Integrating Flow and Structure in Diagrams for Data Science

In Data Science, data modeling (including relational databases and big data) has traditionally used Entity-Relationship (ER) diagrams to represent structural characteristics of data. However, ER diagrams lack the capability to capture the flow and transformation of data through analytic pipelines, which are essential to manage modern data science workflows. On the other hand, flowcharts have been used for decades to describe the processing. Based on this motivation, this paper provides a historical perspective identifying the limitations of employing ER diagrams and Data Flow Diagrams (DFDs), separately, emphasizing the need to integrate both solutions. We examine established diagram notations and design models, including traditional models such as UML, ER, DFD, BPMN, and FLOWER. Our literature analysis suggests that integrative diagram approaches can provide a more intuitive and comprehensive understanding of data collection, data integration, and data transformation for big data analytics in the future.