Treat every data migration like it will go wrong
Data migrations are often treated as routine technical tasks, but they carry far more risk than many teams assume. In an article shared by software engineer and data analyst Sumit Gundawar on Dataconomy.com, he argues that the safest and most effective approach is to assume that every migration has the potential to fail—and to plan accordingly. A central idea in the article is that data migrations fail quietly compared to application failures. While software bugs typically trigger visible errors or system crashes, data migration issues can silently corrupt records, overwrite values, or introduce inconsistencies without immediate warning. The system may appear to run normally while decisions are being made on incorrect data. Gundawar stresses that this risk is amplified when teams run “one-off” scripts or quick fixes directly in production without proper testing, validation, or review. These shortcuts often bypass the safeguards that exist in application development, even though migrations directly impact the organization’s most critical asset: its data. To mitigate this, he recommends adopting a “failure-first” mindset. This includes rigorous testing in safe environments, peer review of migration scripts, validation checkpoints, and clear rollback plans. It also means treating data changes with the same discipline as production code deployments, rather than ad hoc administrative work. Ultimately, Gundawar reinforces that data migrations should never be assumed safe by default. Organizations that anticipate failure, build in safeguards, and validate aggressively are far more likely to avoid silent data corruption and long-term operational issues.
