HR-Data & Advanced Reporting
There is untapped potential in the data assets of every company. As a rule, organizations have too much data rather than too little. This data hides both current problems and solutions. Effective data management is used to extract business-relevant knowledge from the available data. Especially for HR data, this responsibility must not be neglected or fully delegated to an IT department, but HR itself is responsible for a data-driven work culture to get more out of the data. Thanks to the insights gained from the data, companies can adapt more quickly, identify potential opportunities and risks more easily, and reduce costs in a targeted manner.
We offer you the following support:
A data strategy is always important when knowledge is to be generated from various data sources. In HR, data is often available in various databases, IT systems or in Excel spreadsheets. In other words, fragmented siloed data management. The quotation from the HR director of a medium-sized mechanical engineering company that Excel “is the blood in the HR department” is also very true. The problem is that manual work is very error-prone, difficult to update and, above all, time-consuming. The time spent on this can be used more sensibly by the HR department, for example to look after the interests of managers and employees or to (further) develop current strategic topics such as “New Work” and “Employee Experience” in the company This first requires a sensible data strategy, which is then implemented with determination.
The development of a data strategy defines the concrete roadmap for the profitable use of HR data for advanced reporting with a good user experience. For this purpose, goals, challenges and necessary framework conditions are elaborated. In this way, you define how you want to use your personnel-relevant data from internal and, if necessary, external sources in the future according to a structured plan. You can achieve outstanding results with it.
First, the collection and procurement of the relevant data within the framework of the HR data strategy takes place (Collecting Data). Then the data is cleaned (Data Cleaning). In the process, damaged or inaccurate records are identified and corrected. This is important basic work for the subsequent further processing of the data. You may have heard of “Garbage In, Garbage Out”. Accordingly, clean and valid data are the small basics for the subsequent calculations in order to produce valid and meaningful results. The time investment required for this depends on the data volume and data quality of the primary data source. An initial professional data check brings clarity to this. Special care is required when working with employee data. Both data protection and co-determination must be taken into account appropriately in order to maintain and further strengthen employee trust. Early involvement of the data protection officer is essential here. One challenge in using employee-related data is the privacy requirements for collection, storage, and aggregation. Here, the current and future legal bases (e.g. DSGVO) must be observed. This requires close coordination with data protection officers and works councils, which HR Consulting is happy to support if required.
Communication with data has never been more important than it is today. Based on previous data preparation, data can be displayed visually. For this purpose, the data is read into a visualization tool (we work primarily with Tableau, MS Power BI and Excel) and there the interesting HR data, relationships and KPIs (Key Performance Indicators / strategic key figures) can be presented in a visually comprehensible and interactive form, as a report, dashboard or via app. Only good visualization reveals patterns and stories in the data. Data visualization helps organize the information and improve understanding, interpretation, and attention. Depending on the data source and the update cycle of the respective data, the update of such reports and dashboard can be automated by establishing a connection between the data source and the visualization tool. This is faster and more efficient. It is also important to note whether the dashboard is operational (e.g., recruiting dashboard) or strategic (e.g., management dashboard on the company’s key HR metrics).
Depending on the metric, context, and preference, the visual representation can take the form of a scatter chart, bar chart, line chart, tree map, watterfall chart, or histogram, for example.
Data Skills & Culture
For the development of data skills and a data culture in your company, we offer a mix of consulting, coaching and online training. We call our offer in this regard „Blended Journey Consulting“. Your employees will thus be empowered to carry out and expand HR data projects independently. The goal is not to turn HR employees into data scientists, but to build basic competencies that help develop a confident approach to data. This also supports the establishment of a data culture (Data Driven Thinking).
Data Driven Thinking adopts an approach that has been common in product management for a long time (the so-called “Design Thinking”). The 4-stage Data Driven Business Cycle is applied as a systematic approach to complex problems:
Dealing with data is a journey, a process. It’s definitely not a classic project. Often, it is only at the end of the journey that the business benefits and value of improved HR data management are fully realized. Research showed that this journey in HR takes about three to five years to transform the HR team into a well-functioning, strategic value-added analytics function.2
Therefore, it is important for companies – regardless of whether they are corporate groups, medium-sized companies or small businesses – to start or continue this journey immediately!
We are happy to support you in this – contact us.
Examples of HR data:
HRIS data (HR Information System)
Headcount and planning data (workforce data)
Skill data (profiles and competencies available in the company)
Personnel development data (e.g. further training attended, certificates obtained)
Employee satisfaction data
Staff turnover data (e.g. length of service, number, reasons for change)
Performance data (e.g. performance, mobility)
Workplace data (e.g. mobile working, home office)
Time management data
Health data (e.g. health rate, sick days)
Productivity data (e.g. utilization)