Big data management skills: accurate measurement

Area of Science:

• Computer Science
• Data Science
• Educational Measurement

Background:

• Big data is transforming business, health, and governance, necessitating new approaches to data management and analysis.
• The increasing volume of archived data raises global issues regarding retrieval, application, and ethical considerations.
• A critical challenge is ensuring professionals possess adequate digital skills to manage and interpret big data effectively.

Purpose of the Study:

• To present an effective and efficient method for identifying digital skill acquisition in individuals working with big data.
• To demonstrate the evolution from traditional statistical measures to a more comprehensive unidimensional scale for assessing proficiency.
• To provide a tool for understanding individual differences in digital skill development.

Main Methods:

• Transitioned from traditional statistical measures for programmer proficiency to a unidimensional scale.
• Employed a methodology that allows for the comprehension of human performance and test item performance relative to each other.
• Developed a comprehensive scale for measuring digital skill acquisition.

Main Results:

• The proposed unidimensional scale offers a more comprehensive approach to measuring digital skills compared to traditional methods.
• The methodology effectively assesses human performance and test item performance in the context of big data skills.
• Demonstrated a clear progression in measuring digital skill proficiency.

Conclusions:

• The developed methodology provides an effective tool for assessing digital skill acquisition in the big data field.
• This approach aids in understanding individual differences in digital skill development.
• Highlights the importance of robust assessment tools for the evolving demands of big data professionals.