Data Migration: The Underestimated Challenge of Moving to SAP S/4HANA

Data Migration: The Underestimated Challenge of Moving to SAP S/4HANA

Custom code gets the attention. Data quality is what actually determines whether a go-live date holds — and it is rarely treated with the discipline the risk deserves until it has already caused a delay.

Every SAP S/4HANA migration plan includes a line item for data migration. Very few include a line item sized to reflect how often data, rather than code, is the reason a carefully planned go-live date does not hold. Custom code is visible, countable, and testable in a way that invites disciplined planning. Data quality is diffuse — spread across master data, transactional history, and years of accumulated exceptions — and it has a way of surfacing its worst problems only during integration testing or, worse, after go-live, when a report someone relies on suddenly produces numbers that do not reconcile.

This is not a niche technical concern. It is, by a reasonable measure, one of the largest quantifiable costs an organization carries into a migration without realizing it, and it is entirely addressable with the right governance discipline applied early enough to matter.

Gartner’s research on data quality, drawing on its 2020 analysis, puts the average cost of poor data quality to an organization at a minimum of $12.9 million per year — a figure that reflects the ongoing operational drag of decisions made on unreliable data, not a one-time migration expense. That number is a useful reframe for how organizations should think about data cleansing during an S/4HANA migration: it is not simply a migration cost to be minimized, but an opportunity to eliminate a recurring cost the organization has likely been absorbing, unmeasured, for years.