As universities begin to implement business intelligence tools such as end-user reporting, data warehousing, and dashboard indicators, data quality becomes an even greater and more public issue. With automated tools taking nightly snapshots of the database, the faulty data grow exponentially, propagating as another layer of the data warehouse. When the problem of data quality is thrust into public forums, the natural tendency is to cast blame, and because the data typically originate with the campus's enterprise resource planning (ERP) system, the fault must lie in the keepers of that system: the IT department. The author believes that the data-quality problem is neither an IT/technical problem nor an end-user/functional problem; rather, it is a "universal/university" problem that requires dedicated resources to which none of these aforementioned areas can commit. Therefore, once bad data are introduced into the system, they typically remain: "Garbage in, garbage stays." In this article, the author discusses how ERPs can assist in minimizing data-quality issues by taking a different, more proactive approach than the existing, reactive model. He suggests a new model for ERP systems at the data-entry level that allows for the flexibility of university-specific business logic while maintaining data integrity and accuracy. With this model, technology is leveraged to produce data that are as clean as possible at the initial point of entry.