Why data quality makes the difference
A transition rarely involves copying data from A to B. It involves a controlled transfer of data while preserving history, definitions, and dependencies. If this isn’t done properly, you often only notice it later. For example, when:
- Data turns out to be incomplete or has been duplicated
- Definitions don’t match
- Changes can no longer be traced back to the source
- Exceptions continue to circulate in Excel lists
- Corrections after going live take longer than the entire schedule allowed
Data quality is therefore not just an IT issue. It also involves governance, risk management, communication, reputation, and continuity.
Five pitfalls we often see during transitions
- Data quality is discussed too late as a topic
Many processes start with technology, planning, and migration approach. Understandable, but if data quality is only structurally incorporated later, adjustments are difficult and expensive.
Result: stress at the end and remediation work at the beginning of the new situation - No one truly owns the data
Ownership lies with the pension fund! However, due to outsourcing, for example, it’s not always clear to everyone. As a result, it sometimes remains unclear who decides on definitions, exceptions, corrections, and acceptance criteria.
Result: Issues remain untouched or are postponed to the future. - Data is poorly traceable
During transitions, data goes through multiple steps: extraction, transformation, enrichment, and checks. Without good traceability, it becomes difficult to quickly answer questions, such as where the value comes from.
Result: uncertainty in management, operations, and audits. - Temporary solutions emerge that stick
Additional exports, “quick” overviews, separate files for checks. This happens in almost every transition. The problem arises when temporary working methods become permanent.
Result: fragmentation, additional risks, and less control. - There are no firm quality agreements
If you don’t agree in advance on what is good enough, it becomes a discussion at the end. Should we go live or not? While you should be managing based on facts.
Result: Last-minute escalations and unclear decision-making.
Five practical solutions to maintain control
It doesn’t have to be perfect. But it does need to be manageable. But what helps?
- Make data quality concrete: what must be correct?
Start with focus. Which datasets are critical? Which fields are decisive? What are the minimum requirements for completeness and consistency? - Establish ownership per dataset
Appoint data owners who can make decisions. Not to do everything themselves, but to ensure direction and accountability. - Enable traceability by default
Ensure you can explain where the data comes from and what has been done with it. That makes the difference between guessing and managing. - Make quality measurable with fixed checks
Work with clear controls and thresholds. Think of: completeness checks, reconciliation between source and target, deviation reports, and an overview of exceptions. If you make it measurable, you can actually manage it. - Only switch when everything is correct (with a clear go/no-go)
A transition requires a controlled handover. Not based on gut feeling, but on clear acceptance criteria and insight into deviations.
Migration service
At DataTrust Conclusion, we support these types of processes with our migration service. A controlled transition to a new environment, focusing on data quality, insight, and manageability. So you can transition with confidence, without data loss, and without surprises later on.
