Success Analytics from Tucareers.com

HR analytics (also called people analytics) is an upcoming approach which brings an engineering rigor to HR for improving individual and organizational performance. Modern age organizations (e.g. Google) are using the approach extensively (Sullivan, John, 2013) in managing their human resources across applications like resource selection, allocation and given its predictive capabilities even in performance management and attrition control initiatives.

We at Tucareers.com bring this analytical rigor in the personal sphere for career and education related decision making. The “Success Analytics” framework is designed to help discover for a career decision maker the best set of career and education options which can give you the maximum chances of success and satisfaction.  Application of decision theory tools, a structured framework and model based approach helps identify the right environment for an individual to thrive and excel. This environment map ensures that the individual’s strengths, preferred work style and interests are leveraged and keeping one satisfied with ones work. Given the optimal map in the individual and organization’s ability needs the organization is satisfied.  Research also indicates that knowledge and skill building occurs best when an individual discovers ones ‘calling in life’.

The Tucareers.com platform is build on the knowledge base of O*NET (Occupational Information Network), which is USA’s primary source of occupational information. As O*NET also is increasingly becoming used internationally (National Center for O*NET development, 2011) our research shows it can be a good reference in an International context as well.  We provide a unique model driven approach, build on research in career theories and multi criterion decision making to increase the decision value that O*NET provides. Some of the unique sets of offerings included in our model are the following  

 

  • Rich navigation and exploration tools
  • Multi trait assessment and detailed reports
  • Model driven, multi phase approach for selection
  • Predictive framework
  • Evaluation on the cultural dimension
  • Assessment of career difficulties
  • Integration with expert opinion
  • Machine learning

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