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Data Governance Strategy for Hastings Deering
This project aims to conduct a data governance gap analysis of Hastings Deering’s data. The focus of this project is on data quality, privacy, ethics, and security concerns. The goals are to establish a lifecycle for data, identify high priority data points, understand current data governance practices, and develop techniques for their automation.
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Data Quality Root Cause Analysis: Patient Journey in ED
This project aims to identify the root causes for data quality issues in the process data capturing the patient journey process in the Emergency Department of an Australian hospital. Related Publications: Digital Health Data Quality Issues: Systematic Review Digital Health Data Imperfection Patterns and Their Manifestations in an Australian Digital Hospital
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Event Log Quality & Gamification
Data quality is critical for efficient and low-risk data-driven decision-making in organizations. Process mining concerns the analysis of event logs to provide a better understanding of the real processes executed within an organization to support decision-making. Low-quality event logs negatively affect the reliability of process mining results (i.e., garbage in, garbage out). Activity labels, the…
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Praeclarus Process Data Quality (PDQ)
PraeclarusPDQ is an initiative sponsored by the Process Science Group at Queensland University of Technology. It is an open-source framework with the goal of hosting ‘plug-in’ solutions to a multitude of process-data quality problems. This framework development is being conducted jointly by the Process Science Group at Queensland University of Technology (Australia) and the Process…