Every company has designed its own skills and competencies taxonomy. This makes qualitative (text-based) analysis and operations expensive and slow: a lot of data mapping and polishing must be done before even simple analysis. Way too often this taxonomy won't even follow internally the business unit's vocabularies and creates upskilling, hiring, and outsourcing automation challenges making the talent pipeline working in silos, internally and externally.
Headai has developed Machine Learning algorithms that can automatically polish and map all text data into a similar abstraction layer. This enables automated qualitative analysis like:
- Scoring the candidates for jobs only based on text data
- Finding gaps in a company's talent pool
- Showing signals and trends for future talent demand
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