Job analysis involves the collection of various types of job data and worker requirements in an organization and this data serves as a foundation for most, if not all, human resource-related decisions. In its most generic form, job analysis data provides a point of reference to the user about the work performed on the job. However, the potential of job analysis data to aid in organizational human resource functions extends far beyond the scope of this use. The possibilities are limitless if it is applied appropriately and used in decision making.
Performing a thorough job analysis and collecting qualitative and quantitative data is the first step in bridging the gaps often present in evaluating job data. All too often, decisions made using job analysis data rely on loose inferences or qualitative hunches rather than allowing the data to be used to its full potential in dynamic and complex ways in order to draw objective conclusions.
Human resource decisions usually revolve around areas that rely on job analysis information such as recruitment, selection, and training. Workforce metrics can assist HR professionals in effectively and efficiently utilizing job analysis data to make these decisions. Metrics use quantitative and qualitative job analysis data and apply step-by-step mathematical formulas, or algorithms, to answer workforce analysis questions. These step by step formulas allow for the transformation of job analysis data into more manageable and easily interpretable components, thus providing a more economical and effective way of using job analysis data. Because job analysis data are often used in personnel-related areas such as job evaluation, performance appraisal, training needs analysis and personnel selection, the use of metrics can aid those people making personnel-related decisions applicable to the workforce, job, or employee of interest.
As an example, an organization could employ workforce metrics that would help them to determine the most qualified individuals for promotion or to determine the skills that an employee needs to be successful on the job. The nature of the algorithms employed and the type of data used in the metrics allow for more objectivity in answering workforce questions, which ultimately leads to more reliable conclusions. To ensure reliability and proper aggregation of data, metrics are referenced against O*NET and utilize typical organizational job analysis information such as worker and job-oriented data. O*NET elements are aggregated together, weighted accordingly, and analyzed together to draw the most meaningful conclusion.
SkillsNET has created the foundation for a methodology that could serve as the future of analyzing job analytic data in applied settings. The user-friendly and innovative metrics created by SkillsNET provide an organization with quantitative means to address a variety of HR issues and aid in decision making. Currently, SkillsNET has developed metrics in areas including 1) organizing and analyzing basic job data, 2) recruitment, selection, and transferability-related data, and 3) training-related data. To date, 65 metrics have been developed to address each area in as many meaningful ways as possible and additional metrics are in development that address the use of complex job and military mission data. Taken together, SkillsNET’s metrics enable the optimization of SkillObject data and allow human resource professionals to arrive at the best workforce analysis decisions for their organization.
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