Supporting the Management of Predictive Analytics Projects in a Decision-Making Center using Process Mining

Abstract

Decision-Making Centers (DMCs) Environment facilitates stakeholders' decision-making processes using predictive models and diverse what-if scenarios. An essential element of this environment is the management of Decision Support Components (e.g., models or systems) that need to be created with mature methodologies and good delivery time. However, there has been a gap in the understanding of project management best practices in DMC environments and in the application of methodologies to ease project execution. In the following paper, we address that gap by analyzing six predictive analytics projects executed in a Mexican DMC using Process Mining techniques. We perform process discovery using a detailed activity event log, which has not been possible in previous studies. Additionally, we perform a compliance evaluation versus the de facto methodology to identify the current process alignment gaps, and finally, we analyze the social networks present in the process execution. The research reveals that (1) process mining models are helpful to address management issues of PA/DM projects (2) PA/DM projects require alignment to mature methodologies to improve process performance and avoid execution problems (3) PA/DM project execution should be revised at the activity level to identify issues and to propose specific strategies. This study's findings can help project managers to perform process analyses and to make informed decisions in PA/DM projects. The following paper is an extension of the article "Applying Process Mining to Support Management of Predictive Analytics/Data Mining Projects in a Decision-Making Center¨ presented in the 2019 International Conference on Systems and Informatics (ICSAI 2019).

Policy Design for Electricity Efficiency: A Case Study of Bottom-Up Energy Modeling in the Residential Sector and Buildings

“…The data collected included activities from four steps of the CRISP-DM methodology: data interpretation, data preparation, modeling, and assessment. Unfortunately, the data pertaining to the business understanding and implementation phases were not readily available [55]. The collected format included the start and conclusion dates of activities, the average number of h dedicated to each task on a daily basis, and the quantity of resources allocated to each activity for the purpose of constructing a model.…”

“…The present set of best practices has been derived from prior research studies that have provided valuable insights into the potentialities within the field of BUEM design [16,55].…”

“…This section presents a comparative analysis between the implementation of the proposed framework and the approach employed in DMC [53,55]. The authors have previously examined this methodology in previous research; however, they have not conducted a quantitative and qualitative comparison with other methodologies.…”