Please use this identifier to cite or link to this item:
http://ir.lib.seu.ac.lk/handle/123456789/3799
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Suryanarayana, Alamuri | - |
dc.date.accessioned | 2019-10-21T06:36:02Z | - |
dc.date.available | 2019-10-21T06:36:02Z | - |
dc.date.issued | 2018-12-20 | - |
dc.identifier.citation | 7th Annual International Research Conference - 2018, on “Enhancing green environment through innovative management approach", p.20. | en_US |
dc.identifier.issn | 2536-8869 | - |
dc.identifier.uri | http://ir.lib.seu.ac.lk/handle/123456789/3799 | - |
dc.description.abstract | Big Data, Analytics, Predictive Analytics (PA) have made their way into the world of Business in general and Human Resource Management in particular. Today, they have even gained an entry into Board rooms and business meetings as well. PA has immense potential to offer game changing actionable insights into the entire gamut of HR planning activities. In total contrast to the traditional descriptive analytics using tables, reports, ratios, metrics, etc., PA equips firms to analyze the past and attempts to discern trends in key HR-centric data. However, most companies woefully lack a holistic and consistent view of their HR and the incredible power of HR analytics to attempt and achieve employee force optimization. This review of literature-based Paper discusses the issues and challenges involved in using PA and Predictive Retention Modeling as a key component of HR analytics strategy to compete better and secure business excellence through analytic capability. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Faculty of Management and Commerce, South Eastern University of Sri Lanka. | en_US |
dc.subject | Predictive analytics | en_US |
dc.subject | Workforce intelligence | en_US |
dc.subject | HR mandates | en_US |
dc.subject | Predictive retention modeling | en_US |
dc.subject | Actionable business insights | en_US |
dc.title | Human resource planning using predictive analytics | en_US |
dc.type | Other | en_US |
Appears in Collections: | 7th Annual International Research Conference - 2018 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
AIRC 2018 FMC Page 36-36.pdf | 198.6 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.