Quality in, Quality Out: Getting Data Conversion Right in Workday Student

Implementing Workday Student is no small feat. With real-time processing, cross-functional design, and robust automation, it’s the most powerful student information system on the market. But those same features also make it one of the most intense systems to implement.

Amid the excitement of this multi-year transformation, institutions must stay grounded in one critical truth:

Your system is only as good as the data you load into it.

That’s where Data Conversion (DC) comes in. This essential workstream—focused on extracting, transforming, and validating your legacy data for Workday Student—can make or break your implementation. And yet, it’s often one of the most overlooked or under-resourced areas of the project.

What Is Data Conversion (DC)?

During implementation, every piece of data you want to put in Workday Student must be:

• Extracted from your legacy system(s),
• Converted into Workday’s defined file formats,
• Validated for accuracy.

There are separate files for everything: student demographics, course registrations, transfer credit, degree honors, and so on. Each file goes through the same process of extraction, conversion, and validation multiple times over the course of the implementation.

And because Workday Student operates in real time, there’s no batch loading grace period post go-live. You need the right data on day one.

Symptoms of an Under-Resourced Data Conversion Effort

If your DC team is overwhelmed or misaligned, you’re not alone. Here are common red flags:

❌ DC team unaware of timeline or dependencies

• The project timeline is dense with jargon, a dizzying set of due dates, and unclear iterations and data dependencies.
  • Without a clear understanding of how each file connects to each other and the broader implementation, prioritization becomes guesswork.

❌ Dirty data isn’t being cleaned

• Queries surface issues in the legacy system that should be remediated before the data is extracted—but if there’s no time or collaboration to clean the data, bad records get loaded.
  • This erodes confidence in the new system and can lead to costly rework.

❌ Same team handles DC and integrations

• Writing and refining dozens of DC queries and building countless integrations at the same time is unsustainable.
  • Without more staffing or third-party support, quality drops and burnout looms.

❌ Functional decisions don’t reach the DC team

• Functional teams constantly revise decisions as they learn the system. There are too many factors involved to accurately anticipate all of the downstream impacts up front, and Workday is constantly evolving and improving the system.
  • If there’s no formal communication loop between the functional and DC teams, changes aren’t reflected in the files—leading to potential errors in the system and confusion across the project.

How to Resource and Structure Your DC Team

Whether you’re in early planning or deep into implementation, here’s how to set your Data Conversion team up for success:

✅ Build a cross-functional DC team

• Involve colleagues from IT, Records, and Institutional Research.
  • IT can write the queries and convert results into the specified formats,
  • Records experts know what the data means and where it should go,
  • IR ensures it aligns with external reporting and validation expectations.

✅ Dedicate project management

• Your IT, Records, and IR experts need a project manager to coordinate the overall DC efforts.
• Ensure someone from the DC team is present at every other workstream and vice versa—or build a reliable method to communicate decisions and changes across the teams.

✅ Create an internal QC workflow

• Develop an internal process to pull, review, and clean files before submitting to your implementation partner to load into the tenant.
• Track internally identified errors alongside your implementation partner’s error logs with a plan for resolving the errors across iterations.

✅ Respect query “lock” points

• Agree on dates when queries are frozen and develop a plan for addressing any additional issues that arise after.
• Resist the urge to make last-minute query tweaks; they often break more than they fix.
• Instead, identify what you can fix manually in the files during each iterative submission and what you need to fix post-go-live (in Workday) and document those clearly.

✅ Validate, validate, validate

• Just because a file loaded without error doesn’t mean it’s right.
• For example: a degree honors file might load fine but attach honors to the wrong program.
• Build validation reports to pull the data back out of Workday and verify accuracy—during every workset. Many reporting problems can be avoided by ensuring the data itself is accurate.

Already Live? Here’s What You Can Do Now:

• Celebrate your progress. You’ve done something extraordinary!
• Triage issues. Focus first on critical operations (like registration) and never stop testing.
• Keep improving. Workday is flexible; you can clean and configure post-go-live to allow your system to grow and evolve with you.

Not sure where to start? That’s where we come in.

At Kognitiv, we’ve helped dozens of institutions make the leap to Workday—and supported just as many on the other side of go-live. Our advisors can help you:

• Optimize your data conversion effort
• Identify and resolve critical data issues
• Improve collaboration between teams
• And most importantly—build trust in the system by ensuring your data is accurate and reliable

Contact us today for a free evaluation to help you get started. Whether you need a second set of eyes, temporary staff augmentation, or a full advisory partnership, we’ve got your back.