3 SPEC Kit 360: Learning Analytics dedication of staff to positions supporting learning analytics initiatives suggests the expansion of this type of metric as well as administrative perception of the importance of these efforts. Responses about institutional data storage for learning analytics data revealed a variety of storage strategies. Respondents had the option to select multiple answers. The most common LA data storage location is a central data warehouse (41 responses, or 80%). Almost as many respondents (38 or 75%) reported that data is stored by the unit that collects it. Nearly 20% of respondents indicated that they store data in a distinct learning analytics warehouse and 20% also indicated that another repository was being used. Respondents were asked to identify what types of library data are collected and whether this data is collected with or without individual patron identifiers. The most commonly reported types of data captured concern patron interaction with library staff (research consultations, reference, instruction) and materials usage (print and electronic). These data points are being gathered by more than 80% of respondents. The majority of respondents indicated that individuals (via unique identifiers) are not tracked. The two highest measures tracked with personal identifiers are tied to collections: physical circulation and interlibrary loan information. While students are unlikely to be directly identified for coursework and workshop attendance, individual information is required for delivery of materials. Library Practices (Q7–Q12) Questions about library practices covered who participates in collecting and analyzing information and data retention durations. All of the respondents reported that staff librarians gather library LA data, which corresponds with the regular collection of reference and instruction data. There was also nearly universal agreement that librarians are engaged with analyzing the data (43 responses, or 96%). Non-librarian staff also frequently gather LA data (40 or 89%), though fewer are engaged in analyzing the data (29 or 64%). Fewer than half of the responding institutions reported having a records-management schedule or policy that controls the retention of learning analytics data. The survey results did not show a difference between public or private institutions regarding the existence of a retention policy. Only two institutions indicated that their policies had changed because of learning analytics. Text responses about the duration for library learning analytics data retention reveals that institutions that do not have a retention schedule are more likely to keep data indefinitely. At institutions that have a policy, retention periods vary by data type and collection purpose, with periods between one month (electronic vendor data) to ten years (circulation) data being reported. Library and Institutional Data Sharing (Q13–Q24) Fewer than half of respondents reported sharing data with other departments on campus or to a central warehouse, although twenty percent did indicate that they were planning to begin doing so within the next 6–12 months. Interestingly, the data most often shared with other departments concerns collections usage— circulation and e-resource usage—rather than data about patron interaction with library staff. This may be tied to the need to provide contextual numbers to obtain more funding for materials budgets whether this is directly related to department or university level learning analytics is unclear. The respondents who indicated that they are not sharing data beyond the library cited privacy and confidentiality as the primary concerns, although lack of resources was also ranked highly. Other reasons included that the library is not seen as a data source and that there are a lack of campus initiatives or requests for the data.