1. October 2024

Successful DE&I Management with HR Analytics

In the age of big data and AI, where does HR data analysis fit in? According to a 2023 trend study by FHNW, a large proportion of Swiss companies analyse their employee data. Almost 90% of large Swiss companies carry out evaluations, as do around half of SMEs. And this trend is on the rise.

Forecasts with potential

Successful DE&I management requires a good data basis and a clear and accurate descriptive analysis. However, data is now also used to calculate trends and forecasts for the future. The question arises of whether it is worth the effort and investment in data-based HR management and especially predictive HR analytics.

Studies show that organisations can benefit from more accurate personnel planning, better business performance, and increased employee satisfaction when they rely on HR data-based measures. These benefits are not exclusive to large corporations but are also available to smaller firms.

‘Diversity in Technology’ – the Intel case

In 2015, Intel launched the ‘Diversity in Technology’ initiative. The aim of the initiative is to promote women, African Americans, Latinos, and other minorities in the technology industry. Data on hiring practices, promotions, employee satisfaction, and turnover rates among underrepresented groups were analysed. As part of this initiative, unconscious biases in the hiring processes were identified and eliminated. By the end of 2018, Intel had already achieved the set goals – two years ahead of schedule!

The data basis is the foundation for a successful DE&I data analysis

Such a project can only succeed if certain prerequisites are in place or created: It needs the data relevant to the issue at hand. These must be sufficiently up to date and reliable – and ideally available in a uniform format and collected in a single tool. Finally, analysing the data not only takes time and resources, but also requires expertise in the form of specialist knowledge and analytical skills. Metrics are of little use if they are not interpreted correctly. Here, HR acts as an interpreter of the data but relies on the relevant stakeholders (management, HR business partners, line managers) to be involved in decision-making processes.

A guide to successful DE&I data analysis

1. Define the focus: The first step is to define the topic or questions to be focused on. These may arise from the HR strategy or be brought to HR by senior management or line managers.
2. Collect data: This can often be found in personnel records and includes basic demographic data such as gender, age, and nationality. Data protection laws (e.g. the EU's GDPR) can make it difficult for companies to collect and use sensitive data about their employees. Particularly sensitive personal characteristics (such as ethnic origin, sexual orientation, or disability and other relevant diversity characteristics) can only be collected through self-disclosure – the voluntary disclosure of personal data. However, a particularly sensitive approach is needed here to motivate employees to take this step. Data must be anonymised and treated confidentially, and there must be clear communication about the purposes for which the data will be used. Data on recruitment and promotions, turnover rates, data from employee surveys, and salary analyses should also be collected. What often seems simple at first glance can prove difficult in practice, e.g., when the data has to be compiled from different tools. Incomplete or inconsistent data can also distort the analyses and lead to false conclusions.
3. Define KPIs: In general, the different diversity dimensions should be examined in the workforce, in management positions, and possibly also in special roles (profit and loss, personnel management) or by department (IT, sales, maintenance). The KPIs should also include steps in the HR process or evaluations of inclusion (e.g. from employee surveys). It is very important that the KPIs help to make statements about the topic or issue.
4. Analyse data and trends: Compare different groups to identify inequalities. Industry-specific diversity benchmarks can show where there is room for improvement and where you are leading the way. Analyses conducted over several years reveal trends and are often a good indicator of whether the measures taken are effective. Analyses that lack sufficient detail can conceal unequal treatment: for example, if female foreign managers are given more consideration for promotions and at the same time male foreign managers are disadvantaged, the unequal treatment ‘disappears’ when all foreign managers are grouped together in the analysis. Therefore, when analysing the diversity dimensions, an intersectional perspective is always recommended, i.e. the combination of two or more characteristics (e.g. gender and nationality).
5. Make results accessible: DE&I reports or dashboards are two ways to make the results accessible internally to management or to external stakeholders and to create facts. The challenge here is often to communicate data and metrics in a way that is clearly understandable to the different stakeholders. ‘Data is only useful as long as you are able to read it.’ Data literacy is one of the core competencies of professional DE&I specialists. They must be able to understand metrics and discuss them with management and executives.
6. Setting and optimising measures: Data analysis provides an objective basis for identifying strategic focus areas. Particularly in conjunction with an external industry benchmark, KPIs can highlight the need for action (e.g., the above-average turnover of men into part-time work) and provide an opportunity to define measurable targets. In addition, evaluations help to ensure that problems are identified at an early stage so that targeted measures can be taken. Regular evaluations using a set of metrics (e.g. diversity scorecard) help to evaluate the measures taken and measure progress. KPIs can provide a motivational boost when initial successes emerge (e.g. when the metrics for recruitment to management are balanced in terms of gender after an anonymised application process has been introduced).

Make HR analytics a strategic priority

In the future, data-based HR management and predictive HR analytics in particular will become increasingly relevant. If you want to rely on HR analytics, it is worth investing in financial and time resources. It is crucial to first consider which business challenges you want to address with HR data. Whether it's reducing employee turnover or promoting diversity, define clear goals and metrics to measure the success of your efforts in areas such as employee retention, satisfaction, or recruitment. Only with high-quality data can informed decisions be made.