## 4 Results
Appendix A presents the equipment inventory landscape, helping contextualize potential sources of technical issues. Appendix B highlights the distribution of personnel and contact structures, which is essential for understanding user demographics and support routing. Appendix C details the distribution and temporal trends of technical assistance requests (PATs), including their classification into semantic clusters and priority levels, providing actionable insights for proactive resource allocation and service improvement. The set of dashboards presented in the Appendices A-C offers a multidimensional analysis of informatics infrastructure, workforce distribution, and technical assistance trends within the public sector. Appendix A reveals a substantial concentration of generic equipment (38,311 units), yet computers (10,751 units) and printers (1345 units) represent critical assets for daily operations. The equipment status breakdown highlights that a large portion of assets are either new or actively in use, though a non-negligible share remains decommissioned or not repairable, suggesting potential bottlenecks in lifecycle management. Appendix B shifts focus to the human dimension, showing a predominantly female workforce and emphasizing the need for gender-aware support strategies. It also outlines communication channels, with email and location data as the dominant contact types, reinforcing the importance of centralized and updated staff directories. Appendix C delves into complaint management via PAT records, showing that medium-priority requests dominate, particularly from large units such as DRA and DRPA. The cluster analysis identifies key issue types--especially software reinstallation (36.22%) and startup/equipment reassignment (31.81%)--while the temporal distribution highlights a sharp increase in PATs after 2015, followed by a recent decline. Collectively, these dashboards provide a holistic view of operational demands and areas for optimization in technical support and digital asset management. Figure 8: KPI performance improvements across CRISP-DM iteration stages. In addition to evaluating classification and clustering performance, we examined the downstream impact of the dashboard deployment on operational outcomes. Notably, a 27% reduction in repeated complaint categories was observed in the six months following deployment. To investigate whether this reduction could be causally attributed to the intervention rather than to temporal variation or exogenous factors, an interrupted time series (ITS) analysis was conducted. Complaint frequency per category was aggregated weekly over a 12-month window, with the dashboard introduction defined as the intervention point. A segmented regression model was applied to detect changes in level and slope, accounting for autocorrelation and seasonality. The analysis revealed a statistically significant level change (\(p<0.01\)) and a negative post-intervention trend (\(p<0.05\)), supporting the hypothesis that the observed reduction is associated with system deployment rather than coincidental fluctuations. This strengthens the causal interpretation of the intervention's effectiveness in reducing redundant complaints by improving feedback clarity and issue resolution efficiency. To further substantiate the impact of the dashboard deployment, we conducted an interrupted time-series (ITS) analysis to evaluate whether the observed reduction in repeated complaints could be causally linked to the intervention. Weekly counts of semantically repeated issues were aggregated over a three-year period, with the dashboard rollout in early 2023 serving as the intervention point. As shown in Figure 9, the ITS model identified a statistically significant level shift following deployment, along with a continued decline in the post-intervention slope. The red trend line represents the segmented regression fit, with shaded 95% confidence intervals.