Talent Metrics Maturity Framework

While there is a lot of focus on enabling Machine Learning adoption in Talent Operations — a key pre-requisite for ML model creation is the availability of relevant data and a clear understanding of what metrics need to be tracked. Without the relevant metrics in place, any ML initiative is going to be unnecessarily long-drawn and will lead to a loss of credibility for the Data Science program. So, what is the way forward? The Human Capital Reporting (HCR) Team needs to take a multi-pronged approach to developing contextual ML-ready metrics - 1) Assimilate Global Standards: Understand existing global standards in reporting, which will come pre-baked with validation across industries and geographies. The ISO (International Organization for Standardization) enables this through a structured and consultative process (ISO — Stages and resources for standards development). The output of the standards creation process is a document that can serve as a Least Common Denominator (LCD) to be used across industries and geographies. A good reference document is the ISO Standard for “HRM — Guidelines for Internal and External HCR” — ISO 30414:2018 — Human resource management — Guidelines for internal and external human capital reporting. This standard looks at 11 core areas in HR that need to be tracked effectively as part of any HCR approach. The fact that this is an LCD means that it may not be comprehensive in its coverage and hence needs to be contextualized to your organization. <a href="https://togyjose.medium.com/talent-metrics-maturity-framework-7c6c9a8490ad">Read More</a>