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Analytic Provenance in Practice: The Role of Provenance in Real-World Visualization and Data Analysis Environments

Practical data analysis scenarios involve more than just the interpretation of data through visual and algorithmic analysis. Many real-world analysis environments involve multiple types of experts and analysts working together to solve problems and make decisions, adding organizational and social requirements to the mix. We aim to provide new knowledge about the role of provenance for practical problems in a variety of analysis scenarios central to national security. We present the findings from interviews with data analysts from domains, such as intelligence analysis, cyber-security, and geospatial intelligence. In addition to covering multiple analysis domains, our study also considers practical workplace implications related to organizational roles and the level of analyst experience. The results demonstrate how different needs for provenance depend on different roles in the analysis effort (e.g., data analyst, task managers, data analyst trainers, and quality control analysts). By considering the core challenges reported along with an analysis of existing provenance-support techniques through existing research and systems, we contribute new insights about needs and opportunities for improvements to provenance-support methods.