Blend, cleanse and prepare data for analytics, reporting or data modernization efforts
Jim Harris shares examples of how and why AI applications are dependent on high-quality data.
The idea of running software in a self-contained package took off with the launch of Docker in 2013 and has become a hot topic in the application development and DevOps community. In a recent survey by Red Hat, 57 percent of companies questioned said they use containers for some workloads
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
