Windows 10 Is Gone. Don’t Let Your Data Migration Be Next Year’s Crisis.

You already know the Windows 10 deadline came and went. If your organization is like most healthcare and life sciences organizations, your Windows 11 and core system migrations are still a work in progress. According to a recent Gartner report, hardware refresh and OS upgrades spiked in 2025, but the real clean‑up—data migration, legacy retirement, and getting systems to agree on providers, patients, members, and products—will stretch well into 2026 and beyond.​​

You are not behind. You are in the 83%

If your migration is late, over budget, or constantly re‑planned, you are in the overwhelming majority. Gartner’s research shows that roughly 83% of data migration projects fail or run substantially over budget, largely because teams underestimate how hard it is to convert legacy data and keep business running at the same time. For healthcare and life sciences, that pressure is magnified by regulatory deadlines, value‑based models, and AI initiatives that all depend on trusted data.​

You probably recognize the pattern. A new claims, EMR, CRM, or research platform is selected, implementation is underway, and only then does someone ask, “But what about the data?” By the time you fully appreciate the complexity with multiple provider files, overlapping member IDs, orphaned encounters, and fragmented research datasets, you are deep into the project and every change feels painful.

Why you may have delayed or stalled

Most healthcare and life sciences leaders know these migrations are unavoidable yet still hesitate to fully commit. Common concerns include:​

• Fear of disruption to clinical or member operations: You can’t afford claims downtime, broken eligibility checks, misrouted referrals, or unavailable study data because a cutover went wrong.​
• Risks to accurate claims adjudication: Even small discrepancies in provider identifiers, contract terms, enrollment status, or benefit configuration between legacy and target systems can trigger incorrect denials, payment errors, and surge call volumes that damage provider trust and member satisfaction.
• Limited SME capacity: The people who understand your provider contracts, benefit designs, study protocols, and data quirks are the same people running day‑to‑day operations, so pulling them into a migration for months is challenging.
• Unclear data ownership and governance: When no one clearly owns provider, member, or research data across business units, even simple questions like “which address wins?” become political and slow everything down.​
• Underestimating data quality problems: It is one thing to know provider data is messy. It’s another to realize the effort required to fix incomplete, inconsistent, or contradictory records across 20+ systems before you can safely migrate.​
• Regulatory and audit anxiety: You know auditors will ask you to reconcile new systems back to retired platforms, but you do not have an easy way to show lineage or prove that balances, enrollments, and relationships stayed intact.​

These are rational reasons to proceed cautiously, but they don’t make the risk go away. They just push it further into 2026, when you will still be running parallel systems, paying extended support, and carrying technical and data debt.​​

The quiet cost of treating migration as “just IT”

When migration is handled as a one‑time technical task that the ETL team will take care of, the real risks tend to show up late. Documentation of legacy systems is often incomplete or wrong, and traditional waterfall approaches crack under constant spec changes and discovery of new data issues.​

Symptoms look like this:

• Data loads pass basic checks, but functional testing reveals benefit mismatches, incorrect provider networks, or missing research cohorts.​
• Every test cycle requires a manual rebuild of transformations, with spreadsheets and SQL scripts scattered across teams, making each refresh slower and less predictable.​
• The go‑live plan does not fully account for frozen legacy systems, late‑breaking updates, or how to keep dual environments in sync during an extended pilot.​

In a clinical or payer context, that can translate into members seeing the wrong network status for a provider, denials that spike call volume, researchers losing confidence in cohort continuity, or auditors questioning whether you can prove that financial and statistical data reconciles back to the old world.​

Turning OS and core migrations into a strategic data moment

The Windows 11 wave and associated core system replacements create a rare alignment. You are already touching devices, identities, and applications, so now is the perfect time to fix the data foundation under them. Instead of letting each new system carry its own copy of provider, member, patient, and other critical data, you can use this window to centralize control and enforce consistent definitions and rules.​​

Gaine’s Health Data Management Platform (Gaine HDMP) is designed precisely for this moment. It combines data profiling, requirements and rules management, cleansing, matching, validation, lineage, and governance into a single environment built for healthcare and life sciences domain complexity.

Since 2007, Gaine HDMP has been used to retire thousands of legacy applications, giving organizations a repeatable way to manage risk and reduce cost across waves of migrations.​

How Gaine helps you de‑risk healthcare and life sciences migrations

Gaine HDMP doesn’t try to replace your ETL or your target platforms. Instead, it orchestrates the entire migration process around them. Key ways it helps:​

• See the reality before you commit: Gaine HDMP profiles legacy data against your “to‑be” model, surfacing gaps, hidden dependencies, and data quality issues early, so estimates are more accurate.
• Treat the specification as a living asset: Instead of static documents, Gaine HDMP maintains your data conversion spec in a controlled repository, links it to validation reports, and tracks every change and its impact across objects and transformations.​
• Make validation and audit preparation part of the plan: Gaine HDMP drives creation of the dozens or hundreds of validation reports you will need, capturing checksums, record counts, and like‑for‑like comparisons between old and new models so you can satisfy both business users and auditors.​
• Support iterative refresh and regression testing: With process control and automation, you can refresh source data, reapply transformations in the correct sequence, and regression‑test results repeatedly without reinventing the wheel each cycle.​
• Embed governance into the migration, not after: Data governance workflows for key attributes, duplicates, and relationship conflicts mean SMEs can make and document decisions in a structured way, building the governance muscle you will need post‑go‑live.​

For you, that translates into fewer surprises late in the project, more predictable cutovers, and a stronger story for regulators and internal risk committees.​

Why 2026 is still the right year to fund this work

Industry forecasts show that a significant share of device and platform upgrade demand was pulled into 2025, but the service and data work does not stop at the Windows 10 end‑of‑support date. Managed service providers and CIO surveys point to 2026 as a key year for finishing migrations, retiring legacy applications, and cleaning up the data those systems leave behind.​

In parallel, healthcare and life sciences organizations are under mounting pressure to:

• Deliver accurate provider directories and networks amid closer CMS scrutiny.​​
• Share data through FHIR APIs and other interoperability mandates hitting the 2026–2027 horizon.​​
• Feed analytics and AI programs with consistent, governed data rather than a patchwork of extracts.​

Those goals all depend on the same thing your migrations depend on: getting legacy data into a cleaner, governed, and shareable shape. That is why treating Gaine HDMP as part of your 2026 budget is less about “one more tool” and more about giving your organization a repeatable way to move off old systems without dragging old problems forward.​

If you are still mid‑migration—or have quietly pushed phases into next year—you are exactly where many of your peers are. The question now is whether you continue to manage that journey through spreadsheets and late‑night fixes or give your teams a purpose‑built platform to handle the process, the people, and the data in a way that finally sticks. Contact Gaine to see if we can help…whether that’s a quick gut‑check conversation, a focused workshop on your data migration roadmap, or a deeper look at how Gaine HDMP can shoulder the heaviest lift so your teams do not have to.