Name
Abstract | ||
|---|---|---|
Modern organizations face the challenge of constantly evolving skills and an ever-changing demand for products and services. In order to stay relevant in business, they need their workforce to be proficient in the skills that are in demand. This problem is exacerbated for large organizations with a complex workforce. In this paper, we propose a novel, analytics-driven approach to help organizations tackle some of these challenges. Using historic records on skill proficiency of employees and human resource data, we develop predictive algorithms that can model the adjacencies between the skills that are in supply and those that are in demand. Combined with another proposed approach for predicting the learning ability of people based on human resource data, we develop a framework for identifying the propensity of each individual to be successfully re-trained to a target skill. Our proposed approach can also ingest data on manual skill adjacencies provided by the business to augment the predictive modeling framework. We evaluate the proposed approach for a representative set of target skills and demonstrate a high performance which improves further on adding information about manual skill adjacencies. Feedback on preliminary deployment of this approach for re-skilling indicates that a large percentage of employees recommended by the analytics framework were accepted for further review by the business. We will incorporate the observations made by the business to iteratively improve the predictive learning approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDMW.2015.206 | ICDM Workshops |
Keywords | Field | DocType |
human resource, skill adjacency, skills taxonomy, workforce analytics, employee training | Data mining,Predictive learning,Data modeling,Software deployment,Human resources,Computer science,Predictive analytics,Workforce,Knowledge management,Workforce planning,Analytics | Conference |
Citations | PageRank | References |
3 | 0.43 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karthikeyan Natesan Ramamurthy | 1 | 163 | 31.33 |
| Moninder Singh | 2 | 3 | 2.12 |
| Michael Davis | 3 | 3 | 0.43 |
| J. Alex Kevern | 4 | 3 | 0.43 |
| Uri Klein | 5 | 3 | 0.43 |
| Michael Peran | 6 | 5 | 2.17 |