[benhamner]

Completely agree. Data quality issues was a big part of our motivation with Kaggle Datasets (an open data platform where the quality of the dataset improves as more people use it) and Kaggle Kernels (a reproducible data science workbench that combines versioned data, code, and compute environments to create reproducible results).

Two examples of this: Kaggle Datasets supports wiki-like editing of metadata (file and column descriptions) and makes it easy to see, fork, and build on all the analytics created on the data so far.

We're just getting started with each of these products: we want Kaggle Datasets to support a fully collaborative model around working with all your data in the future, and Kaggle Kernels to support every analytics and machine learning usecase.