Data migration challenges are more predictable than most teams expect, and more avoidable than most vendors admit. Analysis of more than 500 enterprise reviews on G2, Gartner Peer Insights, and TrustRadius reveals a recurring pattern: migrations that looked straightforward on paper ran into downtime, data loss, reporting failures, and compliance risks once moved to production. In most cases, the technology was not the problem. Poor planning, weak data validation, and unclear ownership were.
What enterprise buyers describe after these failures matters. Before reviewing specific challenges, see our data migration guide for the foundational context. In this article, we cover the five core data migration challenges that appear consistently across review platforms, why vendor overpromising drives most complaints, how buyers evaluate migration partners after a bad experience, and how an engineering-led approach closes the gap.
Key Takeaways Poor discovery and planning is the root cause of migration failures in the majority of negative enterprise reviews on G2, Gartner Peer Insights, and TrustRadius Data quality issues, governance gaps, and platform-specific knowledge deficits are the three technical challenges that consistently derail execution once migration starts “Overpromised and underdelivered” appears in 42% of negative vendor reviews and almost always points to weak discovery processes rather than technology failures Enterprise buyers who have experienced a failed migration shift their evaluation criteria from feature lists to verified delivery track records and platform specializations An engineering-led approach combined with platform-specific accelerators directly addresses each of the five challenges that repeat across enterprise review data
The Five Core Data Migration Challenges That Show Up in Every Enterprise Review Enterprise review data does not lie. When hundreds of organizations across financial services, healthcare, manufacturing, and logistics independently describe the same data migration challenges across separate review platforms, those challenges are structural, not coincidental.
Analysis of review patterns across G2 and Gartner Peer Insights identifies five data migration challenges that appear regardless of platform, industry, or migration type. These are not edge cases or project-specific risks. They are the default failure modes when an engagement prioritizes sales over engineering.
The table below shows the frequency and downstream impact of each:
Challenge % of Negative Reviews Citing It Primary Impact Poor discovery and planning 68% +150% timeline overruns, significant remediation spend Data quality and integrity failures 54% Corrupted records, broken downstream reporting Governance and compliance gaps 35% Late-stage audit failures, regulatory exposure Platform-specific knowledge deficits 31% Rework, replatforming mid-project Timeline and execution failures 42% Budget overruns, stakeholder confidence loss
Source: Analysis of enterprise reviews on G2 and Gartner Peer Insights, 2024-2025
1. Poor Discovery and Planning This is the challenge that starts almost every failure chain. Vendors rely on business questionnaires instead of deep technical assessments, which means critical dependencies go unmapped until they surface mid-migration.
What enterprise buyers report in reviews:
“The vendor didn’t understand our SQL Server complexity” (Healthcare organization, G2) “They gave us a six-month estimate without ever seeing a single ETL job” (Financial services firm, Gartner Peer Insights)
The mechanics behind this challenge:
Generic questionnaires miss system interdependencies and custom integrations Discovery is treated as a sales phase rather than an engineering phase Vendors quote timelines before assessing data volume, transformation complexity, or governance requirements Source system documentation is assumed to be current when it rarely is
A proper discovery process covers five areas: technical architecture review, dependency mapping, data quality assessment, compliance gap analysis, and performance baseline testing. This is the foundation of every effective data migration framework . Skipping any one of these creates blind spots that compound during execution.
2. Weak Data Quality and Integrity Controls Data quality issues are the second most cited challenge. They are particularly destructive because they surface late: by the time corrupted records or broken schemas appear in production, rollback is often not feasible.
What reviews describe:
“Data quality issues surfaced in month 8” (Manufacturing company, TrustRadius) “We migrated 18 months of records only to find schema mismatches we hadn’t mapped” (Retail enterprise, G2)
Common failure patterns:
Source data profiling is skipped or completed superficially during the presales process Validation checkpoints are placed only at the end of migration, not throughout the pipeline Transformation logic is assumed rather than documented and independently tested ETL pipeline errors are treated as edge cases rather than systematic risks requiring monitoring
Data quality must be addressed before migration begins. Continuous monitoring across extraction, transformation, and loading phases catches errors before they reach production environments.
3. Governance and Compliance Gaps Reviews from regulated industries, including financial services, healthcare, and insurance, consistently flag governance as an afterthought. Security controls and compliance frameworks added after the core migration completes require retroactive changes to data structures and access models already in production. Understanding data governance pillars before migration starts prevents this.
What reviews say:
“Data governance was treated as a post-migration task. It caused compliance issues six months later” (Financial services firm, G2)
Critical governance components that must be built in from day one:
Data governance classification and lineage documentation mapped to the target platformAccess control management with role-based permissions Compliance monitoring against GDPR, HIPAA, SOX, or sector-specific frameworks Backup and recovery protocols tested before production cutover
4. Platform-Specific Knowledge Deficits “Said they knew our platform but learned on our dime” is a phrase that appears across dozens of reviews about failed Microsoft Fabric , Databricks , and Snowflake migrations. Platform expertise is not interchangeable. General data engineering experience does not translate into platform-specific delivery capability.
What this looks like in practice:
Organizations are now prioritizing Microsoft Analytics Specialization credentials, documented platform case studies, and certified engineers over general cloud or data integration backgrounds when selecting migration vendors.
5. Timeline and Execution Failures Timeline overruns average 150% when vendors lack platform-specific accelerators, according to Raconteur research on enterprise transformation programs . Only one in five companies achieves the majority of their revenue gains from transformation projects, a figure that reflects how widespread execution failures are across industries. Platform-specific paths like SSIS to Fabric migration are one proven way to reduce this risk.
What drives overruns in practice:
Scope creep from undiscovered dependencies flagged during execution rather than discovery Manual rework stemming from the absence of automated conversion tooling Sequential workstreams because of resource constraints, when parallel execution would reduce the timeline Extended testing cycles caused by validation failures that a structured pre-migration assessment would have prevented
Looking for enterprise data migration services with proven accelerators? Connect with migration experts to discuss your specific requirements.
Book a Meeting
Why “Overpromised and Underdelivered” Dominates Enterprise Migration Reviews McKinsey research on digital transformation finds that 70% of transformation programs fail to achieve their stated goals. Poor execution is the primary cause, not technology limitations. Enterprise review data mirrors this finding: the most common single phrase in negative migration reviews is not “the technology failed.” It is “overpromised and underdelivered.”
This phrase appears in 42% of negative reviews analyzed across G2 and Gartner Peer Insights for enterprise data migration vendors. It almost never describes a product deficiency. It describes a gap between what was sold and what was delivered.
1. The Gap Between Promise and Delivery Vendors win contracts with polished proposals and confident timelines. The actual complexity of a migration is only discovered when engineers begin working inside the source environment. By that point, the contract is signed and the timeline is fixed.
Three patterns drive this gap:
Discovery done wrong. Reviews consistently cite “didn’t understand our environment” and “missed critical dependencies.” Generic questionnaires replace deep technical assessments because the latter takes longer and risks losing the deal.Execution without automation. Manual conversion approaches create rework cycles that extend timelines well beyond initial estimates. Vendors without migration accelerators underestimate the conversion effort at the presales stage.Governance as an afterthought. Security and compliance problems cause late-stage delays when they are not addressed structurally from the start of the engagement.
2. What Review Data Reveals About Vendor Behavior The pattern in positive migration reviews is different. When organizations describe successful migrations, three factors appear consistently: engineering-led discovery before any work begins, automated conversion tooling that reduces rework, and governance built into the migration architecture rather than added post-cutover. See how this played out in the Azure Data Factory to Microsoft Fabric migration .
These are not differentiators. They are baseline requirements that vendors routinely fail to meet, because meeting them requires investing in presales discovery that most vendors treat as a cost center.
What buyers now look for based on patterns in positive reviews:
Documented case studies in the specific migration type, not adjacent technology experience Evidence of platform-specific certifications verified through the partner’s official credential profile Clear methodology for governance requirements stated explicitly at project kickoff, not deferred Engineering-led discovery that involves the client’s technical team from the first engagement meeting Fixed milestone structure with defined acceptance criteria at each phase
The distinction between “delivered what they promised” and “overpromised and underdelivered” almost always comes down to what happened during discovery. Vendors who front-load engineering effort into discovery produce fewer surprises. Vendors who front-load sales effort produce the reviews that fill G2 with warnings.
3. Common Vendor Patterns and Engineering-Led Solutions Common Vendor Promise Reality Without Proper Planning What Engineering-Led Delivery Provides “6-month timeline” 18+ months with multiple delays Phased delivery with milestone-based reporting “Automated conversion” Manual rework requiring developers Accelerator-driven conversion with validation checkpoints “Built-in governance” Compliance gaps discovered late Governance architecture integrated from project start “Fixed-price project” Budget overruns from undiscovered scope Transparent assumptions documented upfront
How Enterprise Buyers Evaluate Migration Partners After Project Failures Enterprise buyers who have experienced a failed migration change their evaluation criteria entirely. The shift moves away from capability checklists and toward delivery evidence. When organizations carry the memory of a failed migration, carrying remediation costs, timeline overruns, and reporting disruption, they approach vendor selection with sharper requirements.
This shift is documented across enterprise review platforms. Organizations that leave negative reviews about migration vendors almost uniformly describe the same gap: they evaluated vendors on what they claimed to be able to do, rather than on what they had already done for comparable clients. The evaluation framework that follows reflects what experienced buyers now use to avoid repeating that mistake.
1. From Features to Proven Delivery Track Record Capability claims without evidence no longer carry weight. Organizations that have experienced migration failures have seen first-hand that listing compatible technologies says nothing about a vendor’s ability to execute a specific migration type, on a specific platform, within a specific industry context.
What the evaluation now requires:
Documented case studies in the specific migration type, not just the general technology area Client references from projects with comparable complexity and data volumes Timeline performance from prior engagements, not projected timelines from the proposal Named engineers with platform certification on the delivery team, not just in company marketing materials Post-implementation support capabilities and maintenance track records for the target platform Partner credentials verified through official partner portals
Quote from a Fortune 500 manufacturer: “This time we want to see successful Informatica to Databricks migrations in our industry. Not a pitch deck about what you can theoretically do.”
2. From Cost to Total Cost Transparency The initial contract price is no longer the dominant evaluation criterion. Organizations that have absorbed significant remediation costs on failed projects now evaluate migration partners on total cost of ownership, not the contract line item.
What total cost transparency requires from vendors:
Scope documentation with all assumptions stated explicitly before contract signing Change order policies and how out-of-scope work is handled and priced Honest assessment of hidden costs from manual rework when automation tooling is absent Licensing cost projections for the target platform compared to the source environment Post-migration maintenance cost projections over a one-to-three year horizon
Quote from a healthcare organization selecting a new migration partner: “We need upfront clarity on timeline and budget. No surprises in month 6.”
The organizations that ask these questions before signing are the ones whose migration reviews describe outcomes, not regrets.
3. From Generic to Platform-Specialized Services The third shift is toward verified platform expertise. Organizations that experienced a Microsoft Fabric migration failure now require proof of Fabric specialization before shortlisting. The same applies for Databricks , Snowflake , Power BI , and Azure Data Factory.
Platform specialization is not just about having certified engineers. It means the vendor has delivered enough migrations on that specific platform to have encountered and resolved edge cases, undocumented API behaviors, and performance constraints that only appear at production scale. This depth of experience is not transferable from one platform to another.
What buyers now require as evidence:
Microsoft Solutions Partner for Data and AI with Analytics Specialization Platform-specific certifications verified through the partner’s official profile Named engineers with platform certification on the delivery team, not just in company marketing materials Migration accelerators built specifically for the target platform, not generic data migration tools repurposed for a new environment
The Engineering-Led Fix for Enterprise Data Migration Challenges The five data migration challenges reviewed above share a common thread: they are managed as project delivery problems rather than engineering problems. An engineering-led approach reframes each challenge as a technical issue with a defined solution, a measurable output, and a clear accountability structure.
1. Engineering-Led Discovery vs. Business Questionnaires Engineering-led discovery begins before any migration work starts. Rather than relying on stakeholder interviews and business questionnaires, experienced migration teams assess the source environment directly using technical tooling. The output is a documented inventory of every dependency, data quality risk, and compliance requirement. All of this is documented before the project timeline is set.
What a proper discovery covers:
Complete system architecture documentation with dependency mapping across all integration points Data flow analysis across all source and target systems, including shadow data stores API, database, and downstream application dependency identification Performance benchmarking of the source environment under production load conditions Security and compliance gap analysis against target platform requirements
This takes longer upfront. It prevents the 150% timeline overruns that appear consistently in enterprise review data for migrations without structured discovery.
2. Platform-Specific Migration Accelerators Platform-specific accelerators change the economics and timelines of migration. Where manual conversion requires engineers to rebuild transformation logic from scratch, accelerators automate conversion while preserving business logic and data integrity.
Kanerika’s FLIP platform provides accelerators across twelve migration paths:
Verified FLIP results across enterprise migrations:
50 to 60% reduction in migration effort 40 to 60% faster loading post-migration on the target platform 75% reduction in annual licensing costs Complex two-year codebases completed within 90 days
FLIP is available on the Microsoft Azure Marketplace and deployed across Azure, AWS, and GCP.
3. Built-in Governance and Compliance from Day One Governance cannot be treated as a post-migration activity in regulated industries. The third most cited data migration challenge in enterprise reviews is governance gaps discovered late. This is solvable only if governance architecture is built into the migration from the start.
Building governance in from day one means the compliance documentation, access controls, and data lineage records are audit-ready from the moment the target environment goes live. Adding them retroactively means the period between go-live and compliance remediation carries real regulatory exposure.
Kanerika’s governance suite, built on Microsoft Purview , covers all three layers:
KANGovern manages data governance strategy, policy enforcement, and data lineage documentation throughout the migration lifecycleKANComply provides the regulatory compliance framework for GDPR, HIPAA, SOX, and industry-specific requirementsKANGuard prevents unauthorized access and enforces data security controls at every migration stage
4. Phased Value Delivery Over Big-Bang Migration Big-bang migrations, where an entire environment moves in a single cutover, carry the highest risk profile and produce the longest recovery cycles when they fail. A phased approach removes this risk by delivering working functionality in planned increments, allowing stakeholders to validate results before the next phase begins.
A structured phased approach:
Phase 1 (Weeks 1-8): Core data migration with basic reporting validated in the target environmentPhase 2 (Weeks 9-16): Advanced analytics, dashboard migration, and integration connections rebuiltPhase 3 (Weeks 17-24): Integration optimization, performance tuning, and governance enforcementPhase 4 (Weeks 25-32): Advanced features, user training, and legacy system decommission
Each phase includes a defined acceptance gate. Migration moves forward only when the prior phase has been validated by the client’s technical team. This structure protects against the most common execution failure: discovering an undocumented dependency halfway through a production cutover window.
How Kanerika Addresses Enterprise Data Migration Challenges Kanerika is a Microsoft Fabric Featured Partner and Microsoft Solutions Partner for Data and AI with Analytics Specialization. With 100+ enterprise clients and 98% client retention across ten years of operation, Kanerika’s data migration services cover the full spectrum of data migration challenges: pipeline conversion, data quality remediation, governance integration, and post-migration optimization.
Kanerika holds ISO 27001 certification, SOC II Type II compliance, and GDPR compliance. These credentials matter specifically for migrations in regulated industries where governance gaps are the third most cited failure cause in enterprise reviews.
The FLIP migration platform, available on the Microsoft Azure Marketplace, provides twelve pre-built accelerator paths that cover the most common enterprise migration routes. Kanerika’s in-house Microsoft MVP for Power BI , Chief Analytics Officer Amit Chandak, brings platform-specific expertise that generic integration vendors cannot replicate.
The data migration challenges enterprise buyers describe in failed project reviews: poor discovery, platform knowledge gaps, governance afterthoughts, and execution overruns. These are exactly what Kanerika’s engineering-led methodology addresses by design, not by exception.
Case Study: SSIS to Microsoft Fabric Migration Challenges: Legacy SSIS infrastructure built over multiple years, with hundreds of pipeline interdependencies not documented in any central repository No clear data lineage across source systems, creating compliance exposure under audit requirements Business-critical reporting running on daily batch schedules that could not tolerate extended downtime during cutover Internal team lacked Microsoft Fabric expertise, making the transition a high-risk project with no internal safety net Solutions: Kanerika deployed FLIP ‘s SSIS-to-Fabric migration accelerator , automating pipeline conversion while preserving all transformation logic Conducted full technical discovery across all source pipelines before any migration work began, mapping every dependency and integration point Rebuilt data lineage documentation during migration using KANGovern, creating audit-ready records from day one Ran phased cutover in controlled maintenance windows to maintain reporting continuity, with parallel validation runs before each production switchover Results: 30% faster data processing post-migration on the Microsoft Fabric platform 40% reduction in total migration and infrastructure costs compared to a manual migration approach 99.9% data integrity maintained across all migrated pipelines 25% reduction in ongoing maintenance overhead from the new platform architecture
Conclusion Enterprise data migration challenges are predictable. The same five problems, poor discovery, data quality failures, governance gaps, platform knowledge deficits, and execution overruns, appear across G2, Gartner Peer Insights, and TrustRadius with remarkable consistency. That pattern means they are solvable with the right methodology, not just the right technology. Engineering-led discovery, platform-specific accelerators like FLIP, and governance built in from day one close the gaps that generic vendors leave open. Organizations that have experienced a failed migration know the real cost of choosing on price and promises. The ones that recover choose based on evidence.
Looking for engineering-led data migration consulting? Schedule a consultation to explore how proven methodologies eliminate execution risk.
Book a Meeting
FAQs 1. What Are the Most Common Data Migration Challenges? The most common data migration challenges are poor discovery and planning, data quality and integrity failures, governance and compliance gaps, platform-specific knowledge deficits, and timeline overruns. Enterprise review analysis shows poor discovery is the leading root cause, as it creates a cascade of downstream failures across the other challenge areas during execution.
2. What Causes Data Migration Projects to Fail? Data migration projects fail primarily due to inadequate upfront discovery, which leaves system dependencies and data quality issues undetected until they appear in production. Vendor overpromising on timelines, absence of automated migration tooling, and governance treated as a post-migration task are the three execution factors that turn discovery gaps into full project failures.
3. How Do You Handle Data Quality in a Migration? Data quality in migration requires source data profiling before any migration work begins. Validation checkpoints must be placed at each pipeline phase, covering extraction, transformation, and loading, rather than only at completion. Continuous monitoring during migration catches schema mismatches, null value anomalies, and transformation errors before they reach production environments and cause reporting failures.
4. What Is the Role of Governance in Data Migration? Governance in data migration covers data classification, access control, lineage documentation, and regulatory compliance. It must be built into the migration architecture from day one, particularly in regulated industries like financial services, healthcare, and insurance. Retroactive governance work costs more than migration itself and creates audit exposure in the period between go-live and remediation.
5. How Long Does Enterprise Data Migration Take? Enterprise data migration timelines range from weeks to several months depending on data volume, source environment complexity, and the number of dependent systems. Platform-specific accelerators reduce timelines measurably: FLIP cuts migration effort by 50 to 60% and completes complex two-year codebases in 90 days. Migrations without automation tooling consistently take two to three times longer than initial estimates.
6. How Do You Choose the Right Data Migration Partner? Evaluate migration partners on documented case studies in your specific migration type, verified platform credentials rather than general cloud experience, engineering-led discovery methodology, and client references from comparable projects. Buyers who have experienced failed migrations prioritize platform specialization and delivery track records over pricing and generic capability claims.
7. What Are the Risks of Delaying a Legacy Data Migration? Delaying migration from legacy systems creates accumulating technical debt, rising maintenance costs, and growing compliance risk as platforms lose vendor support. Organizations that defer migration face performance degradation, audit exposure from outdated compliance frameworks, and rising costs from manual workarounds substituting for functionality the legacy platform can no longer reliably provide.
8. What Is the Difference Between Data Migration and Data Integration? Data migration is a planned, one-time transfer from a source system to a target, with the source typically decommissioned after cutover. Data integration keeps data synchronized across systems on an ongoing basis. Both often coexist in the same project but require different tooling, timelines, and engineering approaches.
