In my experience, data mapping is never a simple 1:1 or 1:many exercise because these processes don’t take into account the “swivel seat” knowledge base. The users who bridge systems with their knowledge. This knowledge base is largely unaccounted for, not valued, and unseen. Additionally, most data management practices are not robust enough to encompass, quantify, and provide transparency to knowledge. Without this context, traditional data mappings break down. Traditional data mapping will work for simple systems with limited human user integration. However, in the real world of ever evolving, complex, human user integrated systems that broker massive amounts of data in realtime, more holistic data management practices are required.

From my experience, ensuring a smooth data migration involves more than just testing and analysis. Leveraging automation for data mapping, implementing robust data governance frameworks, and collaborating with cross-functional teams are critical. I also ensure version control for mapping configurations and conduct thorough post-migration validation to maintain data accuracy and integrity. These practices have consistently reduced errors and streamlined the migration process.

Investir tempo no Assessment para ter um mapeamento completo do que é necessário será fundamental, falhar nessa fase pode ser fatal para o projeto. Uma vez identificado problemas de mapeamento, de um passo atrás e volte etapa de assessment para entender onde foi o erro.