Hennepin County, a local government organization in Minnesota, is finding new ways to serve and meet the needs of its residents. The county has an opportunity to explore and increase its investment in data analytics and data science to support data-informed decision-making, ultimately improving the outcomes for residents. Decision-makers need access to the right insights at the right time to make beneficial programmatic and policy decisions. Internal research suggests that leaders are mostly unaware or cannot find many of the already developed reports, which presents a need for increased transparency in the reporting being produced across the organization. Using audit log data collected from a selection of PowerBI workspaces, a proof-of-concept item-based collaborative filtering recommendation engine was proposed as a possible solution to this accessibility problem. The algorithm was chosen for its explainability and interpretability. The resulting recommendation engine provided a personalized list of reports to a user using a calculated Pearson’s correlation coefficient to determine what reports a decision-maker may be interested in based on what reports others have viewed. The next steps include continued development of the recommendation engine and demonstrations that will engage leadership further, resulting in increased investment in data analytics and data science, leading to a higher likelihood of data-informed decision-making and the cross-pollination of data insights. Decision-makers will have better access to the insights that can be used to improve the outcomes for residents of Hennepin County.