Your company is collecting more data than ever before. Your dashboards are packed. Yet teams still rely on gut feelings, conflicting metrics, and isolated spreadsheets to make decisions. TL;DR: Most organizations are awash in data but lack the confidence and structure to use it effectively; the real gap is confidence and interpretation, not technical know-how. The seven barriers to data literacy are: misaligned metrics, limited data access, poor executive role modeling, one-size-fits-all training, cross-functional silos, unclear accountability, and the absence of a measurement framework. According to DataCamp’s 2026 State of Data and AI Literacy Report, 88% of enterprise leaders say data literacy is critical, but 60% still see a skills gap in their organization. Databox’s own research shows that only about half of employees are well-trained in data analysis and reporting, and 64.29% of teams need 1–3 days to answer a straightforward business question. Each gap needs a specific remedy: executive-approved metric glossaries, self-service analytics, visible leadership behavior, role-tailored learning paths, unified data sources, designated data champions in each function, and behavioral measurement. Genie, Databox’s AI analyst, boosts data literacy by examining data, surfacing trends, and explaining insights in clear, everyday language — giving non-technical users a confident first experience with live data. Data literacy ultimately hinges on leadership: without executive ownership and visible example-setting, every gap described here will remain, no matter how much you spend on tools or training. Introduction: Most data literacy resources jump to solutions without first identifying the real gaps. Below, you’ll see the seven concrete gaps that exist inside…