We’re Drowning in Data! How to Increase Coordination and Collaboration Across Campus

Data collection and analysis are required for a number of processes: continuous improvement, program review, assessment, accreditation. A plethora of data is collected by staff and faculty, usually without coordination. Coordination and collaboration among these data collectors would result in a richer data environment and better analysis for decision-making.

Detailed description: Data collection and analysis are required for a number of processes: continuous improvement, program review, assessment, accreditation. A plethora of data in support of these processes is collected by staff and faculty on campus and usually held in separate databases. Discrepancies with data from other sources can result. Data collection is often uncoordinated with individuals unaware of the total data effort across campus. Surveys, for example, may be designed by a department unaware of an already existing institutional effort. In addition, institution-wide data needed for comparisons and general information is not well-known outside of a few centralized offices. Coordination and collaboration among data stakeholders would result in a richer data environment and better analysis for decision-making.

Each campus, whether a community college, state university or private institution generally has an office where data are regularly compiled and analyzed. While these offices may have different names (Institutional Research, Planning and Analysis, Student Research), the basics of understanding data, accessing the institutional databases and providing analysis are the same. These data resource offices are also well-interconnected with key support areas such as Assessment and Information Technology.

A panel of institutional researchers from each segment of higher education (community college, CSU, UC and independent) will discuss strategies for and benefits of increased coordination and collaboration on data with faculty. Ideas for better information dissemination and sharing which result in more standardized, comparable and valuable data will be explored.