A successful SaaS migration starts with clean, reliable data — and that rarely happens by accident.
One of the most common mistakes in digital transformation is underestimating the effort involved in preparing data for migration. It’s tempting to focus on the exciting features of the new platform and assume the data will “just work” once imported.
In practice, bad data leads to broken workflows, corrupted reports, frustrated users, and blown-out timelines (and budgets). The earlier you tackle data quality, the smoother your migration will be.
Why Data Prep Matters
Every system, no matter how well maintained, builds up inconsistencies over time. Duplicate records, outdated contacts, mismatched formats, and missing fields are more common than most teams realise, until they try to move that data into a new system.
SaaS platforms often require stricter validation than legacy tools. Fields that were optional before may now be mandatory. Values may need to align with drop-down lists or reference data. Formats might need to match exact patterns. And relationships between records (like clients and contacts, or jobs and invoices) need to be preserved with referential integrity.
If your data isn’t prepared before migration, the process might fail silently or discards important records — or worse, load incorrect or corrupted data that’s hard to detect until go-live (and much more expensive to fix).
The Core Steps: Clean, Enrich, Validate
A good data migration process includes more than just a file export and import. At Biz Hub, our standard approach includes three key phases before any data is loaded into a new system.
1. Cleaning
Remove duplicates, fix invalid entries, and identify missing or inconsistent data. Some examples of this are:
- Merge duplicate clients with similar names but different IDs
- Standardise inconsistent date formats or phone numbers
- Flag records with invalid postcodes or ABNs
2. Enriching
Add valuable information that may be missing or incomplete. For example:
- Match business names to verified ABN lookup
- Add email or phone numbers via CRM integration
- Geocode addresses to prepare for mapping or reporting tools
3. Validating
Ensure data matches the format, rules, and constraints of the new system. Some examples of this are:
- Apply business rules to required fields (e.g. invoice dates must be in the past)
- Ensure foreign key relationships are preserved (e.g. every contact is linked to a client)
- Run dry runs to test import validity before final load
We perform these steps iteratively, reviewing the complete migrated data in the destination environment to catch edge cases and outliers early — rather than waiting until just before go-live.
The Tools and Methods We Use
Biz Hub’s data migration process is powered by Wildcat, our semi-automated migration tool designed for legacy-to-modern platform transitions.
Wildcat supports:
- Structured scanning of legacy systems (Access, Oracle, SQL Server, Excel, CSV)
- Visual field mapping and transformation rules
- Data preview and staged loads
- Validation reports with error highlighting and outlier detection
- Re-entrant loads to support multiple test rounds without full resets
Because Wildcat supports early full loads, we can start refining data quality within days—not weeks—and provide stakeholders with realistic system previews using real data. This accelerates UAT, shortens the feedback loop, and surfaces data issues early when they’re still easy to fix.
Preparing for a Smooth Transition
When done right, data migration can be one of the most powerful parts of a SaaS transition. It’s a chance to leave behind messy workarounds and start fresh — but with your historical context intact.
We’ve worked with clients holding 20+ years of business records, including compliance data, hundreds of gigabytes of attachments, and deeply customised reference tables. With the right planning, all of that value can be retained — without polluting your new system with bad legacy habits.
Where to Next?
If you’re starting to explore your SaaS migration, don’t leave data prep as an afterthought. It should be front and centre from the start.
In our next article, we’ll look at how to keep decades of business data intact — and useful — as part of a modernisation project.
