One concern that comes up with almost every client you will work with is data quality.
And rightly so. AI systems rely on good underlying data to produce useful context and reliable outputs.
The reality is that every business has poor data quality somewhere. Usually in dozens or hundreds of places.
This should and will not stop a project moving forward.
AI is far more tolerant of unstructured data than traditional systems. It doesn’t need everything to be perfectly cleaned or standardised, but it does need data that’s broadly accurate, relevant, and accessible.
This is something we deal with on nearly every engagement, and it’s very solvable.
Our general approach is:
- Accept that data quality will be imperfect at the start
- Centralise data first into a single source of truth often using a data warehouse or data lake like Google BigQuery.
From there, AI adoption becomes iterative. You work with Brim, the client, the data, and the AI to see where outputs are strong and where they’re weak. If gaps show up, you add more sources and improve specific areas of data.
This process is often done while already getting value from Brim. So it shouldn’t stop AI adoption.
Happy to share examples of how we’ve tackled this with clients if helpful.