HR Analytics

Quick access to high quality workforce data are an essential ingredient of successful management. Modern methods and tools create an opportunity for HR to make connections between previously disparate sets of information to tell a complete story – like learning data to performance data or recruiting data to sales data. I am referring to further examples down below.

The development in this area has been accelerating in recent years. HR tech vendors love to advertise their products with bumper stickers such as “AI powered”. However, what’s underneath these procedures is uniformly methods for statistical classification of data which have similar strengths and weaknesses like other statistical methods. If you are planning to deploy such methods, it is advisable to challenge such claims e.g. in the context of a vendor selection.

  • What are the basic procedures, algorithms and their assumptions?
  • What are the prerequisites that need to be met in your company to successfully apply AI-based procedures?
  • How are AI algorithms trained and what effort is required for the successful application?

Under consideration of these guiding questions AI-based procedures can effectively be deployed. The focus should be on the effective combination of AI based applications – which have unmatched superiority in narrowly defined domains – with genuinely human strengths such as abstraction, generalisation and common sense.

Regardless of the methodological preferences – for the productive use of HR Analytics two perspectives are intertwined:

The foundation for data-based decision making

  • Are data definitions aligned across the company and are they actively used?
  • Is the workforce data model sufficiently defined and reliable?
  • What is the level of data quality? Do repeated queries produce identical results?
  • Is your system architecture supporting the "single point of truth"?
  • Are currently collected data, metrics and KPIs relevant, selective and actionable?

Application examples for HR Analytics & Data Science:

  • Which questions from your business units need reliable answers about your workforce?
  • Which attributes of candidates increase the probability of their success in your company?
  • What are your top teams and which criteria determine their top performance? How can such insights be scaled and transferred to other teams?
  • How do you plan your future workforce needs?
  • How can you reduce the leave risk of high performers?
  • How can you measure trainings productivity impact?
  • How can you identify and eliminate unnecessary overtime?
  • How to define and measure diversity goals?

Get in touch

Here are a few examples from my service portfolio:

  • Data architecture/data modeling of complex workforce structures
  • Definition and deployment of dashboard systems (metrics, key performance indicators and quantitative analyses) to align the HR function with the business strategy
  • Process and Service KPIs for quality assurance
  • Multivariate methods for research and validation (e.g. regression analysis, linear structural equation modeling)
  • Support and advisory for the assessment and implementation of scenarios with machine learning scenarios
  • Project controlling and benefit realisation
  • Tool selection (e.g for analyses and visualisation) and implementation
  • Staff training in German and English

All our services and activities are in compliance with the General Data Protection Regulation (GDPR) with regards to the processing and storage of personal data.