Ninety-nine point four percent of products showing zero sales is the kind of stat that makes you realize how much work goes into cleaning up data before you can do anything useful with it. Most people think the hard part is the AI models. It's not. It's getting to the point where the models have something reliable to learn from.
Exactly - this is such an underrated part of the whole AI conversation.
Most people focus on the models, but in reality data quality is the real bottleneck. If 99% of the dataset is noise, duplicates, or zero-signal records, even the best models won’t produce meaningful outcomes.
A big part of the real work is cleaning, organizing, and checking the data. This helps the model learn from useful information. Once that foundation is solid, the modeling part becomes much more predictable.
Curious - in your experience, what’s been the most painful part of the data cleanup process?
Also, if you want to see how we use AI to automate QA testing, you may like QA flow too.
Ninety-nine point four percent of products showing zero sales is the kind of stat that makes you realize how much work goes into cleaning up data before you can do anything useful with it. Most people think the hard part is the AI models. It's not. It's getting to the point where the models have something reliable to learn from.
Exactly - this is such an underrated part of the whole AI conversation.
Most people focus on the models, but in reality data quality is the real bottleneck. If 99% of the dataset is noise, duplicates, or zero-signal records, even the best models won’t produce meaningful outcomes.
A big part of the real work is cleaning, organizing, and checking the data. This helps the model learn from useful information. Once that foundation is solid, the modeling part becomes much more predictable.
Curious - in your experience, what’s been the most painful part of the data cleanup process?
Also, if you want to see how we use AI to automate QA testing, you may like QA flow too.