Data analytics competency framework for solutions consultant
Nguyên, An Thi Thien (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025121536419
https://urn.fi/URN:NBN:fi:amk-2025121536419
Tiivistelmä
The thesis is inspired from the demanding job market where a variety of new job types emerged in the information technology era, while job seekers would need a good preparation for their job application. Learning about a specific job background in advance and having a good self-reflection may support the young to achieve success in their career path. The findings from the thesis are hoped to provide some useful insights to different sorts of audience – students, recruiters, or academic researchers.
The objective of the thesis is to find out and build up a competency framework, which includes the key competencies essential for a solutions consultant working in the specific context – data analytics. Based on the literature review, the audience would have the general understanding of competency and competency framework as well as the definition of data analytics and solutions consultant. With data collected from the industry experts, the thesis delivers to the audience a framework which describes the competencies in demand for consulting roles in data analytical context, based on the years of working experiences and the job profiles. Furthermore, challenges to bridge the gap between a data analytical and consulting position are also revealed.
The study has investigated into several established frameworks, such as the data science competency framework by Data to Decision CRC organization, and the modelling theory built by Klee, Janson & Leimeister, and the comprehensive guide to becoming a successful solutions consultant by TalentGenius. By integrating these with professional standards in consulting practice, the research develops a tailed competency framework with three main domains – technical competency domain, business acumen, and core competency domain.
The mixed research methodology – quantitative and qualitative, is applied using the survey and interviewing methods to collect the data for the thesis.
As a result, thirty-one responses are collected from the survey, and six interviews are conducted with professionals who are experienced in the similar field. The survey results demonstrated a strong internal consistency based on Cronbach’s alpha value and exploratory factor analysis revealed a three-factor structure consistent with the theoretical framework, supporting construct validity.
The demarcation of the thesis is the tight demand on the number of responses to build up a stronger dataset for the analysis. Yet, this can also open the chances for later studies to improve and explore the topic from another level of sample size as well as profession diversity.
The objective of the thesis is to find out and build up a competency framework, which includes the key competencies essential for a solutions consultant working in the specific context – data analytics. Based on the literature review, the audience would have the general understanding of competency and competency framework as well as the definition of data analytics and solutions consultant. With data collected from the industry experts, the thesis delivers to the audience a framework which describes the competencies in demand for consulting roles in data analytical context, based on the years of working experiences and the job profiles. Furthermore, challenges to bridge the gap between a data analytical and consulting position are also revealed.
The study has investigated into several established frameworks, such as the data science competency framework by Data to Decision CRC organization, and the modelling theory built by Klee, Janson & Leimeister, and the comprehensive guide to becoming a successful solutions consultant by TalentGenius. By integrating these with professional standards in consulting practice, the research develops a tailed competency framework with three main domains – technical competency domain, business acumen, and core competency domain.
The mixed research methodology – quantitative and qualitative, is applied using the survey and interviewing methods to collect the data for the thesis.
As a result, thirty-one responses are collected from the survey, and six interviews are conducted with professionals who are experienced in the similar field. The survey results demonstrated a strong internal consistency based on Cronbach’s alpha value and exploratory factor analysis revealed a three-factor structure consistent with the theoretical framework, supporting construct validity.
The demarcation of the thesis is the tight demand on the number of responses to build up a stronger dataset for the analysis. Yet, this can also open the chances for later studies to improve and explore the topic from another level of sample size as well as profession diversity.