Applying Knime Data Mining Tool to Analyse the Job and Self-satisfaction Data

Abstract

With over four hundred thousand foreign workforces in Finland, the concerns for job as well as self-satisfaction has been growing ever since. Job and self-satisfaction have been studied for a long time. Various findings were obtained from these studies. However, the tool to analyse these data has been different and been evolved from time to time. From statistical to data mining where various tools are available for data analysis which not only display various hidden patterns inside the data but also with help of the neural engine and AI is able to predict several outcomes based on data itself. Knime being such a tool is used for data analysis for this thesis.

Results of the study find out various factors for self and job satisfaction as well as how different elements such as gender, country of birth, academic qualification and employment status plays a vital role in various life and job-related factors such as social isolation, failure, entitlement and emotions. The result also establishes a connection between these factors and figure out how they are correlated and up to what extent.

With the number of workforces predicted to grow in coming future also, the discussions about job and self-satisfaction will stay. Thus, the research aims to be useful for further study as well as various policy makings where they can relate the importance of various factors and their correlations with each other to establish a better job as well as self-satisfaction for the workforce.