Analytics Based Approach

Legacy solutions use general rules to assess an individual and thus counselors have to rely on their intuition and experience to make recommendations. Latest research in career decision making, however, recommends the use of non-linear models (data driven) and learning systems for making recommendations. A significant advantage of the comparison of profiles statistically (rather than instinct based) is that both strengths and weaknesses are recognized and considered in career recommendations. Since O*NET provides data on several variables for careers across the world of work, a data driven approach is feasible.

The Tucareers’ analytics model, based on the theoretical context that O*NET provides has significant value adds, some of these area

Analytics Based Approach

 Better descriptive capabilities as Tucareers’ solution makes statistical profile comparison of an individual on a comprehensive set of variables across the entire world of work, filtering and short listing careers that best suit an individual

 A unique theory driven prediction model for determining an individual’s satisfaction, tenure and life time earnings in different careers

 A prescriptive model for use in constraint scenarios (e.g. group of participants for deployment of a team in the industry or for selection of streams / electives in a school / college). The assessment results can be used for providing optimal and stable recommendations

The analytics and the model driven approach as proposed for O*NET in our research, is a unique innovation in the area of career decision making research. We also do use artificial intelligence and machine learning algorithms for recommendations based on cultural factors; however as we already leverage data in O*NET for the basic recommendations, we are not dependent on any fancy (& untested, unproven) algorithms for critical decisions that have life impacting implications