Across more than ninety engagements spanning platform migrations, successor fund transfers, and large-scale programme deliveries, the pattern is consistent: the bottleneck is never the platform, the vendor, or the programme timeline. It is data quality — and it is almost always underestimated at the outset.
This is not a new observation. Fund administrators and their advisers have known for decades that legacy member data carries accumulated errors, gaps, and inconsistencies. What has changed is the cost of ignoring it. In an environment where regulators are demanding clearer member outcome reporting and where platform consolidation continues to accelerate, poor data quality has become a programme-stopper rather than a programme inconvenience.
The data quality tax
Every fund that has operated on an administration platform for more than a decade has accumulated what we call a data quality tax — a compounding debt of incomplete records, address mismatches, duplicate accounts, legacy benefit structures, and undocumented business rules that live in the heads of experienced staff rather than in any system of record.
This tax becomes payable the moment a fund attempts a material change: a migration to a new administration platform, a merger with another fund, or a significant compliance remediation. At that point, data that has been adequate for day-to-day processing becomes inadequate for the precision that transformation requires.
Three failure modes we see repeatedly
- Late discovery. Funds that begin data cleansing work after platform selection and contract execution are routinely forced to extend timelines, absorb additional cost, or — in the worst cases — delay go-live while member records are remediated in parallel with testing.
- Scope underestimation. Initial assessments that focus on field-level completeness miss the more damaging structural issues: inconsistent benefit categories, undocumented employer relationships, and contribution histories that reflect business rules no longer in use.
- Internal dependency. Data remediation work is often allocated to internal teams who are simultaneously supporting business-as-usual operations. The result is a programme that is technically resourced but practically under-delivered.
What funds should do differently
The single most effective intervention is to bring data quality work forward — before platform selection, before detailed business requirements, and well before system integration begins. A structured data assessment at the outset of a programme creates a factual basis for vendor conversations, realistic timeline setting, and resourcing decisions that reflect the actual complexity of the work ahead.
Funds that do this consistently deliver faster, with fewer surprises and lower total programme cost. Those that defer it consistently discover the same problems later, at a point where the cost of addressing them has multiplied.
If your fund is planning a migration or platform change in the next twelve to twenty-four months, the most valuable investment you can make right now is a rigorous assessment of the data you are planning to move.