Upskilling from adjacent skills is more efficient
Gartner TalentNeuron™ research — analyzing billions of job postings — shows that NLP skills are closely related to the skills required to be successful in Python, topic modeling or machine learning. Given the proximity, we can assume that an employee proficient in machine learning, Python or TensorFlow is more likely to learn NLP quickly than someone without those related skills — making it more efficient to upskill them even if they have no prior NLP role experience.
While these direct adjacencies offer some new opportunities to fill skills gaps within a single domain like IT, the real potential of the adjacencies approach lies in identifying and leveraging stepping-stone skills — those that bridge the gap between domains.
By understanding this connection, HR leaders can look to one part of the organization to fill open positions in another, seemingly unrelated part of the organization. Consider the NLP example.
Learn more: How to Use Analytics to Predict Skill Needs
From social listening in marketing to NLP in IT
As the figure below shows, Python is directly adjacent to NLP within the IT domain, but there is also a complementary skill set in marketing: Sentiment analysis.
Sentiment analysis bridges two discrete collections of skills, and provides a stepping stone from marketing skills to IT skills. Specifically, a marketing employee with social listening skills is more likely to be familiar with, and ideally suited for, upskilling into sentiment analysis. From there, it’s a more direct progression to NLP skilling.
By exploiting this adjacency, HR can expand its pool for upskilling and recruiting to target marketers for NLP roles, instead of looking only in the more competitive IT domain.