Moving to a new system? The software is the easy part

Ask anyone who has moved a business from one system to another what the hardest part was.

Very few will say the new software.

Almost all of them will talk about the data.

The customer that exists in three systems. The item codes created years ago that no longer make sense. Supplier records with missing or inconsistent details. Duplicate accounts. Old codes that the new system will not recognise.

None of this shows up in the software demo.

But all of it has to be resolved before go-live.

The migration problem nobody sees in the demo

In many ERP, CRM, HCM and other business system implementations, data migration still relies heavily on spreadsheets, one-off scripts and knowledge held by a small number of people.

Someone exports the data. Someone else profiles it. Business users identify what needs to change. Mapping rules are documented in spreadsheets. Records are corrected. Another extract arrives and parts of the process start again.

The people doing this work are usually careful.

The problem is that the process itself is difficult to control.

·   Nobody is completely sure which spreadsheet is current.

·   Mapping and transformation rules can live in individual people's heads.

·   Each new source extract creates more manual work.

·   Issues are often discovered late in testing.

·   Reconciliation becomes another spreadsheet exercise.

·   Cutover depends heavily on a handful of people getting everything right at the right time.

And when the business finally goes live, it can still be difficult to prove that everything that came out of the old system arrived correctly in the new one.

We built the Data Migration Accelerator to change that

The AYLA Data Migration Accelerator provides a structured workbench for profiling, mapping, transforming, validating and reconciling migration data.

Build the migration once. Rehearse it repeatedly.

Load the source data and the accelerator shows you what is actually there - not what you expect to be there.

·   Duplicate records can be identified and resolved.

·   Source values can be mapped to the values required by the new system.

·   Transformation rules are defined once and reapplied consistently.

·   Data quality issues are identified before they reach the target system.

·   Exceptions that require a business decision are surfaced rather than quietly guessed.

·   Mapping decisions, rules and fixes remain recorded in one place.

·   Migration runs can be validated and reconciled before sign-off.

The repetitive work is automated, while guided decision prompts help the business focus on the choices that can improve the quality and structure of the data going into the new system.

Your first full migration run shouldn't happen on cutover weekend

This is where repeatability changes the project.

New extract from the legacy system on Friday? Run the migration again. Found another set of bad records? Correct them and run it again. Changed a mapping rule? Apply it and rerun the data.

Instead of treating each migration cycle as another manual exercise, the same process can be repeated throughout build, testing, dress rehearsals and cutover.

By the time the real changeover arrives, the migration has already been rehearsed multiple times. The cutover weekend becomes much less exciting. Which is exactly what you want.

Across our projects, this approach has reduced hands-on migration effort by around 60%. Migration activities that previously required hours of spreadsheet work can be rerun in minutes, allowing the project team to spend more time resolving genuine data and business issues rather than repeatedly manipulating files.

What does that mean for the project?

Know whether the data is ready

Instead of relying on a subjective view of migration readiness, project teams can see data quality issues, unresolved exceptions and what still requires a decision. The go-live date becomes less of a guess.

Repeat rather than rebuild

Mappings, transformations and business rules are retained and reapplied to each subsequent source extract. A new dataset does not mean starting the migration process again.

Rehearse before cutover

Migration cycles can be repeated throughout the implementation, allowing issues to be identified and resolved well before the final migration weekend.

Improve the quality of data entering the new system

Duplicates, invalid values, inconsistent codes and missing information can be identified before loading rather than discovered by users after go-live.

Keep migration knowledge with the project

Mappings, transformation logic and decisions remain documented within the process rather than disappearing when a spreadsheet owner or team member moves on.

Reconcile what moved

Validation is built into the migration process. Records can be counted through the migration and discrepancies identified before the migration is signed off.

The decisions still stay with your business

Not every migration decision should be automated.

·   Which customer record is correct?

·   What should an old classification code become?

·   Which historical balances should move?

·   Should an incomplete record be corrected, excluded or accepted?

And sometimes the more important question is whether the legacy structure should be carried forward at all.

The Data Migration Accelerator includes guided decision prompts based on the questions an experienced migration lead or subject matter expert would typically ask, helping business users challenge legacy data before simply moving it into the new system.

·   Do you really need to carry forward this many product categories?

·   Is this an opportunity to introduce a new product naming convention?

·   Should similar classifications be consolidated?

·   How much historical data genuinely needs to move?

·   Are legacy codes and structures still appropriate for the future-state system?

The accelerator puts these decisions in front of the people who understand the business, together with the relevant data and context. Once a decision is made, the resulting mapping, rule or transformation is retained and applied consistently in subsequent migration runs.

The accelerator does not just help move the data. It helps the business decide what the data should look like in the new system.

Designed to work alongside your ERP, CRM or HCM implementation

The Data Migration Accelerator does not replace your ERP, CRM, HCM platform or implementation partner.

It provides a controlled layer around the migration process, helping project teams manage the work between legacy systems and the new platform.

Profile  >  Map  >  Transform  >  Validate  >  Load  >  Reconcile  >  Repeat

Whether you are moving to a new ERP, CRM, HCM or another core business platform, the objective is the same: make migration repeatable, traceable and progressively less risky as you approach go-live.

See what it looks like with your own data

We built the accelerator because we kept seeing experienced project teams spend too much time repeating work that a machine could carry.

The technology should do the repetitive work. Your people should spend their time resolving the decisions that genuinely need them.

If you have an ERP, CRM, HCM or other major system migration coming up, send us a representative source extract.

We can show you how the Data Migration Accelerator profiles the data, identifies migration issues, surfaces expert-led decision prompts, manages mappings and transformations, and prepares the same dataset to be run again.

Bring us a sample of your migration data. We will show you what a repeatable migration could look like before you get anywhere near cutover.