ERP change, merger-acquisition, new e-commerce platform... These digital transformation projects all involve a major risk that many businesses underestimate: the deterioration of data quality.
This risk is all the more insidious as it only fully becomes apparent after production is launched, when operational teams are confronted with missing, corrupt, or inconsistent information. The consequences are then quick to be felt: lost invoices, inaccurate stocks, redundant customer communications, flawed reporting...
Experience shows that failed migration projects often share one thing in common: they treated data as a simple technical asset to be moved from one system to another, without considering its qualitative dimension or its business impact.
Duplication is probably the most common mistake during migrations. It occurs when deduplication rules have not been clearly defined or when source systems already contain unidentified duplicates. The fusion of two customer bases can thus generate duplicate profiles, leading to redundant communications that irritate your customers and degrade your image.
Even more insidious are the fields left empty or partially filled in during the transfer. This missing data can block entire processes: a product without a price code becomes invisible in your catalog, a customer without a complete address cannot receive orders.
Technical differences between systems create discrepancies that often go unnoticed until production is launched. A simple divergent date format (MM/DD/YYYY vs DD/MM/YYYY) can turn January 12 into December 1, with disastrous consequences for deadline management.
Business codifications are also a problem: a product category coded “HAB” in the old system and “VET” in the new system is either lost or misclassified after migration. These technical misalignments, invisible to non-specialists, can paralyze entire departments.
Without a real shared “data dictionary”, confusion quickly sets in. What was called “monthly turnover” in the old system may have a slightly different definition in the new system. These nuances of understanding generate contradictory interpretations of the same indicators between teams.
This lack of a common frame of reference also makes it impossible to make reliable automatic reconciliations between systems impossible, forcing teams to resort to manual manipulations that are prone to errors and time-consuming.
The historical dimension of data is often sacrificed on the altar of simplification. Change logs, order histories or customer interaction timelines disappear, making it impossible to analyze trends or reconstruct journeys.
This loss of history creates a blind spot in your ability to understand past behaviors and predict future trends. It also deprives you of crucial information in the event of an audit or dispute.
Any successful migration starts with a precise mapping of the existing situation. This audit phase consists of comprehensively identifying your data sources, their formats and their interdependencies. It also reveals areas at risk: inconsistent fields, existing duplicates or contradictory business rules.
This step, often overlooked for reasons of economy or time, is nevertheless the most profitable investment in your project. By revealing the problems in advance, it saves you from having to make emergency corrections that are much more expensive after production.
Cleaning the source data beforehand is a critical step. It's not just about transferring your data, it's about improving it in the process. Correct detected errors, remove duplicates, standardize formats, and enrich incomplete information.
This cleaning phase is the ideal opportunity to clean up your information assets. A customer with three different phone numbers in your current base should not maintain this inconsistency after migration.
The mapping of correspondence between systems forms the technical core of your project. For each source field, define exactly how it will be transformed and where it will be placed in the target system. These transformation rules must be documented and validated by business experts.
Remember to identify data that has no direct equivalent, which will require specific treatments. How do I deal with a “customer comment” field if it does not exist in the new system? These particular cases, neglected in overly optimistic schedules, are often responsible for the greatest post-migration disorders.
Progressive validation on pilot datasets is your best protection against unpleasant surprises. Select samples that cover all of your use cases, including the most atypical ones. Transfer them and then check their compliance meticulously.
This test phase should actively involve your business users. Only they can validate that the migrated data meets their operational needs. Their feedback will allow you to refine your process before full deployment.
Vigilance should not end on the day of the changeover. Set up continuous monitoring that verifies the quality and completeness of the data. Plan rapid correction procedures for unavoidable anomalies that will be discovered during production.
Establish a validation circuit involving end users, who can report problems detected during their daily use. This continuous feedback loop will allow you to progressively improve the reliability of your new system.
The success of a migration depends as much on human as on technical factors. Centralize project management under a single responsibility to avoid the dilution of decisions. Set up a governance committee representing all stakeholders, with clear decision-making authority.
The support of the teams remains decisive. Strengthen your user support during the critical transition period. Train your employees not only to use the new system, but also to understand the changes in the data structure. A well-prepared team will identify anomalies more quickly and limit their spread.
Finally, documentation is the sustainable heritage of your project. Carefully archive decisions made, mapping rules, and technical arbitrations. This information will be valuable for understanding certain system behaviors, but also for preparing your future evolutions.
A well-managed migration represents much more than a simple technical transfer: it is an opportunity to transform your information assets into a real strategic asset. By applying these principles, you won't just avoid classic pitfalls — you'll create a solid foundation for your data excellence.
Data quality is not an accessory luxury but the very foundation of your ability to innovate, understand your customers and optimize your operations. In a world where data is becoming the fuel for artificial intelligence and automation, this information excellence is a sustainable competitive advantage that your less rigorous competitors will not be able to match.
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