Data Intelligence, Business Analytics
Below (in italic) is Revolution Analytics point of view. Mine is that is not just science, but a mix of craftsmanship, intuition, art, business acumen, magic and science. I would rather call it data witchcraft, and I call myself a data witch.
Ever since the term "Data Scientist" was coined by DJ Patil and Jeff Hammerbacker in 2009, there's been a vigorous debate on what the team actually means. More than 80% of statisticians consider themselves data scientists, but Data Science is more than just Statistics. (My own take is that Data Science is a valuable rebranding of computer science and applied statistics skills.)
To help bring clarity to the issue, Data Scientist and R user Harlan Harris has published a great presentation he gave at the Data Science DC meetup group, "What is Data Science anyway?". The presentation recaps the key data science discussions over the last few years, from Hal Varian ("the sexy job in the next 10 years will be Statistics"), Mike Driscoll ("sexy skills of data geeks"), Nathan Yau ("data scientists: people who can do it all"), Mike Loukides ("Data science enables the creation of data products"), Hilary Mason ("Data science is clearly a blend of the hackers"), Drew Conway ("The Data Science Venn Diagram") and many others.
In fact, the entire presentation servers as a literature review for the birth of "Data Science" as a concept, and would make excellent fodder for the "Data Science" page on Wikipedia which, sadly, is still a blank page.
(full article at http://blog.revolutionanalytics.com/2011/09/data-science-a-literatu...)