Business-Driven Data Model for Consistent Software Robot Monitoring in 24/7 Environments

Regular monitoring of software robots includes their health and performance monitoring so that they can run effectively and efficiently. In large-scale robotic process automation (RPA), managing quantities of tasks executed by software robots that run 24/7 is critical. It calls for an end-to-end solution comprising planned schedules, service level agreement (SLA) compliance, server capacities, and system outage avoidance strategies. This paper proposes a new business-driven data model for RPA monitoring to enhance the evaluation and maintenance of software robots in 24/7 environments. The literature review confirmed a lack of flexible data models for RPA monitoring with a business orientation, particularly in big environments. The business-driven data model contains three domains, thirteen enumerations, seven entities, and 116 attributes for enabling seamless robot task execution. The quality is measured against five established criteria related to data models. Moreover, the Center of Excellence (CoE) applies the business-driven data model in a Shared Service Center (SSC). The CoE manages over 170 robotized tasks, ensuring efficient operations. This research increases the monitoring and assessment of Robotic Process Automation (RPA) to work effectively and efficiently with software robots. By embracing an enterprise-focused data model, organizations can align RPA initiatives more effectively with business objectives, enhance system visibility, and make more informed decisions.