How to improve data accuracy
Phil Simon says that the downsides of even a few discrepancies can be enormous.
Phil Simon says that the downsides of even a few discrepancies can be enormous.
Jim Harris shares examples of how and why AI applications are dependent on high-quality data.
Data scientists spend a lot of their time using data. Data quality is essential for applying machine learning models to solve business questions and training AI models. However, analytics and data science do not just make demands on data quality. They can also contribute a lot to improving the quality