On many campuses, decisions support teams struggle to encourage campus members to make data-informed decisions. This study provides guidance on how to improve the user-friendliness and accessibility of self-service analytics resources for the campus community.
Download the complete publication or explore the table of contents to learn more about how institutions such as the University of Kentucky, Bucknell University, and York University implemented data-informed decision making on campus.
The goal: Decision support, not data support
Our previous study explored one of the critical competencies required to advance the use of analytics – data management. This study showed that common definitions, trusted quality standards, and timely access to information are all essential components of any data-driven campus.
However, high-quality data is not the only goal. Campus members need that data to make decisions to drive desired outcomes. The underlying data quality and infrastructure, while important, are rarely top-of-mind issues for data consumers. For CIOs, the goal is to ensure that data is actually employed for decision making.
A key challenge: The growing demand for data
Decision support teams (e.g., Institutional Research, Business Intelligence) often struggle to provide true decision support to campus members largely because they are overwhelmed with one-off, low-level requests. Decision support is unable to focus on higher-level tasks, such as how to improve predictive modeling and ensure that people and divisions have the data they need to make informed decisions.
Additionally, campus members can get stuck in “analysis paralysis,” unable to identify the data they should use or how to use it. These individuals often lack the training or support required for data-informed decision making. There is a growing tension between increasing interests in using data effectively and equipping staff with the necessary skills to meet that interest.
The 13 tactics outlined in this study will help you improve the accessibility and use of analytical resources for self-service and central decision support.
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