• DOI:10.1108/itp-06-2019-0290
• Corpus ID: 216379615

Key competencies for big data analytics professions: a multimethod study

@article{Persaud2021KeyCF,
  title={Key competencies for big data analytics professions: a multimethod study},
  author={Ajax Persaud},
  journal={Inf. Technol. People},
  year={2021},
  volume={34},
  pages={178-203}
}

• A. Persaud
• Published 28 February 2020
• Business
• Inf. Technol. People

Purpose: This study aims to identify the precise competencies employers are seeking for big data analytics professions and whether higher education big data programs enable students to acquire the competencies. Design/methodology approach: The study utilizes a multi-method approach involving three data sources: online job postings, executive interviews, and big data programs at universities and colleges. Text mining analysis guided by a holistic competency theoretical framework were used to derive insights into the required competencies. Findings: We found that employers are seeking workers with strong functional and cognitive competencies in data analytics, computing, and business combined with a range of social competencies and specific personality traits. The exact combination of competencies required varies with job-levels and tasks. Executives clearly indicate that workers rarely possess the competencies and they must provide additional training. Limitations: A limitation is our inability to capture workers’ perspectives to determine the extent to which they think they have the necessary competencies. Practical implications: The findings can be used by higher educational institutions to design programs to better meet market demand. Job seekers can use it to focus on the types of competencies they need to advance their careers. Policymakers can use it to focus policies and investments to alleviate skills shortages. Industry and universities can use it to strengthen their collaborations. Social Implications: Much closer collaborations between public institutions, educational institutions, industry, and community organizations are needed to ensure training programs evolve with the evolving need for skills driven by dynamic technological changes. Originality: This is the first study on this topic to adopt a multi-method approach incorporating the perspectives of the key stakeholders in the supply and demand of skilled workers. It is the first to employ text mining analysis guided by a holistic competency framework to derive unique insights.