The essentials of the HRD dashboard

January 27, 2020

Talentia simplifie la complexité RH

Key HRD indicators, state of play

Data Culture and HR

Like Finance, there is no real data culture in terms of HR performance. This is due to two key factors: the low number of indicators and the time lag in HR, where changes take time to make themselves felt. In addition, the processing of data related to individuals (absenteeism, etc.) is strictly regulated by law and limits the possibilities of analysis. Establishing and managing an HR dashboard is therefore not easy. In addition to data related to turnover, absenteeism and recruitment, there are steering indicators related to the HR department’s missions in terms of fairness of treatment, diversity and employee commitment, while guaranteeing anonymity.

HR must focus on areas where it can have a real impact (performance management, payroll) and be able to produce factual data to demonstrate that employees are treated fairly and that performance is equitably distributed.

Data structuring and processing

The HR function is increasingly driven by data, the role and volume of which has increased with new technologies and the emergence of new professions such as data scientists.

The HR function is structured around three periods: entry into the Company, which touches on the themes of attractiveness and recruitment, life within the Company (commitment, diversity, equity) and employee departure (turnover analysis). Indicators are needed for these three areas. For example, applicant track systems make it possible to collect a large amount of data on the profile of candidates (number of CVs received, origin, etc.) which makes it possible to optimise investment in recruitment.

In the same way, the data can be used to steer the employer brand policy and target priority actions, particularly on social networks. In another area, the offering in terms of engagement surveys has evolved significantly and constitutes a mine of information to help management in decision-making, for example by studying the link between turnover and engagement rates, which are not necessarily correlated. Tools such as Glassdoor or Happy Candidate allow companies to work on their employer brand and analyse their recruitment.

Data Limits


The regulations on personal data, the GDPR in particular, have a strong impact on the data collected by HR, their processing and their use. Indeed, beyond the control of data confidentiality, the regulations prohibit the collection of data on a certain number of sensitive areas (religious beliefs, social origins, etc.) and the hindsight on these issues is still insufficient to grasp the work to be carried out on these data.

Setting priorities

Companies sometimes lack hindsight on certain tools such as Glassdoor and sometimes suffer a form of “dictatorship of the note”. However, the opinion given on such sites is not necessarily relevant, whether it comes from customers or former employees. It is therefore important to define the importance given to this data and to give it meaning through the implementation of action plans. It is therefore necessary to save time in producing the data, and to define priorities before indicators.

Two areas should be distinguished: data that fall within the scope of the HR management function (recruitment, employee life, etc.) and data related to equity and regulations, for which the HR function is the guarantor. However, two aspects pose difficulties. The first concerns the borderline between HR and production: is the treatment of absenteeism an HR or production responsibility? In this area, HR cannot replace management in decision-making.

There are also a series of questions related to the indicators to be communicated to the management committees.

Manual data processing

The manual processing of the data allows, with relatively simple filters, to establish typologies by department that may give rise to HR problems. For example, an unproductive employee is often indicative of an HR issue. However, the multiplicity of systems and tools can make it difficult to identify existing data and consolidate it.

Key indicators to be monitored

Three key indicators are to be monitored: rate and reasons for leaving (or turnover), payroll and hiring rate. Other themes may be of interest: attractiveness of the company (Glassdoor rating, number of applications received, recruitment lead times, departure rate in the first two years), average seniority per department, mobility rate, cost of turnover, corporate social responsibility and sustainable development, etc.

Measuring the effectiveness of HR dashboards

Good practices for defining an effective dashboard

There are problems of data construction and reliability, especially for manually processed data. In this respect, it is important to keep the same methods over time, in order to facilitate the comparison and interpretation of the evolution of the data. Indeed, HR data must be assessed over a long period of time, because corrective actions do not produce their effects from one month to the next. The obligation to achieve results is less direct than in areas related to a company’s finances.

Existing data, but which are not always up to date

In particular, for SMEs, there is a subject of corporate management and the reporting of relevant information to the Executive Committees and social partners, which does not allow problems to be identified and decisions to be taken. It all depends on the maturity of the HRD and the managers’ appetite for these issues. The typologies mentioned, on attractiveness for example, are relevant, but the data are not necessarily fed back into practice.

Towards automated and forward-looking dashboards

As HR data plays a role in decision making, it is important that dashboards take a forward-looking approach, for example to establish a profile of a typical candidate. However, this horizon still seems distant. Moreover, this raises questions about the use and comparison of the data with those of other companies, which raises the question of making HR data available in Open Data, as the volume of data in the SMEs is relatively low, it is necessary to be able to compare oneself with other companies in order to make projections.