Kompetenzen Entwickeln und Richtig Nutzen (KERN)

Java, Database Modelling, Mathematics

KERN was a significant project I contributed to during my stay at SAP. It encompassed a wide range of objectives and initiatives. In its initial stages, my primary responsibility involved supporting the development of a prototype to demonstrate the feasibility of the concept.

Within the KERN prototype, my focus was on three key areas:

  1. "Fit" Assessments: This component involved analysing an employee's skill metrics and comparing them to the skill metrics associated with specific job roles and career levels within SAP's hierarchical structure. Designing an algorithm to assess "fit" appropriately presented an interesting challenge. Conventional algorithms measuring proximity or similarity proved inadequate in this context, as they penalised both over-attainment and under-attainment equally. However, hiring an overqualified candidate incurs different costs than hiring an underqualified employee.
  2. Recommendation: The Recommendation component served two purposes. Firstly, it identified areas in which employees were under-attaining in their current roles. If no under-attainment was observed, it determined the skills necessary to progress to the next career tier (e.g., from mid-level to senior-level). Secondly, once under-attaining skills were identified, the component recommended appropriate learning resources tailored to the content and skill level. For instance, if a software architect at tier 3 aimed to progress to tier 4, KERN would identify their insufficient management skills and suggest advanced management courses or reading materials.
  3. Communication: As part of my involvement in KERN, I took the initiative to establish connections with key figures in SAP's HR department. By arranging meetings between the KERN team and HR managers with a vested interest in the project's potential, we gained valuable insights that would have otherwise remained inaccessible.

While the project exhibited promise, it faced several challenges that were not adequately addressed. One of the major concerns was the reliability of the data used to assess skills accurately. Assigning a numerical rating to someone's skills is an arduous task, and the effectiveness of the entire tool depends heavily on the quality of this data. Additionally, even if KERN functioned flawlessly, many employers would likely exhibit reluctance in adopting such a tool. For example, if an employee discovered they were overqualified for their current position, it could lead to questions about promotion opportunities and potentially create difficulties for the employer.

Nevertheless, working on KERN was an engaging challenge and provided invaluable learning experiences.

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