Workday implementations are full of moving parts, so it makes sense that teams try to simplify wherever they can. One common assumption I hear is that data quality is already covered under data validation. It sounds right at first, but in practice, that shortcut creates one of the biggest risks in the entire project.
Data Validation Is Not the Same as Data Quality
A successful load only tells you the file met a format requirement. It does not confirm the values are correct, complete, or meaningful. A null value can load cleanly and still be entirely wrong for how your institution operates. Even careful spot checks have limits, because they are not designed to prove accuracy at the scale and nuance a student system requires.
The Real Test Is What the System Produces
In Workday Student, many of the most important outcomes are not simple fields you load into the system. GPA, institutional credits, and transfer credits are outputs driven by legacy history, configuration choices, and policy rules working together. That means you can load data that looks fine, but still get results that are incorrect once Workday applies logic and calculates what users actually depend on.
When those outputs are wrong, the impact is not minor. It affects advising, eligibility, degree progress, financial aid decisions, and the confidence staff have in the system on day one.
The True Owners of Data Quality Are Often Not IT
Data quality is not just technical work. The people who understand what the data should mean and how it should behave are often in the Registrar’s Office and Institutional Research. They know the exceptions, the edge cases, and the policy level nuance that makes student data so complex. Many problems show up as technical defects, but the root cause is usually translation. It is the gap between legacy rules, institutional policy, and new Workday logic.
Why This Deserves a Real Workstream
Data quality work is deep, complex, and critical, yet it is often treated like a side task or an end phase checklist. It deserves structured ownership, clear time allocation, and visibility alongside the other major workstreams. When you treat data quality as its own effort, you reduce risk, protect student outcomes, and help your campus trust what the system produces.
Strong data quality is not just about clean data. It is about confident decisions and stable operations.
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