advisory one. HR professionals consider HR analytics to be a condition to successfully fulfil this new role and provide the organisation with data-driven advice.

It turns out that the number of organisations where HR data is gathered and actually used for predictive analyses has remained stable over the past three years. At the same time, the demand for reliable HR data is increasing. I also see this with many of our clients at Improven. HR functions not only require reliable HR data for reporting purposes but they are also keen to start using data for predictive analyses. So, if the interest in predictive analyses within HR is increasing, why has the number of organisations using this actually remained the same?

Obstacles

The stagnation can be explained by the lack of preconditions that are in place. Organisations are simply not ready to start using HR data for such advanced analytical purposes. Many analyses start with discussions around definitions of HR data and HR KPIs. Common definitions that organisations often do not (yet) have in place. Furthermore, many organisations have limited analytical and statistical competency within their HR department. In order to conduct analyses, affinity with, and knowledge of, statistical methods is necessary. However, these are requirements you don’t typically find in HR vacancies.The pyramid of HR data usage

HR analytics : a definition

How can organisations make sure they are ready for HR analytics ? Above all, it is important to agree on the definition of HR analytics. Many organisations classify everything that has to do with HR data as ‘HR analytics’. Research indicates that the majority of HR managers, when asked what HR analytics actually is, think it is about HR reporting and the justification of investments in HR. At Improven we define such activities as ‘HR Metrics’. HR Metrics describe and measure what happened in the past. HR analytics is more analytical and predictive, and therefore future-oriented. We use the following definition for HR analytics: ‘The use of HR data for the purpose of analysing and predicting the efficiency and effectiveness of HR activities, and their impact on overall organisational performance’.

The four levels of HR data usage

In using HR data, we distinguish between four levels. Level 1 consists of operational HR reporting. Level 2 focuses on dashboards with HR KPIs. Level 3 concerns tactical analyses on the efficiency and effectiveness of HR activities. Lastly, level 4 consists of strategic analyses regarding the impact of HR activities on the organisation’s overall performance. Levels 1 and 2 describe and measure what happened in the past (HR Metrics). Levels 3 and 4 are more analytical and predictive in nature (HR analytics).

Cumulative preconditions

To make progress in the use of HR data, organisations need to meet specific preconditions. These preconditions differ for each level but are cumulative, meaning that if organisations want to conduct tactical analyses (level 3), they not only have to meet the precondition for level 3, but also those for HR reporting (level 1) and HR KPIs (level 2).

I will further elaborate on these cumulative preconditions with an example. Let’s say an organisation wants to know whether the duration that their recruiters have worked with them affects the quality of their new hires. In fact, this is about analysing the effectiveness of HR activities. A tactical analysis at level 3. An important element of this analysis is ‘length of employment’. If we don’t know the length of employment of the recruiters, we are not able to conduct this analysis. The first step would therefore be to run a query on ‘length of employment’ for all recruiters in the organisation – HR reporting at level 1. A question that logically follows is how ‘length of employment’ is determined. Does it follow from the employee’s last hire date? Or initial hire date? What if an employee is re-hired after working for another organisation for a period of time? Or if an employee was employed as a contractor first?

In summary, where do you start and stop counting? By agreeing with one another on what ‘length of employment’ means and how it is determined and registered, you avoid strategic decision-making based on misinterpretation and unreliable outcomes, which is obviously highly undesirable. Therefore, defining HR data is considered to be a precondition for properly reporting on HR data (level 1). A similar question can be asked for the definition of the KPI ‘quality of new hire’. When is a new hire considered to be successful and how do you measure this? In the example above, we first have to define ‘length of employment’ and ‘quality of new hire’ and agree with one another on how they are determined or measured.

Challenging preconditions

We see many organisations struggle with the preconditions that are associated with levels 1 and 2. Moving towards clear, unambiguous HR data definitions is a challenge that is easily underestimated. Besides defining HR data and HR KPIs, organisations experience difficulties in creating a ‘single point of truth’. By using multiple systems, organisations run the risk of data being registered more than once and not kept up-to-date in a consequent matter. How do you know which data to use in your analysis? The challenge becomes even more complex if there’s confusion about who ‘owns’ the data. Quite often, when HR data, or reports, are incomplete or (appear to be) incorrect, the finger is pointed at IT. How can IT be held responsible or accountable for the data entered by HR employees or line managers? Organisations should assign responsibilities related to data ownership but are sometimes reluctant to start this discussion.

Are employees unaware? Do they wonder why they have to enter or report on specific data when nobody ever looks at them? Are data definitions missing and responsibilities related to data ownership not assigned? And are agreements on HR processes and HR systems lacking? This will lead the quality and reliability of HR data to drop rapidly. When organisations are not able to move towards using HR data at level 3 and 4, we call this ‘hitting the wall’. HR departments want to quantify their contribution to the organisation’s overall performance, but are hitting a proverbial wall in doing so.

Maturity model for HR data usage

How to gain control

Improven has developed an approach that offers a solution to this problem. We help organisations to get ready for HR analytics by creating a basic foundation. In doing so, we use Nolan Norton’s maturity model. This model allows organisations to mature in their use of data by focusing on the following areas: organisation, systems, people and processes. For each of these focus areas, Improven has defined critical preconditions, for both level 1 and 2, see also image 2. By meeting these preconditions, organisations create the right foundation to start working with HR analytics .

HR Scan

How does Improven translate this approach into practice? Using an HR scan, we determine the current level of HR data usage. We assess the extent to which preconditions on level 1 and 2 are currently met and suggest improvements for each focus area: organisation, systems, people, and processes. We then work with our clients towards meeting these preconditions. Which of the focus areas need most attention varies from one organisation to the other. Some of the organisations have their processes standardised, digitalised and clearly described but somehow still experience difficulties in providing reliable management information. This can be due to missing HR data or KPI definitions, for example.

In other organisations, employees are lacking necessary knowledge of the HR data chain. They don’t understand what the HR data is used for or by whom, both inside and outside of the HR department. Even though the focus of improvements differs between organisations, the ultimate goal is to let organisations meet the preconditions up to level 2. This enables them to be in control of their HR Metrics, thus creating the necessary foundation for HR analytics.

Related: Four success factors for the adoption of HR analytics and reporting.

News