Data migration is the most consistently underestimated element of any custom software implementation. Organizations plan for development, testing, training, and change management. They allocate budget for infrastructure and licenses. And then they discover that migrating their existing data into the new system is as complex, as risky, and as time-consuming as any other phase of the project.
This article explains what data migration involves, why organizations consistently underestimate it, and what they must plan for before committing to a custom software implementation.
What Data Migration Actually Involves
Data migration is the process of moving data from one or more existing systems into a new system. At its simplest, it is exporting data from the old system and importing it into the new one. In practice, it is almost never that simple.
Existing systems organize data according to their own assumptions, which rarely match those of a new system. For example, field names and data structures may differ. A single record in the old system may need to become several related records in the new one. Similarly, the migration process may need to convert free-text information into structured fields.
Data quality in existing systems is typically lower than organizations expect. Duplicate records. Inconsistent formatting. Required fields left empty. The source system may imply relationships between records rather than define them explicitly. Therefore, teams must identify and assess every data quality issue. They must then clean each issue before migration or address it during the migration process.
Why Data Migration Is Consistently Underestimated
The Data Has Not Been Looked at Closely
Organizations often do not have a clear picture of the state of their own data until they begin a migration project. When teams analyze the data for migration, they may discover quality problems for the first time. As a result, they may need to perform unplanned deduplication work. Historical records in formats that no longer match the current data model. Data entry inconsistencies that accumulated over years without anyone noticing because the old system did not require consistency.
The Mapping Work Is Not Accounted For
Teams must map every field in the old system to a corresponding field in the new system. Alternatively, they must define a transformation rule that converts the data into the required format. This work requires both technical and business knowledge. Finally, people who understand the data’s operational meaning must review and approve the completed mapping document.
Historical Data Volume Is Larger Than Expected
Organizations often underestimate how much historical data they must migrate. Seven years of customer records, transaction history, project files, and correspondence is a lot of data. Migration processes need to handle that volume, and the testing required to validate that historical data migrated correctly adds significant time to the overall project.
What to Plan for Before the Migration
A Data Audit
Before any migration work begins, audit the data in the existing systems. What data exists, how much of it is there, what its quality looks like, what relationships exist between records, and what the known quality problems are. This audit produces the information required to plan the migration accurately and avoids the discovery of major quality problems mid-migration when they are most disruptive.
A Migration Strategy Decision
Organizations need to decide what data to migrate and at what level of detail. Migrating complete historical data is often not necessary or practical. Custom software development firms with migration experience can help organizations think through the trade-offs between migrating all historical data, migrating a defined period of historical data, or migrating summary data with the full detail available in the old system as an archive.
Data Cleaning Before Migration
Cleaning data quality problems before migration is almost always more efficient than handling them during migration. Deduplication, format standardization, and required field population done in the source system reduces migration complexity and produces cleaner data in the new system.
A Validation Plan
The team must validate the migration before go-live. Not a sample check, but a systematic validation that the migrated data is complete, accurate, and correctly related. This validation takes time and requires people who understand what correct data should look like to review it.
Frequently Asked Questions
A focused migration from one source system with clean data can take two to four weeks. In contrast, migrations involving multiple systems, poor data quality, or large historical volumes may take two to four months. Ultimately, the timeline depends on the quality and complexity of the source data and the effort required to map it to the new system.
Teams may fail to identify data quality problems in the source system before migration begins. They may also underestimate the data volume or the complexity of mapping old structures to new ones. In addition, project plans may not provide enough time for validation. As a result, teams may launch the new system with data errors that disrupt operational processes.
The right approach depends on operational requirements and migration costs. When teams need historical data for ongoing operations, regulatory compliance, or reporting, they should migrate the complete history. However, when they need older data mainly for reference, they can migrate a defined period and archive the rest.
The old system may fail to identify duplicate records. Key fields such as names, addresses, and identifiers may use inconsistent formats. The new system may also require information that the old system treated as optional or did not capture. In addition, teams may need to define relationships that the old system only implied. Historical records may also fail to meet current data standards.
Technical staff who understand both the source system data structure and the target system requirements. Business staff who understand what the data means operationally and what correct data looks like. Compliance staff if regulated data is involved. Senior leadership for decisions about what data to migrate and what to archive.
