## 7 Discussion
### Feedback from the Evaluation Phase  
During the evaluation phase, three case studies were performed with three institutions using the same version of the model (v1.0). All interactions with the participants were recorded, including workshops and interviews. This enabled a fine data collection in terms of feedback, suggestions, and requests for change. Comments were also gathered from the received feedback forms, as well as the researchers' notes. Each comment was stored and annotated in an Excel file. Figure 6 displays the total number of comments received for each dimension of the model. Comments were classified into three categories: A--rephrasing, B--minor change, and C--no action required. The User capabilities dimension received the highest number of comments, 13 in total, of which only nine led to revisions in the model. From the list of the top-10 dimensions with the most comments, only three are related to the organizational part. The objective of the workshops and interviews was to collect the aspects that were not understood by the users. Unsurprisingly, the technical aspects generated more discussion and requests for clarification. Apart from the absolute value of comments, it is also relevant to analyze the impact of the comments on the model's improvements leading to v2.0. Figure 7 presents a visualization highlighting the parts of the model that were improved (i.e., dimension definition and level descriptions) in blue, as well as the number of A comments received for each part. As expected, the User capabilities dimension was refined in order to solve the inconsistencies commented by the users. Most comments were related to the levels' progression, mentioning that administrative users need to be supported first rather than leaders. The revisions in the Data variety dimension included a clarification in the definition, stating that \"variety\" relates to the different data types in data sources and a simplification of the level progression. Figure 6: Summary of comments received to HE-BI MM v1.0. Most comments about Data velocity focused on whether the dimension covered the need to have analytics updated as close to real-time, or if it was technically feasible to have that. Many participants mentioned that in universities, educational data is generated on a semester or yearly basis (e.g., student questionnaires). As can be seen from Figures 6 and 7, the received comments did not originate a rephrasing and were labeled C (no action). However, we decided to clarify the dimension's definition, stating that \"this dimension refers to the real consumption of existing data for analysis purposes by the BIA users.\" Moreover, the level progression was simplified to monthly (since most financial reports have this periodicity), weekly, daily, and real-time. Analytical applications providing close to real-time data are still scarce in higher education. For instance, Ref. [57] reports on the use of analytics to provide updated information about the occupancy and utilization of spaces and how that was instrumental in constructing resilient and adaptive timetables. In March 2020, due to the COVID-19 pandemic, organizations in general, and also HEIs, were forced to accelerate their digital transformation strategies. During this period, new data-driven products were developed in which the velocity of data analysis was crucial. For instance, analytics were used to efficiently monitor in real-time the usage of learning spaces or to track quarantine and isolation cases [58]. Given requirement R4, we decided to keep the model focused on innovative use of analytics; hence the velocity of data analysis is an important aspect. All three case studies were completed before the pandemic. We believe that, currently, all three universities would report differently in this dimension. Comments received for the Architecture and Technical integration dimensions resulted in a clarification of the level progression stages.