Abstract: This paper analyzes the similarities and differences between an ontology (focused on meaning), and a database schema (focused on data). We address questions about purpose, representation, creation, usage and semantics of each. We distill out twenty-five features that characterize these two representational artifacts, the majority of which are relevant to both. Each has a strong semantic heritage using formal logic to build conceptual models of some subject matter. And while there are differences in 90% of the features, the differences are mostly historical, not technical. We identify pros and cons for each, and notice that there is usually no free lunch. The disadvantage that you think you are getting rid of may show up elsewhere in a different and unexpected way. We close by considering how ontology contributes to enterprise data integration. The emergence of using URIs as global identifiers (e.g. in OWL) dramatically enhances data integration as well as schema reuse and sharing. The primary focus on meaning helps ontology break through a lot of unnecessary complexity that exists in large traditional databases and greatly simplifies the process of integration. Ontology is providing a glimmer of light at the end of the tunnel for enterprise-wide data integration.