Over the past half-century, data-based research in paleontology has increasingly assumed a prominent role. It is widely acknowledged that contemporary scientific research has entered the era of Big Data. However, owing to the inherent characteristics of non-laboratory disciplines, the rate of production of paleontological data resources is limited, making it challenging to align with the fundamental characteristics associated with Big Data temporarily. Nevertheless, the era of Big Data and its associated concepts have clearly exerted a positive influence on paleontology. For instance, recent years have witnessed the diversification of data output in paleontology, along with the inherent complexity of mathematical methods and models, which are closely linked to this era. This article, primarily based on the author’s research background, offers a concise overview of the three key stages in the history of quantitative paleontological research. Simultaneously considering the commonalities among paleontological data, it categorizes paleontological data within the context of Big Data as structural, semi-structured, or non-structured, while also providing an introduction to fundamental research methodologies. Following a discussion of the similarities and differences between the two major research perspectives of quantitative paleontology and analytical paleobiology, the article emphasizes the advantages of analytical paleobiology’s research methodologies and statistical models over traditional statistical approaches. In recent years, paleontology has unmistakably displayed characteristics indicative of data-driven research. However, a model-driven research perspective may be necessary. The methodology combines top-down model design with bottom-up data collection and analysis could ensure the sustainability of paleontological data research. Furthermore, given that paleontology is not inherently a data-intensive discipline, its collaboration with data from other geoscientific fields, will in turn promote the interdisciplinary growth of paleontology. Finally, the latest developments in the field of statistics also warrant the attention of paleontologists. The selection of appropriate statistical models and the nuanced interpretation of data should account for the inherent complexity and potential multiple solutions within paleontological studies. Particular caution should be exercised when identifying causal relationships related to statistical significance.