Towards a Data Science Framework Integrating Process and Data Mining for Organizational Improvement

Andrea Delgado, Adriana Marotta, Laura González, Libertad Tansini, Daniel Calegari


Organizations face many challenges in obtaining information and value from data for the improvement of their operations. For example, business processes are rarely modeled explicitly, and their data is coupled with business data and implicitly managed by the information systems, hindering a process perspective. This paper presents a proposal of a framework that integrates process and data mining techniques and algorithms, process compliance, data quality, and adequate tools to support evidence-based process improvement in organizations. It aims to help reduce the effort of identification and application of techniques, methodologies, and tools in isolation for each case, providing an integrated approach to guide each operative phase, which will expand the capabilities of analysis, evaluation, and improvement of business processes and organizational data.


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