Data Quality in the era of A.I. – Towards Data Science – Medium

As the director of datamine decision support systems, I've delivered more than 80 data-intensive projects -- including data warehousing, data integration, business intelligence, content performance and predictive models -- across several industries and high-profile corporations. In most cases, data quality proved to be a critical success factor. The obvious challenge in every case was to effectively query heterogeneous data sources, then extract and transform data towards one or more data models. The non-obvious challenge was the early identification of data issues, which in most cases were unknown to the data owners as well. There are many aspects to data quality, including consistency, integrity, accuracy, and completeness.