Amazon's Mechanical Turk is a platform for soliciting work on online tasks that has been used by market researchers, translators, and data scientists to complete surveys, perform work that cannot be easily automated, and create human-labeled data for supervised learning systems. Its namesake, the original Mechanical Turk, was an 18th-century chess-playing automaton gifted to the Austrian Empress Maria Theresa. An elaborate hoax, it concealed a human player amidst the clockwork machinery that appeared to direct each move on the board. Amazon's Mechanical Turk (mTurk), which they call "artificial artificial intelligence," isn't all that different. From the outside, mTurk appears to perform tasks automatically that only humans can, like identifying objects in photographs, discerning the sentiment towards a brand in a tweet, or generating natural language in response to a prompt.

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.