Planning a Secure Infor LN Data Migration

An Infor LN data migration often determines whether the go-live proceeds smoothly or whether problems only become apparent during live operation. What is critical is not only the technical migration itself, but above all the question of which data is truly needed, must be cleaned up, and must be transferred to the target environment in a valid format.

We support data migrations in a way that ensures risks are identified early on, tests are based on reliable data, and the cutover does not become a source of uncertainty.

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Typical Challenges in Data Migration

Unclear Data Responsibility

Inconsistent or Outdated Master Data

Time Pressure During the Cutover

Strict Traceability Requirements

Why Data Migration Is Critical

In short: Data migration is not merely an import process. It is a risk, quality, and organizational issue at the heart of every ERP migration.

Many companies come into a project with a clear expectation: the data must be complete, accurate, and available by the target date. At the same time, a migration almost always changes the data structure. This is precisely where the challenge lies: what has evolved over the years in the legacy system does not automatically fit into the new target environment without modification.

There is also a second problem: errors aren’t just technical in nature. In many projects, it’s unclear who is technically responsible for certain data sets, which information is still relevant, and which legacy issues are best left behind. If these questions aren’t clarified in time, the risk is shifted to the testing phase and go-live.

Why Completeness Does Not Equate to Quality

Not all available information is automatically worth migrating. If outdated, duplicate, or inconsistent data is transferred without being verified, problems from the legacy system will be carried over into the new environment.

That is why, in practice, the following often holds true: Less data - but clean data - leads to a more stable start.

What Data Should Actually Be Transferred

Only the data that will actually be needed in future operations should be transferred—not everything that could technically be migrated.

So the key question isn't: What can we migrate?
But rather: What do we really need for future operations?

An Infor LN data migration should be guided by the intended future use:

What data is needed for day-to-day operations?
What information is relevant for subsequent processes?
Which open transactions need to be processed further in the target system?
What historical data must remain available for reporting, documentation, or service purposes?

Master data that directly controls operational processes is particularly business-critical. Typical examples include:

Product and Material Master Data
Customer and Supplier Master Data
Pricing and Terms Data
Bills of Materials and Work Plans
Inventory and MRP Data
Financial Master Data

Master data, in particular, quickly reveals whether processes have been properly maintained over the years or whether redundancies, gaps, and inconsistencies have arisen.

Movement data is often particularly sensitive because it directly affects ongoing processes. Examples include:

Sales Orders
Orders
Contracts
Service Orders
Inventory
Open Inventory or Goods Movements

The key factor here is which transactions are open as of the cutoff date and must be carried forward to the target system without interruption. Missing orders, purchase price agreements that were transferred incorrectly, or purchase orders that were not migrated can directly impact operational processes and material planning.

Data with no operational value - that is, anything that increases complexity without being needed in the target system - should be left out. Typical candidates for deliberate exclusion are:

Outdated contact and address information
Items or partners that are no longer in use
Historical data with no operational relevance
Inconsistent data records that do not provide reliable value

Selective data migration reduces complexity, storage requirements, and the risk of errors.

How an Infor LN Data Migration Works

A reliable data migration follows a clear roadmap. The goal is not to wait until just before go-live to determine whether the data is correct, but to ensure its quality through a series of steps.

Step 1: Define the data foundation and the target vision

The first step is to determine which tables, data objects, and information areas should be migrated in the first place. This involves not only technical decisions but also the business-oriented target vision:

What structures apply in the target system?
What data is still needed there?
What dependencies exist between tables and processes?
What technical preparations are required?

It is important to note that the target vision often becomes more precise as the project progresses. Therefore, the data migration must be designed to be adaptable from the very beginning.

Step 2: Ensure Quality, Completeness, and Order

The next step focuses on quality and interrelationships:

Are the tables complete?
Are the references and dependencies correct?
In what order must the data be imported?
What is done automatically, and what is done manually?

Especially in established Infor LN landscapes, referenced tables, split data sets, and future target structures must be taken into account early on. Otherwise, errors will not become apparent until processes are tested.

Step 3: Evaluate Additional Fields and Runtimes

Many LN environments contain customer-specific extensions. Therefore, the following must be clarified:

Which additional fields and tables are relevant?
Which adjustments affect the data transfer?
How long do the export, transfer, and import actually take?
Which manual steps can be avoided?

This step is important for realistically assessing not only the data logic but also the technical feasibility.

Step 4: Plan Transaction Data Separately

Master data and transaction data should not be automatically scheduled using the same transfer route. Transaction data such as orders, purchase orders, or service transactions often follow their own logic and require separate scheduling.

The key point is:

What transaction data must be open as of the cutoff date?
How is this data transferred?
How are follow-up processes ensured?

Step 5: Prepare for the dress rehearsal and set up the training environment

Before the go-live, a thorough dry run is required. This phase reveals whether:

the migration sequence works,
the runtimes fit within the cutover window,
the data quality is sufficient for final testing,
and training and functional testing can be performed using realistic data.

By this point at the latest, there should be no major surprises.

Step 6: Ensure a Smooth Cut-Over

Reliability is key during the cutover. The final data migration must be coordinated in terms of timing, technical aspects, and organizational arrangements:

clear time frames
defined responsibilities
transparent verification steps
clear escalation procedures in case of deviations

The better the preliminary steps are prepared, the lower the risk during the final migration window.

Which methods have proven effective

There is no single tool that works for every migration. The key is to choose the approach that best fits the data type, target architecture, and project situation.

For master data in large volumes and large amounts of data, automated import is often the best approach. It is particularly useful in cases where:

Data structures are clearly defined,
transformations must be reproducible,
test runs are performed repeatedly,
and large volumes of data need to be processed cost-effectively.

Not everything is worth automating. For smaller data sets or specific types of master data, manual or semi-manual maintenance may make more sense, for example when:

the number is manageable,
a technical assessment is required on a case-by-case basis,
the effort required for automation would be disproportionate.

Transaction data often requires a specialized approach. Open transactions, balances, or transaction-related data must be transferred in such a way that processes in the target system can continue to run consistently. A clear separation between master data logic and transaction logic is crucial here.

A strict 1:1 approach may seem low-risk at first, but it is not the best approach for many projects. Reasons for this may include:

Changes to processes in the target system
Additional or modified master data requirements
Resolution of inconsistent data
Reorganization of established structures
Reduced overhead in the target environment

Especially when migrating to more modern target structures, selective migration is often more sensible than a blind, full migration.

Where Data Quality Breaks Down in Mature LN Environments

Data quality deteriorates in established LN landscapes, especially where many small changes have accumulated over the years:

Outdated or duplicate master data
Information maintained inconsistently
Lack of consistency across data records
Contradictory references and inconsistencies
Legacy issues from previous process versions

Why Migration Is an Opportunity for a Fresh Start

An ERP migration is not just a technical change. It is also a good opportunity to make targeted improvements to the database:

Eliminate unnecessary baggage
Refine relevant data
Clarify responsibilities
Restructure master data
Ensure quality for future processes

How Testing and Validation Ensure Safety

The sooner data is available in the target environment, the more realistically processes can be tested. This is precisely where a major factor in project reliability lies.

Importing data early on provides transparency regarding:

Master Data Structures
Transaction Data
New Process Logics
Future Workflows in the System

Why Early Test Imports Are So Valuable

Early test imports help ensure that problems aren't identified only at the last minute. Key users and process owners can use realistic data to verify:

whether the structure is appropriate,
whether processes can be executed,
whether information is complete,
whether customizations and software enhancements work,
whether transformations have been implemented correctly from a technical standpoint.

How Test Migrations Reveal Vulnerabilities

Gaps and errors often only become apparent during test runs. This is not a failure, but rather part of a proper migration process. What’s crucial is that this leads to structured feedback:

Document the problem

Identifying Errors

Narrow Down the Cause

Customize the transformation

Improve the database

Retake the test

FAQ on Infor LN Data Migration

No. In many projects, it makes more sense to import only the data that will actually be needed for future operations, open tasks, or relevant analyses. Selective data import reduces complexity and often improves data quality.

Through early test runs, realistic runtime measurements, clear migration sequences, and a coordinated cutover plan. It is crucial not to wait until the final migration window to evaluate the migration under realistic conditions for the first time.

Technology alone is not enough. Responsibilities must be clearly defined among IT, business units, key users, and project management. Data relevant to the business can only be properly evaluated with the help of the appropriate stakeholders.

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