TL;DR
The ten data migration risks start with data loss, silent logic drift, poor source quality, schema and encoding mismatch, and hidden dependencies. The other five are failed rollback, compliance exposure, performance and cost spikes, testing gaps, and early source retirement without an owner. Each gives a warning sign, such as differing totals behind matching row counts, rejected rows in load logs or an unowned object still in use. The strongest single control is reconciling business totals instead of row counts. Choose the cutover approach your downtime limit allows, from a rehearsed big bang to phased waves, change data capture or a parallel run. Whatever the approach, score each risk by likelihood and impact and block go-live while any high score lacks a proven control.
Key Takeaways
Data migration risks fall into three groups: damage to the data, damage to the business that runs on it, and damage to trust and compliance.
Matching row counts prove little on their own, so reconcile sums, checksums and the business totals people actually use.
Score every risk by likelihood and impact on a 1 to 5 scale, then give each high score a control, an owner and a trigger.
Pick the cutover approach by downtime tolerance and rollback needs, because big bang, phased, parallel run and trickle each move risk to a different place.
Keep the legacy source, backups and retention records until the business signs off, since early retirement removes your last way back.
Plan for downstream damage as well, because one rounding change can move Power BI measures and the forecasts trained on them.
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Fabric Migration Risk: Undocumented Business Logic
Business rules hidden in old reports and pipelines are the hardest migration risk to test. This video shows how that logic surfaces during a Microsoft Fabric migration and why it has to be found before cutover.
The Migration That Passed Every Row Count
Picture a Friday night cutover for a finance data warehouse. Every table lands in the new platform, and the row counts match to the last record. The team then signs off before midnight and goes home.
On Monday, the controller runs the quarter-close revenue report, and the total is off by an amount nobody can explain. Two days later, an analyst finds the cause. The old system stored unit prices with four decimal places, and the new column kept two.
Every row arrived. Many of them arrived wrong.
That gap between “the data moved” and “the data still means the same thing” is where data migration risks live. Row counts confirm that records exist, yet they say nothing about values, logic, timing or the reports and models built on top of them.
This guide covers the ten risks that do the most damage, the warning signs that show up early and the controls that close each one. It also shows how to score risks, choose a cutover approach and protect the analytics and AI that depend on migrated data.
What Are Data Migration Risks?
Data migration risks are events that could cause data to be lost, changed, exposed or unavailable while it moves from one system to another. They also include the business effects of those events, such as wrong reports, missed deadlines, compliance penalties and higher running costs.
It helps to separate three words that often get used as one. A risk is something that might happen. It becomes an issue once it does happen, and it becomes a failure when the issue reaches users, customers or regulators before anyone catches it.
The distinction matters because each stage has a different owner and a very different cost to fix. A team that tracks risks early can deal with them while the fix is still a design decision, often before a single row moves. For the full project lifecycle around these risks, see the enterprise guide to data migration .
Term What it means Example Who acts
Risk A problem that could happen during or after the move Legacy order dates use two formats, and the target accepts only one Migration lead, during assessment Issue A risk that has happened and been detected Some order dates fail to load in a test run Data engineer, before cutover Failure An issue that reached the business or a regulator Finance closes the quarter on misdated orders Business owner and executive sponsor
The rest of this guide stays in the first row of that table. A risk caught at assessment costs a meeting, while a failure can cost a restated report and a lot of trust.
Checklist
Data Migration Checklist
A step-by-step checklist for planning, moving and validating data, so every risk in this guide has a matching task and owner before cutover.
Get the Checklist → Why Data Migration Is Riskier in 2026
Moving data has always been hard, but the conditions around it have changed. Few organizations now copy one database to another over a quiet weekend. Instead, they move pieces of a hybrid estate while the business keeps running, and the migrated data feeds far more consumers than it used to.
Five of the 2026 data migration trends raise the risk most:
Hybrid estates. Data spans on-premises systems, several clouds and SaaS applications, so one migration touches many owners and network paths.Change data capture on live systems. Many teams replicate changes from a running source instead of freezing it, which adds lag, ordering and duplicate risks.Business logic buried in old code. Rules for revenue, discounts and status codes often sit inside stored procedures, ETL jobs and BI reports that nobody documented.Stricter privacy rules. Copies of personal data in staging areas and test environments carry the same obligations as production data.AI and semantic models downstream. Migrated data now feeds Power BI measures, forecasting features and AI agents, so a small error spreads further and faster.
None of these pressures is new on its own. Together, though, they mean a migration is rarely a one-time copy. It belongs inside a wider cloud transformation strategy that decides which systems move, in what order and why. For platform moves specifically, the guide to data platform migration covers the architecture choices.
The 10 Biggest Data Migration Risks and How to Avoid Them
The ten risks below cover where migrations tend to break, roughly in the order they surface during a project. Each one has an early warning sign, a primary control and a natural owner. Use the table as a quick reference, then read the sections that match your project.
Risk Early warning sign Primary control Owner
1. Data loss and incomplete loads Load logs show rejected or skipped rows Row, sum and checksum reconciliation per table Data engineer 2. Silent corruption and logic drift Business totals differ while row counts match Parallel run that compares business totals Business analyst and finance owner 3. Poor source data quality Profiling finds duplicates, nulls and orphan keys Profile and cleanse before the move Data steward 4. Schema, type and encoding mismatch Truncation or conversion warnings in test loads Type-mapping spec and test loads on real samples Data architect 5. Hidden dependencies Reports or jobs fail in test with missing objects Lineage mapping and consumer sign-off BI lead and application owners 6. Downtime and failed rollback Replication lag grows during rehearsal Rehearsed cutover with a timed rollback point Migration lead 7. Security and compliance exposure Personal data appears in staging or logs Masking, encryption and access review Security and privacy officer 8. Performance and cost spikes Test queries run slower or scan more data Query and partition tuning, budget alerts Platform engineer and FinOps 9. Testing gaps Test plan checks counts but not business rules Business-rule tests with signed acceptance criteria QA lead 10. Ownership gaps and early retirement No named sign-off owner per dataset Ownership per dataset and a retention gate Executive sponsor
How the Ten Risks Group Together
The warning signs in the table rarely arrive one at a time. Truncation warnings, failing reports and mismatched totals in the same test week usually trace back to one gap. It might be a mapping nobody reviewed or a test plan that only counts rows. Fixing that gap clears several signs at once.
The ten risks also group by what they damage. Risks 1 to 4 harm the data itself, while risks 5, 6 and 8 harm the business that runs on it. The remaining three, risks 7, 9 and 10, harm trust and compliance.
The first group splits once more. Risks 1, 2 and 4 break integrity, because good data changes in transit. Risk 3 is a quality problem the source already had, and the guide to data integrity vs data quality explains the difference.
1. Data Loss and Incomplete Loads
Data loss is the most visible risk, and yet it is often the easiest to prevent. Records go missing when a load job hits a constraint, a batch times out or an extraction query quietly skips rows with null keys.
The control is reconciliation at three levels for every table. Row counts catch missing records, and sums of key numeric columns catch changed values. A hash of each full row catches changes that cancel each other out. A query like this one, run on both sides and compared, covers the first two levels.
SELECT order_date,
COUNT(*) AS row_count,
SUM(net_amount) AS total_net,
COUNT(DISTINCT customer_id) AS customers
FROM sales.orders
GROUP BY order_date
ORDER BY order_date;Tools can carry part of this load. AWS Database Migration Service validation compares each source row with its target row after the full load and during ongoing replication. From version 3.4.6, a full-load validation-only task compares every row in a single pass and reports failures at once. That suits a final check just before cutover. For the wider method, see Kanerika’s guide to data reconciliation .
In a DMS task, validation is switched on in the task settings JSON. The block below follows the AWS data validation task settings reference, checked in October 2026.
{
"ValidationSettings": {
"EnableValidation": true,
"ThreadCount": 10,
"PartitionSize": 10000,
"FailureMaxCount": 10000,
"TableFailureMaxCount": 1000
}
}EnableValidation is off by default, so it has to be set. A ThreadCount of 10 doubles the default of 5, which finishes sooner but runs more queries on both databases. The two failure limits are the defaults, and validation is suspended for the task or the table once failures pass them.
2. Silent Corruption and Business Logic Drift
Silent corruption is the risk from the opening example. Every row arrives, but some values no longer mean what they meant. It is harder to catch than loss, because no job fails and no count changes.
The causes, however, are usually small. A rounding rule changes when a DECIMAL(18,4) column becomes DECIMAL(18,2). A timestamp shifts because the old system stored local time and the new one assumes UTC.
Rewritten code adds its own drift. A null becomes an empty string, so a report that treated nulls as “unknown” now counts them as valid. And when stored procedures are rebuilt for a new platform, a CASE statement can lose the branch that handled one rare status code.
The control is a parallel run that compares business totals, not only technical ones. Think revenue by month, open orders by status and inventory by plant. Ask the business owner which five numbers they check first, and reconcile those. The ETL migration guide covers how to carry transformation logic across safely.
Undocumented logic is the hardest part, because nobody can test a rule they do not know exists. Finding it means reading the old code and also asking the people who use the reports, not only the people who built them. BI platform moves such as Cognos to Power BI migration carry this risk in every calculated field and report filter.
3. Poor Source Data Quality Carried Forward
A migration copies problems as faithfully as it copies good data. Duplicate customers, orphaned order lines, free-text country fields and dates stored as strings all arrive in the new platform unless someone deals with them first.
The cost is real. Gartner has estimated that poor data quality costs the average organization at least USD 12.9 million a year, according to its research from 2020. A migration is often the cheapest moment to cut that cost, since the data is already being read, mapped and tested.
So profile every source table before you map it. Measure null rates, duplicate keys, invalid codes and broken references. Then decide, problem by problem, whether to fix it at the source, fix it in flight or accept it and record why. The worst choice is the silent one, where bad data moves because nobody looked.
4. Schema, Data Type and Encoding Mismatch
Every database engine stores numbers, text and dates a little differently, so every migration includes a translation step. That step is where data conversion and data migration overlap. Most mismatches fail loudly in testing, so they get fixed. The dangerous ones succeed and change the data on the way in, as these four cases show.
Source Target What can go wrong
DECIMAL(18,4) unit price of 2.3456 DECIMAL(18,2) Stored as 2.35, so 10,000 units total 23,500 instead of 23,456 VARCHAR(200) product name VARCHAR(100) Long names are cut off, or the load rejects them Text in a legacy code page such as Windows-1252 UTF-8 Accented customer names turn into garbled characters Dates stored as day-first text A date parser that expects month-first 03/04 loads as March 4 instead of April 3, with no error
In SQL Server, the first row is documented behavior. Microsoft’s decimal and numeric reference says a conversion to lower precision and scale rounds by default. A loss of only precision and scale raises no error. A query on the source shows how many rows would change, and by how much, before anyone runs a load.
SELECT COUNT(*) AS rows_that_round,
SUM(unit_price * quantity)
- SUM(CAST(unit_price AS DECIMAL(18,2)) * quantity) AS revenue_drift
FROM sales.order_lines
WHERE unit_price <> CAST(unit_price AS DECIMAL(18,2));The control is a written type-mapping specification for every column that changes type, length or encoding, reviewed by someone who knows the data. Test loads should use real samples, including the longest values, the oldest records and names with accents. Synthetic test data rarely contains the edge cases that break.
Datasheet
FLIP Migration Accelerators
See what each FLIP accelerator automates across discovery, conversion and validation for ETL, BI and RPA migrations.
View the Datasheet → 5. Hidden Dependencies and Broken Integrations
Every system that has run for years collects consumers nobody planned for. A finance spreadsheet pulls from a view over ODBC, and a nightly job sends a file to a partner. A dashboard in another department reads a table directly.
Yet none of them appear on the migration plan until they break. That is why lineage mapping has to happen before cutover, not after the first support ticket.
Start with evidence instead of interviews. Query logs and access logs show which accounts read which objects, and BI metadata shows which reports use which tables. Then list every scheduled job, API and file transfer that touches the source. Ask each consumer’s owner to confirm their item works against the target.
The early warning sign is an object with recent reads and no owner on your list. Treat each one as a blocker until someone claims it.
6. Extended Downtime and Failed Rollback
Downtime depends on how much data must be in sync at the moment users switch. With a full freeze, the outage lasts as long as the final load and checks. With change data capture , which logs inserts, updates and deletes on the source, the target keeps catching up while users work. The outage then shrinks to the time it takes to drain the last of the lag.
CDC brings its own cutover risk. If lag grows during peak hours, the final catch-up runs long and the window overruns. Watch lag in every rehearsal, and switch only when it sits below an agreed limit.
On SQL Server, the sys.dm_cdc_log_scan_sessions view reports latency in seconds for each recent log scan session. Polling it during a rehearsal shows whether lag holds steady or climbs at peak load.
SELECT TOP (5) session_id, start_time, end_time, latency
FROM sys.dm_cdc_log_scan_sessions
WHERE session_id > 0
ORDER BY session_id DESC;Rollback also has a deadline that many plans miss. The source stays a valid rollback target only until the new system accepts its first real writes. After that, going back means losing those transactions or replaying them in reverse, which is a second migration in itself. So define the rollback point in hours, rehearse it and decide in advance who can call it.
Kanerika Service
Data Migration Services from Kanerika
Kanerika plans and runs migrations with dependency mapping, rehearsed cutovers and automated validation, so rollback points and go/no-go checks are set before the first table moves.
Explore Migration Services 7. Security, Privacy and Compliance Exposure
A migration also creates copies. Extracts sit in staging buckets, test environments get loaded with production data and log files capture sample rows. Each copy therefore needs the same data security controls as the production system, and each one is easy to forget.
Getting this wrong is expensive. IBM’s Cost of a Data Breach Report 2026 puts the global average cost of a breach at USD 4.99 million. That is a record high and 12% more than the year before. Under Article 83 of the GDPR , the most serious violations can draw fines of up to EUR 20 million or 4% of worldwide annual turnover. The higher of the two applies.
Three controls close most of the gap:
Encrypt data in transit and at rest, including staging areas, exports and backups.
Mask or tokenize personal data before it reaches any test or development environment.
Review access on the target, because role mappings rarely carry over cleanly, and check each dataset’s storage region against residency rules.
8. Performance Regressions and Target Cost Spikes
A query that ran in two seconds on the old system can take two minutes on the new one. Indexes, statistics, partitioning and query plans do not travel with the data, and the target engine may need a different design to perform well.
On cloud platforms, slow queries also cost money. Consumption billing charges for compute time or data scanned. An unpartitioned table that every dashboard scans in full shows up on the invoice within days.
Run the heaviest queries from the source workload against the target before cutover, compare timings and set budget alerts from day one. If you are still weighing platforms, the guide to choosing Azure migration tools by workload and the roundup of data migration tools compare the options.
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Estimate Your Migration ROI
Put your own numbers into Kanerika’s migration ROI calculator before you commit to a target platform.
Calculate Migration ROI → 9. Testing Gaps and False Go-Live Confidence
A green test report proves only what the tests covered. A plan that checks row counts and a handful of sample records will pass a migration with broken business logic, as the opening example showed. The real risk is confidence that the evidence does not support. Suppose a discount applies only to orders above a volume threshold. A test of 50 random sample rows may never touch one of those orders. The rule can break and the report still goes green.
Write acceptance criteria from business rules, sign them before testing starts and include at least one full rehearsal on production-sized data. Test types and validation techniques are covered in depth in Kanerika’s guide to data migration testing .
10. Ownership Gaps, Scope Creep and Early Source Retirement
Migrations stall when nobody owns the decision, which is the gap a data governance framework closes by naming an owner for each domain. A dataset without a named business owner has nobody to sign acceptance and nobody to rule on a data quality problem. Nobody says no when a stakeholder adds “just one more” system in month three, either.
Key-person risk sits close by, too. If one engineer understands the legacy jobs, the project carries that person’s calendar as a risk. Pair them with a second engineer early and write down what they know.
The last trap comes at the end. Decommissioning the old system before retention, legal hold and audit needs are signed off can destroy records the business must keep. When retired hardware is wiped, follow NIST SP 800-88 Rev. 2 , the media sanitization guidelines NIST published in September 2025, so the data is truly gone and the process is documented.
How to Run a Data Migration Risk Assessment
A data migration risk assessment turns the ten risks into decisions for your own project. For every dataset it answers three questions: what could go wrong, how bad would it be and what will we do about it. The output is a risk register that the team reviews every week until go-live.
Identify risks per dataset. Walk each source table or domain through the ten risks and note which apply. A customer master table may carry quality and privacy risk, while a large fact table carries volume and performance risk.Score likelihood and impact from 1 to 5. Multiply the two for a score between 1 and 25, and use evidence such as profiling results or past incidents wherever you have it.Assign a control, an owner and a trigger. The trigger is the measurable sign that a risk is turning into an issue, such as a variance above an agreed threshold.Set go/no-go rules. Decide in advance which scores block go-live and which can ship with monitoring.
One simple banding works for many projects. Scores of 15 to 25 get fixed before go-live. Scores of 8 to 14 need a control and active monitoring, while scores of 1 to 7 are accepted and logged. Adjust the bands to your own tolerance, but write them down before anyone starts scoring.
Example Risk Register
The register below is an example for a finance data warehouse move. Its scores are illustrative, chosen to show how the bands drive action, and they are not benchmarks.
Risk Likelihood Impact Score Control Owner Trigger
Unit price rounding changes in invoice lines 4 5 20 Reconcile revenue by month in a parallel run Finance data steward Any revenue variance above tolerance CDC lag overruns the cutover window 3 5 15 Lag monitoring, switch only below the agreed limit Platform engineer Lag above the limit for 15 minutes Undocumented report reads a legacy view 3 4 12 Lineage scan and consumer sign-off BI lead Recent reads from an unowned account Personal data copied to unmasked staging 2 5 10 Masking in the pipeline, access review Privacy officer Unmasked fields found in a staging scan Compute cost rises after go-live 3 3 9 Query tuning and budget alerts FinOps lead Weekly spend above forecast Legacy server retired before retention sign-off 2 4 8 Retention approval gate in the decommission plan Records manager Decommission ticket without approval
In this example, the first two rows block go-live until their controls are proven. The other four ship with monitoring, and the team rescores all six every week as evidence comes in.
Go/No-Go Conditions
Go-live should be a decision against written conditions, not a date on the calendar. A typical set looks like this:
No open risk scores 15 or higher without a proven control.
Business totals reconcile within the agreed tolerance for every critical dataset.
The rollback has been rehearsed, and its deadline is known.
Every consumer on the lineage map has confirmed access on the target.
The business owner of each critical dataset has signed acceptance.
Which Cutover Approach Carries the Least Risk?
The least risky cutover approach is the one that fits your downtime tolerance, rollback need and budget. Each option moves risk to a different place. Big bang packs it into one window, while phased and parallel approaches spread it out but keep two systems alive for longer.
The guide to types of data migration defines each approach in detail. The table below compares them on risk alone.
Approach Downtime Rollback window Cost Complexity Best fit
Big bang Longest, one planned outage Short and clear, until new writes begin Lowest Low to medium Small or medium datasets where a weekend outage is acceptable Phased Short outages per wave Set per wave Medium Medium Large estates that split cleanly by region, unit or domain Parallel run Close to none for users Long, because the old system stays live Highest, since two systems run and get reconciled High Finance and regulated data where logic must be proven Trickle with CDC Minutes at the final switch Shrinks as the target takes writes Medium to high High Always-on systems that cannot freeze writes
The two lowest-downtime options carry the most complexity. Less outage time is bought with more moving parts, and every moving part is one more place for a risk to hide.
One rule turns the table into a choice. Time the final load and checks in a rehearsal, then add the time a rollback would take. If that total fits inside the outage the business accepts, a rehearsed big bang is usually the simplest pick.
If it does not, use phased waves when the estate splits cleanly by domain, and CDC when writes can never stop. Add a parallel run for any dataset where a wrong total would reach finance or a regulator.
Worked Example: Moving ERP Reporting
Picture a manufacturer moving ERP reporting from an on-premises warehouse to a cloud data warehouse . Finance closes the books on the fifth working day of each month, and plant managers check production dashboards every shift.
A big bang cutover would put the whole finance close at risk in a single weekend, which the CFO will not accept. A pure trickle migration would keep plant dashboards fresh, but it adds CDC work the team has never run. So the team picks phased waves by business area, and plant reporting goes first because its logic is simple and its users spot problems fast.
Finance goes last, with one full month-end close run on both systems at once instead of a single switch. The team switches only when the two sets of totals agree. Each wave also gets its own rollback point before any user writes to the target.
Risk Controls Before, During and After Cutover
Controls work best when they are tied to a moment in the project rather than to a general intention. Each list below names, in brackets, the risk it closes, so every control traces back to the register. For the full phase-by-phase task list, use the data migration checklist .
Before Cutover
Profile every source table and agree on fixes (risk 3).
Write the type-mapping specification and test it on real samples (risk 4).
Build the lineage map and collect consumer sign-off (risk 5).
Mask personal data before it reaches any test environment (risk 7).
Rehearse the full cutover and rollback on production-sized data (risks 6 and 9).
During Cutover
Freeze or capture writes on the source at the agreed time (risk 6).
Monitor CDC lag and load errors live (risks 1 and 6).
Reconcile counts, sums and checksums per table (risk 1).
Compare the business owner’s key totals (risk 2).
Make the go/no-go call against the written conditions (risks 9 and 10).
After Cutover
Keep reconciling business totals daily through the first close cycle (risk 2).
Track query times and spend against forecast (risk 8).
Review target access logs for unexpected users (risk 7).
Keep the source and backups until the rollback deadline and retention sign-off pass (risks 6 and 10).
Sanitize retired media and record the evidence (risk 10).
The after list is the one teams cut first when a deadline tightens. It is also where silent corruption usually surfaces, often in the first month-end close, so protect it in both the plan and the budget. Once the project team moves on, data observability checks on freshness, volume and schema keep watching for the same signs.
Downstream Risk: Analytics, Semantic Models and AI
The damage from a migration error rarely stays in the database, because data now travels further. Migrated tables feed semantic models, dashboards, machine learning features and AI agents, and each layer can turn a small error into a large one.
Consider a currency field that the new platform rounds before conversion instead of after. In the warehouse, each row is off by only a fraction of a cent, but those fractions add up. In the Power BI model, a measure sums millions of those rows, and the regional revenue figure moves.
A forecasting model trained on that revenue history then learns a small, false trend. The planning team acts on it, and nobody connects the forecast back to a rounding rule.
Three controls contain this risk. First, reconcile business totals at the semantic layer, because that is the number executives see. Second, freeze semantic definitions such as measures, hierarchies and filters during the migration, so any change in output can only come from the data. Third, revalidate model features and recheck any model whose inputs moved before its predictions drive decisions.
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Worried About What a Migration Does to Your Reports?
Kanerika scores your migration risks, maps the reports and models that depend on each dataset and plans the reconciliation that proves they still match.
Talk to a Migration Expert → Common Mistakes That Raise Data Migration Risk
The same handful of decisions shows up again and again behind migrations that go wrong. Each one feels reasonable under deadline pressure, which is exactly why the table pairs every mistake with the reason it hurts and a practical fix.
Mistake Why it hurts Fix
Treating equal row counts as proof Counts miss changed values and broken logic Reconcile sums, checksums and business totals Choosing tools before assessing risk The tool shapes the plan instead of the data Run the risk assessment first, then pick tools that close the top risks Assuming phased is always safest Each wave adds coexistence, sync and reconciliation work Choose by downtime tolerance and rollback needs Never restoring a backup An untested backup may not restore in time, or at all Restore it during rehearsal and time the restore Using the source as rollback after new writes Rolling back loses every transaction written to the target Set a rollback deadline before the first real write Letting the deadline override failed acceptance Known defects go live and become production incidents Make go/no-go conditions binding and sign them in advance Retiring legacy before retention sign-off Records the business must keep can be destroyed Add a retention approval gate to the decommission plan
If two or more of these sound familiar, slow down before the next wave rather than after it. A short pause to fix the plan costs far less than a rollback.
How Kanerika Reduces Data Migration Risk
Kanerika runs data and analytics migrations for enterprises moving off legacy ETL, BI and warehouse platforms, often as one step in legacy system modernization . We start from the risk register rather than the tool list. The estate gets scored first, and then we choose the path and automation that close the highest risks.
The aim is a migration where the business can see the evidence for go-live, not just a green status report. In practice that means reconciliation at the business-total level, rehearsed cutovers and a rollback point that everyone knows in advance.
Where Automation and FLIP Reduce Risk
FLIP is Kanerika’s family of IP-led migration accelerators for ETL, BI and RPA platforms. Each accelerator automates three stages where manual work tends to introduce errors: discovery, conversion and validation.
Automated discovery builds the inventory of source objects from the system itself rather than from memory, which is where hidden dependencies (risk 5) usually start. Machine conversion of code also cuts the hand-rewriting that causes logic drift (risk 2). Automated validation runs support the reconciliation behind risks 1 and 2.
Automation does not remove the need for judgment, though. People still decide which business rules are correct, which data quality problems are acceptable and when the evidence is strong enough to go live. The FLIP migration accelerators datasheet lists what each accelerator covers.
Case Study: 90% Logic Accuracy Moving Crystal Reports to Power BI
Challenge. A leading material handling service provider, operating across multiple regions, had relied on Crystal Reports for many years. Report preparation was slow and manual, reports were static and isolated, and even small changes needed the IT team.
Solution. Kanerika automated the extraction and conversion of the Crystal Reports logic into Power BI, which removed manual rework from the riskiest step. The team also integrated Power BI with Oracle Cloud ERP and set up drill-down dashboards under centralized governance. The guide to Crystal Reports to Power BI migration explains the platform steps behind this kind of move.
Results. The migration achieved 90% logic accuracy, keeping the integrity of the legacy reports, and cut reporting time from months to days. That is a direct answer to logic drift (risk 2). It also shows why review stays in the plan, since any logic that conversion does not carry across exactly still needs a person to find and confirm it. The full Crystal Reports to Power BI case study has the details.
Case Study
90% Logic Accuracy in Crystal Reports to Power BI Migration
A material handling service provider moved years of Crystal Reports to Power BI with automated logic conversion, reaching 90% logic accuracy and cutting reporting time from months to days.
Read the Case Study → Bottom Line: Control Data Migration Risks With Evidence
Data migration risks are manageable when they are named, scored and owned before the first table moves. Start with a risk assessment per dataset and reconcile business totals instead of row counts. Then choose the cutover approach by downtime tolerance and rollback needs rather than habit.
So what are the biggest data migration risks, and how do you control them? They are the ten in this guide, from data loss to early source retirement. You control them with evidence, meaning a scored register, a rehearsed cutover and rollback, and sign-off from the people who own the numbers.
Frequently Asked Questions
What are the biggest risks of data migration?
The biggest data migration risks are data loss, silent corruption of values and business logic, poor source quality, schema and encoding mismatches, hidden dependencies, downtime with failed rollback, security exposure, performance and cost spikes, testing gaps and unclear ownership. The silent ones do the most harm, because counts still match while the numbers people rely on quietly change.
Why do data migration projects fail or run over budget?
Projects fail or overrun when complexity surfaces late. Undocumented business logic, poor source data and unknown downstream consumers appear during testing instead of assessment, so rework piles up near the deadline. Weak ownership makes it worse, because nobody can approve trade-offs quickly. A scored risk assessment at the start moves those discoveries to the cheapest point in the project.
How do you build a data migration risk register?
List each risk against the dataset it affects, then record likelihood, impact and their product as a score from 1 to 25. Add the control that closes it, a named owner and a measurable trigger that shows the risk turning into an issue. Agree score bands before scoring, so high scores automatically block go-live until fixed.
How can data migration be done with minimal downtime?
Use change data capture or a trickle approach, so the target keeps catching up while users work and the final switch only drains the remaining lag. Watch replication lag during every rehearsal, switch when it sits below an agreed limit, and schedule cutover outside peak hours. Phased waves also limit how much any single outage affects.
What is a rollback plan in data migration?
A rollback plan defines how to return users to the source system if cutover fails, who can call it and by when. The deadline matters most, because the source stays a valid rollback target only until the new system accepts real writes. After that point, going back means losing or replaying transactions, which becomes a second migration.
Which is safer, big bang or phased data migration?
Neither is safer in every case. Big bang suits small or medium datasets where a planned weekend outage is acceptable, because it keeps a short, clear rollback window and one system to run. Phased suits large estates that split cleanly by region or domain, but each wave adds coexistence, synchronization and reconciliation work.
What are the risks of migrating data to the cloud?
Cloud moves add their own risks on top of the usual ones. Consumption billing turns slow or unpartitioned queries into cost spikes, role mappings and network rules may not carry over, and data residency rules decide where datasets may be stored. Performance can also drop when indexes and query plans do not suit the new engine.
How do you verify data integrity after migration?
Compare source and target at several levels. Check row counts, sums of key numeric columns and row hashes per table, then reconcile the business totals that owners actually use, such as revenue by month or inventory by plant. Keep reconciling daily through the first close cycle, because some errors only appear when reports run end to end.
How do you prevent data loss during data migration?
Reconcile every table at three levels after each load. Compare row counts to catch missing records, sums of key numeric columns to catch changed values, and row hashes to catch changes that cancel out. Keep tested backups, log rejected rows instead of discarding them, and rehearse the full load on production-sized data before the real cutover.
How do you keep data secure and compliant during migration?
Treat every copy as production data. Encrypt data in transit and at rest, including staging areas, exports and backups. Mask or tokenize personal data before it reaches test environments, review access on the target because role mappings rarely carry over cleanly, and confirm each dataset’s storage region against residency rules before go-live, then review access logs afterward.
How does automation reduce data migration risk?
Automation removes the manual steps where errors creep in. Automated discovery builds the source inventory from the system itself, conversion tools cut hand-rewritten logic, and validation jobs reconcile source and target at scale. People still decide which business rules are correct and when evidence is strong enough to go live, so automation supports judgment rather than replacing it.
How can companies reduce the overall risk of a data migration project?
Start with a risk assessment per dataset, then score each risk by likelihood and impact and give the high scores an owner, a control and a trigger. Profile source data early, map lineage, rehearse cutover and rollback, and set written go-live conditions. Reconciling business totals rather than row counts closes the risk that most teams miss.
What are the signs of poor data migration governance?
Warning signs include datasets with no named business owner, acceptance criteria written after testing starts, scope that keeps growing without a formal decision, and go-live dates that override failed checks. Another sign is a risk register nobody updates. When nobody can say who signs off a dataset, the project is carrying unmanaged risk into cutover.
Is data migration safe?
Data migration is safe when its risks are managed with evidence rather than assumed away. Moving data always creates exposure, including extra copies, transformation errors and a cutover window. Projects that profile data, encrypt and mask copies, reconcile business totals, rehearse rollback and follow written go-live conditions routinely migrate large estates without losing data or trust.
What is data migration risk?
Data migration risk is the chance that moving data between systems causes it to be lost, changed, exposed or unavailable, together with the business harm that follows. That harm includes wrong reports, missed deadlines, compliance penalties and higher running costs. A risk becomes an issue once it happens and a failure once it reaches users or regulators.
What is a data migration risk assessment?
A data migration risk assessment is a structured review of what could go wrong for each dataset in a migration. The team identifies applicable risks, scores likelihood and impact from 1 to 5, multiplies them, and assigns each risk a control, an owner and a trigger. The result is a risk register reviewed weekly until go-live.
