LOCATION: SOUTH CAROLINA
DESTINATION: CLARITY
DESTINATION: CLARITY
In South Carolina, our analytics report revealed nuanced differences in leave-taking patterns among employees, highlighting variances by tenure, gender, branch location, and more. We used the programming language Python to manipulate and visualize the data in complex ways to unlock insights that would otherwise be hidden. We provided narrative analysis alongside the visuals so all the library's decisionmakers could understand and act.
Integrated with qualitative survey responses, the report validated the positive direction of the new leave policies and helped contextualize outlier responses. This dual approach of quantitative and qualitative data provided a comprehensive understanding, reinforcing the policy's effectiveness and helping to optimize implementation.
SAMPLE PYTHON CHART
This chart uses simulated data to show an example of the data visualizations used in the report. Here we used Python's data capabilities to create a multi-year average and plot it against the policy results, grouped by custom employee tenure bins.
