Best Practices for Data Quality and Workflow Design in Scalable Private Equity Systems
Hi everyone Introduce Yourself Adam here. I spend most of my time building data+workflow systems in private equity environments. Integrating across fragmented sources, normalizing messy data, designing workflows on top of how teams actually operate. Lately moving deeper into data prep for diligence (massive data rooms, people/company sourcing flows) as the systems mature. On the sourcing side, I find that the work is mostly centered around 3 things:
making inconsistent data actually usable once you start stitching sources together
dealing with identity resolution when inputs are ambiguous
and structuring workflows that don’t fall apart as you scale the system
Been getting deeper into Clay to understand how people are handling enrichment, identity resolution, waterfalls and workflow reliability in GTM systems. Curious how others here are thinking about data quality and workflow design at scale. Happy to share notes/learn from what people are building.