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reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)© The Author(s) 2020. [http://journals.sagepub.com/home/ero](http://journals.sagepub.com/home/ero)  \nFrom Democratic to “Need to Know”: Linking Distributed Leadership to Data Cultures in the Florida College System  \nRebecca L. Brower  \nChristine G. Mokher  \nTamara Bertrand Jones  \nBradley E. Cox  \nShouping Hu  \nFlorida State University  \nThis multiple case study examines the extent and ways in which leaders and administrators in Florida College System (FCS) institutions engage in distributed leadership through data sharing with frontline staff. Based on focus groups and individual interviews with administrators, faculty, and staff (659 participants) from 21 state colleges, we found a continuum of three data cultures ranging from democratic data cultures to blended data cultures to “need to know” data cultures. We triangulate these results with survey data from FCS institutional leaders and find considerable variation in the extent of data sharing and perceptions of effectiveness of institutional data use. Institutions with democratic data cultures tended to have distributed leadership that encouraged information sharing and collaboration among staff to use data to inform change. Need-to-know institutions faced challenges, including weak data quality, concerns about adequate time and resources among staff for reviewing data, and perceptions that staff lack data literacy skills.  \nKeywords: case studies, community colleges, data utilization, decision making, educational policy, focus group interviews, in-depth interviewing, information processing, leadership, mixed methods, survey research  \nAmong the most pressing problems in community colleges nationally has been the need to improve student outcomes ina higher education sector that serves 10 million students annually yet graduates less than 40% of these students within 6 years (Bailey et al., 2015). In response, many reform efforts have been enacted to target at opportunity structures (e.g., performance-based funding and articulation agreements), institutional practices (e.g., career pathways and learning communities), and students (e.g., early assessment programs and performance-based scholarships; Goldrick-Rab, 2010) . One commonality among all these postsecondary reforms is reliance on data to track student progress and assess program or policy effectiveness. These efforts have been furthered by extensive investments from state and federal governments in developing comprehensive data systems linking K–12 and higher education, which have been lauded for their potential to drive research that will promote innovation (Blume et al., 2014), enhance accountability (Ewell, 2011), and lead to improvements in student outcomes such as the reduction of income-based attainment gaps (Papay et al., 2015) . Yet several studies in recent years have begun to question the empirical link between data practices and improved student outcomes and experiences (Banta & Blaich, 2011; Cox et al.,  \n2017; Hamilton et al., 2009; Marsh et al., 2006; Tandberg & Hillman, 2013) .  \nOne reason for the possible disconnect between datadriven decision making and student outcomes has been that data practices have been poorly implemented in higher education institutions (Cox et al., 2017) . Several issues related to poor implementation have been proposed. First, scholars have questioned the quality of institutional data in higher education, particularly self-reported data and information from students (Adams & Umbach, 2012; Chen, 2011; Saxet al., 2003). Also, leaders may not involve employees at all organization levels in data practices because frontline academic employees often lack the time, resources, and understanding of data to effectively utilize data in everyday practice (Blaich & Wise, 2","cbCaigCXXoYQ196Y","https://ap.wps.com/l/cbCaigCXXoYQ196Y","pdf",615861,15,"English","# Distributed leadership and data cultures\n## Three data cultures in Florida College System institutions\n## Implementation challenges in “need to know” settings","[{\"question\":\"What does the study examine about distributed leadership and data sharing?\",\"answer\":\"It examines how leaders and administrators in Florida College System institutions engage in distributed leadership by sharing data with frontline staff.\"},{\"question\":\"How many data cultures does the study identify, and what are they?\",\"answer\":\"The study finds three data cultures along a continuum: democratic, blended, and “need to know.”\"},{\"question\":\"What challenges do “need to know” institutions face?\",\"answer\":\"They report weak data quality, concerns about staff time and resources for reviewing data, and perceptions that staff lack data literacy skills.\"}]","From Democratic to “Need to Know”: Linking Distributed Leadership to Data Cultures in the Florida College System | PDF",38]