TL;DR
In data conversion vs data migration, pick migration when data changes systems and conversion when its shape changes; most replatforming needs both. Data migration moves records, history, metadata and permissions from one system to another. Data conversion changes formats, schemas, code values or units, and it can also happen without any move.
A same-version lift to the cloud is mostly migration, while an in-place character-set upgrade is pure conversion. Two tests settle it: does the system of record change, and do formats, codes or rules change? When both answers are yes, as in ERP replacements, plan mapping, conversion and reconciliation as separate workstreams.
Key Takeaways
Data migration changes where data lives, while data conversion changes the shape of the data itself.
Conversion can run in place with no move, while migration can run with almost no conversion.
Two tests decide the mix: does the system of record change, and do formats, codes or rules change?
Conversion effort grows with mapping rules and data quality debt, whereas migration effort grows with volume, downtime limits and dependencies.
Code conversion, such as rebuilding Informatica or SSIS pipelines, is a third workstream that needs its own team.
Neither job is done until counts, keys, control totals and reports reconcile and a data owner signs off.
Why One Project Plan Hides Two Different Jobs
Picture a distributor replacing a 20-year-old ERP with a cloud suite. The project plan has just one data line, which says to move customer, product and order history. So the budget assumes a copy job with some testing at the end.
Three months later, the first trial load fails. Dates arrive as text in two different formats. Country names are spelled four ways, and the old status codes 1, 2 and 9 mean nothing to the new system.
None of that is a moving problem. Instead, it is a conversion problem that was hiding inside the migration line all along. As soon as the team sees it, they need mapping workshops, business owners and validation rules that nobody costed.
That is why the data conversion vs data migration question matters before a project starts. One job changes where the data lives, while the other changes what the data looks like and what its values mean. Most enterprise replatforming contains both, so the mix decides the schedule, the team and the risk.
This guide defines each job, shows where they overlap and then walks through a worked mapping example. It also gives you two tests for scoping your own project.
Data Conversion vs Data Migration at a Glance
The quickest way to separate the two is to ask what each one changes. Migration changes the address of the data, while conversion changes its form. The table below compares them on the points that drive planning.
Point of comparison Data conversion Data migration
Question it answers What shape must the data take? Where must the data live? What changes Format, encoding, schema, code values, units, business rules System, platform, storage or location Scope Fields, tables and the rules applied to them Whole datasets plus history, metadata, permissions and dependencies Typical trigger New target model, new standard, merged systems, format upgrade Platform retirement, cloud move, consolidation, hardware refresh Effort driver Number and complexity of mapping rules, data quality debt Volume, transfer window, downtime tolerance, dependencies Main risk Wrong meaning, truncation, lost precision Missing records, downtime, access exposure, broken links Proof of done Converted values pass rules and the owner signs the mapping Counts and totals reconcile and the source can retire Typical tools Transformation code, schema converters, dbt, ETL tools Replication and migration services, bulk loaders
In practice the two columns rarely stay apart. Blurred scope between them often explains why modernization programs slip, so the overlap section later in this guide matters as much as the definitions.
Watch on YouTube
Enterprise Data Migration Failure: 5 Causes That Derail Modernization
Kanerika walks through five causes that derail data migration and modernization programs, including the scoping gaps that let conversion work surface mid-project.
What Is Data Conversion?
Data conversion is the process of changing data from one format, structure or set of values into another. The aim is to change it so that a target system can read and use every value correctly. The data may stay exactly where it is, because what changes is how it is written and what each value means.
A simple case, for example, is turning a CSV export into Parquet files. A harder case is turning a legacy status code of 9 into the word “Closed” because the new system expects text. Both are conversion, but the second one needs a business owner to confirm what 9 really meant.
What Changes During Conversion
Conversion touches the content and structure of data rather than its location. It usually involves one or more of these changes.
Format: file types and storage formats, such as fixed-width text to CSV or CSV to Parquet.Encoding: character sets, such as EBCDIC to UTF-8 when mainframe data moves to open systems.Schema: table structures, data types, field lengths and relationships.Code values: lookup codes, flags and categories mapped to the target’s reference lists.Units and rules: currencies, measures, date conventions and derived business logic.
Each of these can also break silently. For example, a field that shrinks from 100 to 50 characters cuts off addresses without raising an error, so conversion always needs its own checks.
Three Kinds of Conversion
It helps to sort conversion work into three kinds, because each one needs different people. Format work is mostly engineering. Structural work needs data modelers, while semantic work needs the business.
Kind What it changes Example Who decides the rule
Format conversion File type, encoding, data type representation CSV to Parquet; EBCDIC to UTF-8 Data engineers Structural conversion Schema, keys, relationships, table layout One wide customer table split into customer, address and contact tables Data modelers and architects Semantic conversion Code values, units, business rules, meaning Status 1, 2, 9 mapped to Active, Inactive, Closed; amounts restated in one currency Business data owners
Semantic conversion is where projects stall, since the rule often lives in people’s heads rather than in the code. Shared definitions held in a governed catalog therefore help, and our guide to enterprise data governance shows how teams set them up.
Kanerika Service
Data Governance Services
Kanerika helps teams agree on shared definitions, reference codes and ownership, so semantic conversion rules are decided once and reused across systems.
Explore Data Governance Services How Data Conversion Works, Step by Step
A sound conversion follows the same six steps whether the job is small or large. The order matters, because each step produces evidence the next one depends on.
Profile the source: measure formats, null rates, distinct values and outliers before you write any rule.Map source to target: write a field-level mapping document that names each source field, its target field and the rule between them.Build the rules: turn the mapping into transformation code or tool settings, with a reject path for values that fail.Run on a sample: convert a representative slice, including the ugly records, before touching the full dataset.Validate the output: compare converted values against the rules, then check rejects and test the reports built on the result.Get owner sign-off: finally, a named business owner approves the mapping and the reject handling before production runs.
Teams that skip the sample run usually meet their worst records during cutover weekend. Running the rules on a slice first is cheap, so surprises turn into a backlog you can schedule.
Case Study
60% Faster Data Processing: Informatica to Microsoft Fabric
Kanerika moved a global consumer electronics brand’s Informatica flows to Microsoft Fabric with FLIP and set one governed rule set for data structure and KPIs, cutting infrastructure costs by 42%.
Read the Case Study → What Is Data Migration?
Data migration is the process of moving data from one system, platform or storage location to another. Once the move is done, the old system is retired or repointed. The new system becomes the trusted home for that data, so everything that depended on the old location must keep working.
For example, moving an on-premises SQL Server database to a managed cloud database is a migration. When the engine and version stay compatible, the records may arrive almost unchanged. The work then centers on volume, downtime, security and the applications that connect to the database.
What Moves During Migration
A migration moves more than rows. History has to travel with the current records, because reports that compare years break on day one without it. Metadata such as table definitions, constraints and lineage has to arrive too.
Permissions are easy to forget and painful to lose. When a migration copies data but not access rules, it either locks users out or exposes records to the wrong people. Dependencies close the list, since jobs, reports, APIs and data pipelines all point at the old system and need repointing.
Common Migration Types
Migrations are usually grouped by what moves. Each type below also has its own risks and tooling, and our guide to the types of data migration covers them in depth.
Storage migration: data moves to new disks, arrays or object storage, often with no change in form, such as a hardware refresh.Database migration: data moves to a new database engine or version, which can also pull in schema conversion.Application migration: an application is replaced or rehosted, so its data must fit the new data model.Cloud migration: data moves from on-premises systems to a cloud platform, such as Azure, or between clouds.Business process migration: data follows a merger or redesign, so several systems become one.
How Data Migration Works, Step by Step
Migration has its own sequence, and it is built around a safe cutover rather than around rules. These seven steps fit most projects, although the timing varies.
Inventory the source: before planning anything, list datasets, volumes, owners, history depth and every system that reads or writes them.Prepare the target: build the target structures, security model, network paths and capacity before any load.Choose big-bang or phased: move everything in one window, or move in waves while old and new systems run side by side.Load the data: run bulk loads for history, then use change capture or replication for data that keeps changing.Reconcile: compare counts, keys and totals between source and target, then explain every difference.Cut over: freeze writes, run the final sync, then repoint applications and confirm users can work.Retire the source: archive what policy requires, then shut the old system down so nobody keeps using it.
The last step is the one projects defer. An old system left running quietly becomes a second source of truth.
Checklist
Data Migration Checklist
Work through inventory, target preparation, cutover planning, reconciliation and source retirement with a step-by-step checklist for your next migration.
Get the Checklist → How Conversion and Migration Overlap
The clearest way to see the overlap is to ask two separate questions about any project. Does the data change location, and does it change shape? Since each answer is yes or no, every project lands in one of four boxes.
A storage refresh changes location only, whereas an in-place Unicode upgrade changes shape only. An ERP replacement changes both, while routine upkeep changes neither. The rest of this section looks at each combination that matters in practice.
Conversion Inside a Migration
Much of the hard work in a large migration is conversion that happens on the way. Moving from Oracle to PostgreSQL means converting data types, sequence and identity syntax, and PL/SQL procedures into PL/pgSQL, because the two engines handle each differently.
While the move itself may take a weekend, conversion design, testing and owner reviews can take far longer. So plans that treat the job as one line item tend to slip.
Migration Without Conversion
Some moves really are lift-and-shift. For example, copying files to a new storage array keeps every value intact. So does moving a database to the same engine and version on new hardware.
Even so, a little conversion often sneaks in. Collation settings, time zones and file paths can differ on the new host, so a quick profile of the target is still worth doing.
Conversion Without Migration
Conversion can also run while the data stays put. For example, a team might upgrade a database’s character set to Unicode in place. Another team might rewrite CSV archives as Parquet in the same storage account.
Nothing moves in either case, but every value is rewritten. The risks are pure conversion risks, such as truncated text, changed precision and broken downstream parsers.
Which Comes First?
There are two common orders, and the choice depends on where the transformation power sits. In the classic order, you extract from the source, convert in a staging area and then load the clean result.
The modern ELT order, by comparison, loads raw data into the target first and converts it there. Teams often use it with cloud warehouses, and our data lake vs lakehouse guide explains where each fits. Loading first keeps a raw copy for audit, so you can rerun conversion rules without going back to the source. Converting first, in contrast, keeps bad data out of the new system entirely, which some regulated teams prefer.
Code Conversion Is Not Data Conversion
Platform moves bring a third job that people confuse with data conversion. Code conversion rewrites the pipelines, scripts and reports that process the data, such as SSIS packages or Informatica mappings rebuilt for a new platform.
Data conversion, on the other hand, changes the records themselves. An Informatica to Fabric migration usually needs both, because the mappings become new pipelines that then convert and load the data. An SSIS to Fabric migration works the same way. Scope and staff the two separately, since code conversion needs platform engineers while data conversion needs data owners.
Worked Example: Mapping a Legacy Customer Record
A concrete record shows how much conversion hides inside a “simple” move. Suppose a company migrates customer records from a legacy order system into a new cloud CRM during a wider ERP replacement.
The table below follows seven fields from the old record into the new one. For each field, it names the rule, the kind of conversion involved and the check that proves the rule worked.
Source field Target field Rule Kind Validation check
CUST_NO customer_id Carried over unchanged as the primary key None Every source ID present once in the target OPEN_DT (text, DD/MM/YY and MM-DD-YYYY) account_open_date Parse both patterns, write as an ISO 8601 date Format No unparsed dates; no dates in the future COUNTRY (free text) country_code Map names and spellings to ISO 3166-1 alpha-2 codes Semantic Every value found in the reference list PHONE phone_e164 Strip symbols, add country prefix, store in E.164 form Format Pattern check; rejects logged with reason STATUS (1, 2, 9) account_status 1 to Active, 2 to Inactive, 9 to Closed Semantic No unmapped codes; counts per status match source BALANCE (“USD 1,200.50”) balance_amount, balance_currency Split one text field into a decimal amount and a currency code Structural Sum of amounts per currency matches source ADDRESS (one free-text line) street, city, postal_code Parse into parts; send unparseable lines to review Structural Parsed share tracked; review queue cleared
Six of the seven fields change shape, even though the business described the job as moving customers. Only the customer ID passes through untouched, and that is exactly why it anchors every reconciliation check.
Reading the Reconciliation Result
Now take an illustrative load to see how reconciliation reads. Assume the source holds 250,000 customer records. Once the load finishes, the target holds 249,640 records and the reject log holds 360, each with a reason.
Since 249,640 plus 360 equals the 250,000 extracted, the counts tie out. Still, the work is not finished yet. Balance totals per currency must also match the source, and a data owner must review the 360 rejects before anyone signs.
Data Conversion vs Data Migration: How to Decide
Deciding comes down to two plain tests that anyone on the project can answer in a meeting. Run them early, because the answers set the team, the timeline and the budget lines.
Test 1: Does the System of Record Change?
If users will read and write this data in a different system, platform or location after the project, you have a migration. If the data stays in the same system, there is no migration. Upgrading SQL Server 2016 to 2022 on the same host, for example, keeps the system of record. Moving that database to Azure SQL changes it, so that project is a migration.
Test 2: Do Formats, Schemas, Codes or Rules Change?
If any field is reshaped, retyped, remapped or recalculated, you have conversion work. A field that goes from VARCHAR(100) to VARCHAR(50) is a yes, and so is a status code 9 that must become “Closed”. When every value and structure arrives unchanged, there is no conversion to plan.
Five Questions That Size Each Side
Two yes answers mean both jobs, so the useful question becomes which one dominates. These five follow-up questions help you size each side.
How many source systems feed the target, especially ones that use different codes for the same thing?
Which fields need a rule rather than a straight copy?
How long can the business tolerate the system being read-only during cutover?
What reports, jobs and integrations depend on the source, and which will break once it moves?
What evidence will auditors or regulators expect once cutover is complete?
Eight Common Scenarios and Their Verdicts
The scenario table below applies the two tests to eight common projects. Use it as a starting point, and then adjust for your own data.
Scenario Test 1: system changes? Test 2: shape changes? Verdict Dominant effort
On-premises SQL Server to Azure SQL, compatible version Yes Little Migration Transfer window, cutover, security and connections Oracle to PostgreSQL Yes Yes Both Schema, data type and procedure conversion ERP replacement Yes Yes Both Mapping, code values and business rules Informatica to Microsoft Fabric Yes Yes Both Pipeline code conversion plus data movement CSV archive rewritten as Parquet in place No Yes Conversion Data types and file layout Character-set upgrade to Unicode in place No Yes Conversion Field lengths and truncation checks Two CRMs merged into one Yes Yes Both Matching, de-duplication and code harmonization Storage array refresh Yes No Migration Copy speed, checksums and cutover
The first row is a common starting point for teams migrating data to Azure . It stays mostly migration so long as the engine version stays compatible. Once the tests have sized the two jobs, the next decision is the destination, and our framework for choosing the target platform covers that step.
Migration ROI Calculator
Estimate the Return on Your Migration
Use Kanerika’s Migration ROI Calculator to estimate the savings and payback of moving your data platform, once you know how much conversion and migration work it holds.
Calculate Migration ROI → Where Data Transformation and ETL Fit
Two more terms crowd this topic, and both describe methods rather than project goals. Data transformation is the general act of changing data, so conversion is one kind of transformation done to fit a target system.
Term What it means Relation to conversion and migration
Data transformation Any change to data values or structure, including cleansing, enrichment and aggregation Conversion is the subset done to fit a target system ETL or ELT A pipeline pattern that extracts, transforms and loads data, in either order A common way to run both conversion and migration Data integration An ongoing process that keeps data from many systems combined and current Runs after a migration; a migration is a one-time move
ETL is a tool for doing the work, while migration and conversion describe the outcome you need. For example, a one-off ETL run that moves and reshapes data is a migration with conversion inside it. When the same pattern runs nightly for years, it is integration instead.
Our guide to data integration vs ETL explains that split in detail. For the ingestion side, also see data ingestion vs data integration , and the ETL framework guide covers how to structure the pipelines.
Tools for Data Conversion and Data Migration
Tools tend to lean toward one job, so it pays to know which side each one covers before you shortlist. Some services move data well but leave mapping rules to you. Others transform data well but assume it is already in place.
Datasheet
FLIP Migration Accelerators
See which legacy platforms FLIP converts, including Informatica, SSIS, IBM DataStage, SAP BusinessObjects and Qlik Sense, and what each accelerator automates.
View the Datasheet →
Tool Role Best fit Verified note
AWS DMS with DMS Schema Conversion Both Moving databases to Amazon Aurora or RDS DMS Schema Conversion converts source schemas for Aurora and RDS targets, builds on the AWS SCT engine and can use generative AI to convert more codeAzure Database Migration Service Migration Moving SQL Server databases to Azure SQL A fully managed service per Microsoft Learn ; offline moves to Azure SQL Database and online moves to Azure SQL Managed Instance Fabric Migration Assistant for Data Warehouse Migration Moving warehouses into Microsoft Fabric The Migration Assistant supports Azure Synapse dedicated SQL pools and SQL Server, with Teradata metadata in preview; you upload a DACPAC or connect to the source Oracle GoldenGate Migration Low-downtime moves that need continuous replication GoldenGate provides real-time data integration and replication across different environmentsQlik Talend Cloud Both Integration and transformation across many sources Talend is now part of Qlik and sold as Qlik Talend Cloud Informatica IDMC Both Large enterprise integration and data management estates Informatica’s cloud platform is IDMC; Salesforce completed its acquisition of Informatica on November 18, 2025 dbt Conversion SQL transformations inside the target warehouse dbt pricing lists a free Developer plan for one seat, Starter at $100 per user per month and custom Enterprise; the core code is Apache-2.0 licensedFLIP (Kanerika) Both Converting legacy pipelines and reports to modern platforms FLIP accelerators cover Informatica, SSIS, IBM DataStage, SAP BusinessObjects and Qlik Sense moves
Which Tool Owns the Business Rules?
No single tool owns the semantic rules. Even the best migration service will copy a status code of 9 faithfully, because only your data owners know that 9 means Closed.
Teams therefore keep each rule on record outside the tool, in two places. The signed mapping document says what the rule is and who approved it. Version-controlled transformation code, such as dbt models, says exactly how it runs. When the team swaps a tool, the rules survive because they never lived only inside it.
For the wider market, our round-up of data integration tools also compares more options.
What Drives Cost, Effort and Timeline
Cost follows effort, and effort comes from different places on each side of the project. Conversion cost rises with the number of rules and the mess in the source data. Migration cost rises instead with volume, downtime limits and the systems that depend on the data.
Published price bands rarely survive contact with a real data estate, so the reliable approach is to size the work from a pilot. Convert and move one representative domain, such as customers, and measure the hours it takes. Then scale that number by the drivers below.
Driver Raises conversion effort Raises migration effort
Number of sources Yes, because each source brings its own codes and formats Yes, because each source needs its own extract and cutover plan Mapping complexity Yes, strongly Slightly Data quality debt Yes, strongly Slightly, through rejected loads Volume and transfer window Slightly, through longer test runs Yes, strongly Downtime tolerance No Yes, since low tolerance needs replication and phased cutover Downstream dependencies Yes, when reports read converted fields Yes, since every job and API needs repointing Validation depth Yes Yes Regulatory evidence Yes, for rule approval records Yes, for reconciliation and access records
Read the table by row rather than by column. For instance, a project with heavy mapping and messy data needs more analysts and owner time. In contrast, a project with huge volumes and a two-hour outage window needs replication tooling and timed rehearsals.
Risks, Validation and Common Mistakes
Every conversion and migration fails in its own typical ways, so naming those ways early is the cheapest protection. Because the risks differ by job, the controls differ too.
Conversion risks are about meaning, since a value can arrive intact but wrong, cut short or rounded. Migration risks, on the other hand, are about completeness and access, such as missing rows, longer outages or permissions that did not travel.
Risks and the Control for Each
Job Risk Control
Conversion Wrong meaning (code mapped to the wrong value) Owner-approved mapping document; counts per code compared before and after Conversion Truncation of text fields Profile maximum lengths; reject and log values that do not fit Conversion Lost precision in numbers and dates Compare sums and checksums on numeric fields; test time zone handling Migration Missing records Count and key reconciliation per table, with every gap explained Migration Downtime beyond the window Timed rehearsals; replication for changing data; a rollback plan Migration Access exposure Migrate and test permissions before users arrive Migration Broken dependencies Dependency inventory; repoint and test every job, report and API
Access controls deserve the same rigor as the data itself. A good set of data governance tools makes permissions easier to compare before and after the move.
Reconciliation Checks Before Cutover
Reconciliation is the evidence that both jobs worked. Run these checks in order, and do not cut over until each one passes or has a signed explanation.
Record counts match per table once logged rejects are counted.Keys and orphans check out, so every source key exists in the target and no child record lost its parent.Checksums or hash totals match on fields that should stay unchanged during conversion.Control totals match on financial and operational figures, such as balances per currency.Report parity holds when the main reports run on both the old and new systems.The rejected-record log is reviewed before sign-off, with a decision recorded for each reject.Finally, a data owner signs off on the results, the rejects and the mapping.
The first and fourth checks can also run as one query against a source extract and the loaded target. Here is a simple pattern, assuming both tables sit in the same warehouse once the load ends.
SELECT 'source' AS side, balance_currency, COUNT(*) AS records, SUM(balance_amount) AS total
FROM staging.customer_source_extract
GROUP BY balance_currency
UNION ALL
SELECT 'target', balance_currency, COUNT(*), SUM(balance_amount)
FROM crm.customer
GROUP BY balance_currency
ORDER BY balance_currency, side;Any currency where the two sides differ goes back to the reject log before sign-off. For a deeper set of controls, see our guide to data quality during migration and our walkthrough of data migration testing .
Common Mistakes to Avoid
The same mistakes repeat across projects of every size. Most of them come from treating conversion as a detail of the move rather than a job of its own. The table links each mistake to what it costs and to the part of this guide that covers the fix.
Mistake What it costs Where this guide covers the fix
Budgeting a “data move” with no line for mapping and owner reviews Unplanned mapping workshops and owner time mid-project Why One Project Plan Hides Two Different Jobs Writing conversion rules before anyone profiles the source Rules that miss the real formats, nulls and outliers How Data Conversion Works, step 1 Letting engineers guess what legacy codes mean Values that load cleanly but mean the wrong thing Three Kinds of Conversion Testing only on clean sample records The worst records surface on cutover weekend How Data Conversion Works, step 4 Checking record counts but not control totals or reports Wrong amounts pass as a complete load Reconciliation Checks Before Cutover Copying data without permissions and access rules Users locked out, or records exposed to the wrong people What Moves During Migration Leaving the old system running after cutover A second source of truth How Data Migration Works, step 7 Treating code conversion and data conversion as one workstream Platform engineers asked to decide what business codes mean Code Conversion Is Not Data Conversion
Each mistake is cheap to prevent at the planning stage but expensive to fix after go-live. Good data observability after cutover also catches the problems that slip through.
How Kanerika Delivers Conversion and Migration Projects
Kanerika treats every platform move as two linked workstreams, one for moving the data and one for reshaping it. Our data migration services therefore pair automation for the repeatable work with experts who own the rules that need judgment.
Watch on YouTube
How Kanerika Automates Informatica to Microsoft Fabric Migration | Complete Walkthrough
See FLIP convert Informatica mappings into Microsoft Fabric pipelines step by step, the code conversion half of a platform move, with the validation that follows.
The automation comes from FLIP , our migration accelerator. FLIP converts legacy pipelines and reports, such as Informatica, SSIS and IBM DataStage workloads, into code for modern platforms. The Azure to Fabric Migration Accelerator, powered by FLIP, has been available as a native Fabric workload since FabCon 2026 on March 19, 2026. Because it runs as a workload, teams can start an Azure to Fabric migration from their own Fabric workspace.
Automation does not decide what a code means, though. Instead, our data engineers run the profiling, write the mapping documents with business owners and keep the reject log honest.
Final sign-off stays with the client’s data owner, since the reconciliation evidence described above backs every approval. Our data integration services then keep the new platform fed once the move is complete.
Case Study: Both Jobs in One Informatica to Fabric Move
A global consumer electronics brand ran batch-heavy Informatica pipelines that slowed refreshes. Its regions also used different transformation rules and KPI definitions.
Kanerika’s migration and pipeline conversion work moved the high-volume flows to Microsoft Fabric with FLIP. The data conversion work set one governed rule set for data structure and KPIs across regions. After both, the brand reported a 60% improvement in data processing speed, a 42% reduction in infrastructure costs and 76% faster time-to-insights.
Kanerika is a Microsoft Solutions Partner for Data and AI and a Microsoft Fabric Featured Partner. It also holds the Data Warehouse Migration to Microsoft Azure specialization and is a Databricks Consulting Partner.
Kanerika Service
Data Migration Services
Kanerika plans and runs data migrations with FLIP automation for pipeline conversion and experts who own mapping rules, reconciliation and sign-off.
Explore Migration Services The Verdict: Conversion, Migration or Both?
Choose data migration when the system of record changes and the data can arrive as it is. Choose data conversion when the data stays put but its format, schema, codes or rules must change.
Plan for both when the two tests both say yes, as they do for ERP replacements, engine changes, system mergers and pipeline modernization. In those projects, give conversion its own owners, mapping documents and sign-off rather than folding it into the move.
So which do you need, data conversion or data migration? Ask whether the system changes and whether the shape changes, then staff and budget each yes as its own job.
Frequently Asked Questions
What is the difference between data conversion and data migration?
Data migration moves data from one system, platform or location to another, and the new system becomes its home. Data conversion changes the data’s format, schema, code values or units so a system can use it. Migration changes where data lives, and conversion changes its shape. Many enterprise projects, such as ERP replacements, need both jobs.
What is data conversion?
Data conversion is the process of changing data from one format, structure or set of values into another. Examples include turning CSV files into Parquet, changing EBCDIC text to UTF-8, or mapping legacy status codes to new labels. The data can stay where it is. The goal is that the target system reads every value correctly.
Which comes first, conversion or migration?
Either order works. In the classic ETL order, data is extracted, converted in a staging area and then loaded into the target. In the ELT order, raw data is loaded first and converted inside the target platform. Loading first keeps a raw copy for audit, and converting first keeps bad data out of the new system.
Is ETL a data migration?
ETL is a pipeline pattern that extracts, transforms and loads data, and it is a common way to run a migration. A one-time ETL run that moves data from a retiring system into a new one is a migration with conversion inside it. The same pattern running nightly to keep systems in sync is data integration.
What is the difference between data migration and data transformation?
Data migration moves data to a new system or location. Data transformation is any change to data values or structure, including cleansing, enrichment, aggregation and conversion. Transformation can happen during a migration, inside a daily pipeline or in a report. Conversion is the part of transformation done specifically to fit data to a target system.
What are the biggest risks in data conversion vs data migration?
Conversion risks are about meaning, such as codes mapped to the wrong value, truncated text and lost numeric precision. Migration risks are about completeness and access, such as missing records, outages longer than planned, exposed permissions and broken reports or jobs. Each risk needs its own control, from mapping sign-off to count, key and total reconciliation.
How do you validate data after a migration?
Start by matching record counts per table after logged rejects. Then confirm every source key exists in the target, compare hash totals on unchanged fields and match financial control totals. Run the main reports on both systems and compare results. Finally, review every rejected record and get a data owner to sign off before cutover.
What is the purpose of data conversion?
The purpose of data conversion is to make data usable by a system that expects a different format, structure or meaning. It lets new applications read old records, aligns codes across merged systems and applies standards such as ISO 8601 dates. Done well, it protects the meaning of each value while changing how that value is stored.
What are the three types of data conversion?
A practical way to group conversion is into three kinds. Format conversion changes file types, encodings and data types. Structural conversion reshapes schemas, keys and table layouts. Semantic conversion changes code values, units and business rules, such as mapping status 9 to Closed. Semantic work needs business owners, because the rules often live in people’s heads.
What is an example of data conversion?
A common example is converting legacy customer records for a new CRM. Dates stored as text become ISO 8601 dates, country names become two-letter ISO codes, and phone numbers are rewritten in E.164 form. Another example is rewriting an archive of CSV files as Parquet in the same storage location, with no move at all.
What is an example of data migration?
Moving an on-premises SQL Server database to Azure SQL is a typical data migration. The records, history, users and permissions move to the new platform, applications are repointed, and the old server is retired. If the engine version stays compatible, the data can arrive almost unchanged, so the work centers on transfer, downtime and security.
What are the four types of data migration?
Four commonly cited types are storage migration, database migration, application migration and cloud migration. Storage moves data to new hardware or object storage. Database migration changes the engine or version. Application migration moves data into a new application’s model. Cloud migration moves data to a cloud platform. Business process migration is often listed as a fifth type.
Is data conversion always part of data migration?
No. A storage refresh or a move to the same database engine and version can keep every value and structure unchanged. Still, most large migrations include some conversion, because new systems rarely share the old data types, codes and rules exactly. Profile the target early so hidden conversion work shows up before the budget is set.
Can data migration happen without data conversion?
Yes. Copying files to a new storage array, or moving a database to identical engine software on new hardware, is migration with no conversion. Small differences can still appear, such as collation, time zone or file path settings on the new host. A quick comparison of source and target settings confirms whether any conversion is needed.
Can data conversion happen without data migration?
Yes. Conversion can run in place while the data stays in the same system. Upgrading a database character set to Unicode, or rewriting stored CSV files as Parquet, changes every value’s form without moving it anywhere. These projects carry pure conversion risks, such as truncated text, lost precision and parsers that no longer read the files.
