Utilizing data analysis to optimize event management experience

Abstract

This thesis studied the application of data analysis to enhance the event management process within a business context. The primary objective is to identify challenges in existing event man-agement operations and develop practical solutions to improve operational visibility, planning capability and service quality. The scope of work was based on a simulated six-month dataset of business events with Microsoft Excel used as the primary tool for data handling and dashboard development.

The theoretical foundation was established through an examination of core principles in data analysis and their application within the event management domain. The framework outlined key event management concepts, common operational challenges and the structured phases of event execution. Concurrently, data analysis processes were explored, with emphasis placed on the critical importance of data quality and security. Structured data analysis was assessed in supporting operational efficiency and informed decision-making in alignment with strategic busi-ness goals in the context of event management industry.

The empirical component focused on developing practical business solutions for a specific case study. Following a review of operational, managerial and systemic challenges within the current processes, the Excel-based event tracker was analyzed across the data analysis lifecycle through data collection, preprocessing, to analysis and visualization. Solutions were developed and implemented following the phases with reflection on business goals. Data collection was enhanced through standardized formats, data validation rules, and clearer input guidance. Data preprocessing and cleansing employed consistent formatting, normalization, handling of missing fields, creation of derived metrics and consolidation of fragmented monthly data into a master worksheet. A data analysis framework was developed, integrating booking rules and determining key performance indicators (KPIs) including room utilization rate, event readiness status and cancellation rate. These facilitated timeliness tracking, potential trend analysis and resource forecasting. Finally, data visualization was implemented through an interactive dashboard with key visualization to translate raw data into actionable and expeditious insights.

The implemented solutions demonstrated enhancement in data quality and primary analytical capability, supporting the event management team in transitioning towards more proactive plan-ning and resource allocation. While the case study utilized a simulated dataset and an Excel cen-tric approach, the applied methodologies and derived insights serve as a valuable model for simi-lar context and a baseline for further developments. Future development pathways have been proposed, including the adoption of more advanced business intelligence platforms such as PowerBI, an evolution towards more advanced predictive and prescriptive analytics, and the strengthening of comprehensive data management and distribution strategies to further optimize event management processes.