Thousands of enterprises are tackling enterprise data migration right now, and most will fail. Industry research puts the failure rate at 73%, with 80% of projects blowing past budgets by 150% or more and timelines stretching eighteen months beyond initial plans.
The platforms work. The failure sits in execution. Manual code conversion, business-level scoping, governance treated as a final step, and vendor teams billing their learning curves as expertise. These patterns have produced the same outcomes for a decade.
Kanerika’s newly published State of Enterprise Data Platform Migration 2026 report examines what is driving failure and where AI-first automation is producing a different result. In this article, we’ll cover the research findings, the failure patterns, and what a better approach looks like.
Key Takeaways Governance integrated from day one, not bolted on at the end, is the single strongest predictor of post-go-live outcomes 73% of enterprise data migration projects fail to meet their stated objectives 80% of projects that proceed exceed original budgets by 150% or more The top causes are poor technical discovery, platform inexperience, late governance, and manual processes AI-first automation handles 60 to 85% of migration tasks, cutting cost and timeline Kanerika’s FLIP platform has delivered a 95% project success rate across 100+ enterprise engagements
Running an Enterprise Data Migration? Kanerika’s FLIP platform has delivered a 95% project success rate across 100+ migrations.
Talk to our Team
3 Market Forces Pushing Enterprises to Migrate Now The IT services market grew 2 to 3% overall in 2025. Within that, enterprise data migration and application modernization outperformed every other segment at 4.3% year-over-year growth, according to TBR’s Q3 2025 IT Services Vendor Benchmark covering 30 major vendors across a $439.8 billion market. That gap between modernization and everything else is the signal.
3 forces are converging to explain the acceleration.
1. The Informatica Deadline Informatica discontinued on-premise PowerCenter support after 2026. Enterprises that built data integration infrastructure around PowerCenter over the past decade face a hard deadline. Staying on unsupported, unpatched infrastructure carries security and compliance risk that compounds over time. Thousands of enterprises are now in active migration evaluation.
2. The Agentic AI Requirement TBR’s benchmark ranked agentic AI as the seventh most-purchased enterprise technology despite being newly available. Enterprises moving toward agentic AI workforces need modern, structured, accessible data infrastructure. Legacy platforms built around Informatica, SSIS, Crystal Reports, and SSAS cannot support the data pipelines agentic AI requires. Migration comes before AI adoption. There is no workaround.
3. The Post-Deferral Wave Capital budgets were constrained through 2023 and into 2024. Modernization projects were evaluated, approved in principle, and deferred. Those deferrals are ending. CFOs now face an accumulated cost of further delay that exceeds the cost of execution. The consulting segment feeding this work grew 4.0% year-over-year in Q3 2025, after posting only 0.8% growth a year earlier. The release is visible in the numbers.
Why Enterprise Data Migration Projects Fail The 73% enterprise data migration failure rate traces back to a consistent structural mismatch between how projects are sold and how they are delivered. Industry research has identified five contributing factors that appear consistently across failed projects.
1. Inadequate Technical Discovery The most commonly reported cause is insufficient technical assessment before work begins. Vendors conduct a business-level scoping exercise that documents high-level data flows, counts source tables, and catalogs report names. The engineering-level analysis of the actual environment gets skipped. Sixty-eight percent of failed migration projects cite this as a primary contributing factor.
“Most enterprises don’t fail at migration because the technology doesn’t work, they fail because the project was scoped before anyone looked at the actual environment,” says Amit Chandak, Chief Analytics Officer at Kanerika.
When the engineering-level analysis gets skipped, complexity surfaces during execution instead of before it. The downstream costs are consistent:
Undocumented dependencies surface mid-project and require rework Data quality issues missed in scoping need remediation mid-flight Business logic buried in legacy transformations has to be reverse-engineered on the clock Each discovery adds time and cost that was never in the original estimate
2. Platform Inexperience Billed as Expertise Large global systems integrators are built around fungible labor. A developer certified in a new platform is treated as equivalent to a developer with years of production experience. Both carry the same billing rate. The difference in delivery outcomes is substantial. Review data from G2, Gartner Peer Insights, and PeerSpot consistently surfaces the same complaint from enterprise buyers: paying for vendor learning curves rather than outcomes.
The downstream effects go beyond slower delivery. Platform inexperience leaves a trail:
Query patterns that worked in the legacy system perform poorly in the target environment Data models pass technical review but create performance problems at scale These issues typically surface six to twelve months after go-live The remediation cost was never in the original budget
3. Governance Treated as a Final Phase Most migration methodologies treat data governance as a downstream activity, scoped, estimated, and staffed after the core migration work is already designed. That sequencing consistently produces the same problems:
Governance requirements not factored into architecture decisions force rework after the fact Compliance gaps discovered in the final weeks trigger delays of months Security policies not integrated into migration design require retrofit before go-live Audit exposure and regulatory risk accumulate throughout a project that was never governed from the start
Froedtert Health and the Medical College of Wisconsin underwent a fifteen-month Collibra implementation after a failed 2017 attempt. The root cause was governance complexity that was never addressed at the architecture stage.
4. The Economics of Manual Migration Manual enterprise data migration scales cost with complexity. Developer teams write conversion scripts, recreate report logic by hand, and test transformation outputs individually. In enterprise environments, complexity is always higher than initial estimates. The numbers reflect this:
A large enterprise with 500 Informatica PowerCenter mappings and 200 Crystal Reports adds up to tens of thousands of hours of manual work At blended vendor rates of $150 to $250 per hour, that scope runs $3 to $8 million Timeline: twelve to eighteen months, before scope surprises The same migration at 70 to 80% automation runs in weeks and at a fraction of the cost
The automation tools exist. The gap is whether vendors are willing to build and maintain them. Most are not, because longer manual engagements are what their billing model depends on.
What successful migrations have in common is the reverse of these failure patterns. They start with an engineering-level assessment before any timeline is set. They bring governance into architecture decisions from the first sprint, not the final phase. And they use automation to handle conversion work that manual teams consistently underprice. The vendor landscape shows why these practices remain rare.
5. The Enterprise Data Migration Vendor Landscape TBR’s Q3 2025 benchmark covers 30 major vendors across a $439.8 billion market. The headline numbers are worth reading carefully before choosing a migration partner.
The large GSIs (Accenture, IBM Consulting, Capgemini, TCS, Infosys, Wipro, Cognizant) dominate by revenue. Their Q3 2025 results tell a more complicated story:
TCS posted -2.7% growth in North America, its largest market Wipro’s manufacturing segment declined for the ninth consecutive quarter Atos Group reported -14.4% growth amid ongoing financial instability Capgemini faces significant exposure to European automotive weakness IBM cut 8,000 roles through AI automation, the same AI that compresses the manual labor their billing model depends on
The structural issue is that these firms built their economics around labor arbitrage. Longer engagements, larger teams, hourly billing. AI-first migration directly erodes that model, which gives them limited incentive to fully automate what they currently bill by the hour.
The mid-market gap is the most structurally significant finding. Companies with $500 million to $5 billion in revenue, data infrastructure built over ten to fifteen years, real modernization urgency, and fixed budget constraints are the fastest-growing segment of migration demand in 2026. They are also the least well-served by traditional vendor economics.
How AI-First Automation Changes the Migration Model Most AI-powered migration offerings in the market layer AI tooling on top of fundamentally manual processes. Developers still write the migration logic, and AI assists with documentation or testing. The underlying cost structure, where labor hours scale to complexity, remains unchanged.
FLIP inverts this architecture for enterprise data migration. The platform’s AI agents perform the analysis, conversion, and validation work that traditionally requires development teams. Human engineers direct and review. The ratio of automated to manual work in a FLIP-powered migration runs 60 to 85%, depending on the platform combination and source environment complexity.
How FLIP Works What separates AI-first migration from the typical “AI-assisted” label is where the automation sits. In FLIP, multiple agents work in parallel on tasks that would otherwise require sequential developer effort, the analysis, conversion, and validation happen concurrently, not in a queue. The core capabilities that make this work:
Intelligent pattern recognition: FLIP analyzes source systems (Informatica mappings, SSIS packages, Crystal Reports, Cognos Framework Manager models) in days. A developer team takes weeks for the same analysis.Automated code generation: FLIP generates target-platform code directly from its analysis. An Informatica mapping becomes a Databricks Delta Live Table. A Crystal Report becomes a Power BI report with preserved calculations and layout. Engineers review and validate the output. They do not write it.Real-time error detection: Data quality issues, referential integrity problems, and transformation errors are caught during migration. Issues found mid-process cost a fraction of what they cost post-go-live.FLIP Copilot: A generative AI assistant embedded in the platform. Technical and business stakeholders can query migration status, data lineage, and transformation logic in plain language throughout the project.
Performance Across 12 Migration Paths Kanerika has built and deployed FLIP-powered accelerators across 12 distinct migration paths. These are not projected outcomes. They are documented results from the project portfolio, covering the most common enterprise data migration scenarios in 2025 and 2026.
Source: Kanerika project delivery data 2019–2025. All figures reflect documented project outcomes, not modeled projections.
The SSAS to Microsoft Fabric path stands out for one specific reason. Query performance improvements of 12x have been documented in production. SSAS cube migration to Fabric semantic models is technically complex and rarely automated elsewhere. FLIP converts cube definitions, transforms MDX calculations to DAX, and migrates dimensions and measures at 80% automation, the highest rate across any migration path in the portfolio.
Governance as an Enterprise Data Migration Foundation Failed migrations consistently reveal the same pattern: governance requirements discovered too late. Teams scope compliance frameworks, data lineage, access controls, and audit trails as final-phase work, then hit them mid-project as architectural blockers they never designed around.
Kanerika builds governance in from day one. KanGovern, KanGuard, and KanComply connect into FLIP-powered migrations at the initial technical assessment, not the end. That sequencing is what separates projects that stay on track from ones that stall in the final weeks.
KanGovern: Sets up governance infrastructure in the target environment before data lands there. Policy management, data classification, and stewardship workflows are in place on arrival, not added after.KanGuard: Manages data security and access control throughout migration. Handles sensitive data discovery, masking, encryption, and permission inheritance during the period when access patterns are actively changing.KanComply: Generates regulatory compliance documentation as a built-in output. Covers SOX, FCA, Basel III, HIPAA, and FDA data integrity requirements automatically. Audit trails are produced during migration, not assembled after it.
Projects that incorporate all three from the initial assessment phase achieve 100% regulatory compliance maintenance throughout migration, zero security incidents during or after implementation, and automated audit trail generation. Projects treating governance as a final phase rarely achieve any of these outcomes.
How Kanerika Approaches This A few facts worth knowing before evaluating Kanerika as a migration partner:
Microsoft Solutions Partner with Analytics Specialization and Microsoft Fabric Featured Partner status Databricks Consulting Partner and Snowflake Select Tier Partner Certified ISO 27001, ISO 27701, SOC 2 Type II, CMMI Level 3, and GDPR compliant 10+ years, 100+ enterprise clients, 98% client retention across financial services, healthcare, manufacturing, logistics, pharmaceuticals, and insurance
FLIP powers all 12 migration accelerator paths and has delivered a 95% project success rate across the portfolio. FoodPharma, a food and pharmaceutical manufacturer, consolidated six operational systems on Microsoft Fabric in a seven-week implementation, cutting cross-functional reporting cycles from two business days to ninety minutes. That project is a verified Microsoft Customer Story.
The research behind the State of Enterprise Data Platform Migration 2026 report draws on Kanerika’s internal project delivery data from 2019 to 2025, TBR’s Q3 2025 IT Services Vendor Benchmark , and multiple published industry research sources. All performance metrics reflect documented project outcomes rather than modeled projections.
Case Study: SSRS to Power BI Migration A global enterprise running SQL Server Reporting Services needed to move to Power BI without losing paginated report functionality, subscription workflows, or parameter logic. FLIP automated 75% of the conversion work and cut the timeline by 65% versus manual baseline.
Challenges Static SSRS layouts needed a full redesign for Power BI’s responsive interface Complex queries and stored procedures required DAX-based rewrites Security and access control gaps blocked centralized governance Subscription workflows and report parameters had to migrate without disrupting live reporting
Solutions FLIP automated SSRS to Power BI (.pbix) conversions with layouts, filters, and logic preserved Structured deployment phases kept downtime minimal during cutover Role-based access and audit governance configured in Power BI Service Power BI integrated with Microsoft Fabric for unified data modeling post-migration
Results 75% of migration tasks automated by FLIP 65% reduction in delivery timeline versus manual baseline $100K–$250K in project cost savings Zero report logic lost in conversion
Wrapping Up Enterprise data migration has a 73% failure rate. The platforms work. The execution model keeps breaking. When 80% of budgets exceed original estimates by 150% or more and timelines overrun by an average of eighteen months, the issue is structural: manual processes scaled to complexity, governance discovered late, and platform inexperience billed at expert rates.
AI-first automation changes this equation by inverting the ratio of automated to manual work. When intelligent agents handle 60 to 85% of migration tasks, costs drop, timelines compress, and governance fits in from day one. Read the full report to understand the research, the vendor landscape, and what a better migration framework looks like in 2026.
Ready to Move Off Legacy Infrastructure? Get a technical assessment of your migration scope before committing to a vendor.
Schedule a Conversation
Frequently Asked Questions What is the current failure rate for enterprise data migration projects? Industry research places the failure rate at 73%. Of projects that do proceed, 80% exceed original budgets by 150% or more, and timeline overruns stretch an average of eighteen months beyond initial plans. The failure rate reflects structural problems in how migrations are sold and delivered, not individual project bad luck.
What are the main causes of enterprise data migration failure? Five factors appear consistently across failed projects: budget overruns exceeding 150% (80% of failures), inadequate technical discovery (68%), manual processes creating schedule variance (61%), platform-specific inexperience (42%), and governance gaps identified too late in the project (35%). Most of these are predictable and preventable with the right methodology.
Why is the Informatica PowerCenter end-of-support deadline significant? Informatica discontinued on-premise PowerCenter support after 2026. Enterprises that built data integration infrastructure around PowerCenter over the past decade now face a hard migration decision. Staying on unsupported, unpatched infrastructure creates security, compliance, and operational risk. Scoping and vendor selection for a complex migration takes two to three months, so organizations beginning evaluation in mid-2026 are likely to operate on unsupported infrastructure through at least some portion of 2027.
How does AI-first automation change migration economics? In a manual migration, labor hours scale directly with complexity, and enterprise environments are always more complex than initial estimates. A fully manual migration of 500 Informatica mappings and 200 Crystal Reports can run $3 to $8 million over twelve to eighteen months. When 60 to 85% of conversion steps are automated, the same scope runs in weeks rather than months at a fraction of the cost. The cost structure changes because automated work does not scale with complexity the way manual work does.
What is FLIP and how does it work? FLIP is Kanerika’s AI-powered migration accelerator. Multiple intelligent agents work in coordination to analyze source systems, generate target-platform code, detect errors in real time, and validate transformations. Human engineers direct and review. FLIP supports 12 migration paths covering Informatica, SSIS, SSAS, Crystal Reports, Tableau, Cognos, SSRS, and legacy ETL systems migrating to Microsoft Fabric, Power BI, Databricks, Azure, and Talend.
What migration paths does Kanerika support? Kanerika has built FLIP-powered accelerators across 12 migration paths, including Azure to Microsoft Fabric, SSIS to Fabric, Informatica to Fabric, SSAS to Fabric, Crystal Reports to Power BI, Tableau to Power BI, Cognos to Power BI, SSRS to Power BI, Informatica to Databricks, Informatica to Talend, SQL Server to Azure, and legacy ETL to cloud platforms. Automation rates range from 60% to 85% depending on the source-target combination.
What is the mid-market gap in enterprise data migration? Companies with $500 million to $5 billion in revenue represent the fastest-growing segment of migration demand in 2026 and the segment least well-served by traditional vendor economics. Large GSI engagements bring overhead and billing structures built for much larger projects. Platform vendor certifications reveal little about actual delivery track records. Mid-market enterprises need expert migration capability at economics that match their scale.
Where can I read the full State of Enterprise Data Platform Migration 2026 report? The full report is available at kanerika.com. It covers the structural causes of migration failure, the market dynamics driving urgency in 2026, a detailed breakdown of FLIP performance across all 12 migration paths, regional market dynamics for the US, Canada, and UK, a vendor evaluation checklist, and Kanerika’s recommendations for enterprise data leaders preparing for migration.
