Abstract
This paper carries out a lightweight review to explore the potentials of data science in the last two decades and especially focuses on the four essential components: data resources, technologies, data infrastructures, and data education. Considering the barriers of data science, the analysis has been mapped into four essential components, highlighting priorities and challenges in social and cultural, epistemological, scientific and technical, economic, legal, and ethical aspects. As a result, the future development of data science tends to shift toward datafication, data technicity, infrastructuralism, and data literacy empowerment. The data ecosystem, at the macro level, has also been analyzed under the open science umbrella, providing a snapshot for the future development of data science.
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