Predictive analytics tells you what will happen; prescriptive analytics tells you what to do about it.
Decision Support and Analytics has traditionally addressed Descriptive Analytics and Predictive Analytics. Jeff Bertolucci highlights this domain founded on the methods of Operations Research and called by IBM, “the final phase” in business analytics.
In “HOW SECRECY CAN DISTORT DATA” (http://www.newyorker.com/online/blogs/elements/2013/06/the-problem-with-secret-information.html), David Berreby cites two studies that posit that an individual will rate classified information, on average, with 15% more credibility than non-classified information. I find the article and the studies cited to be naive in their approach to supporting the notion that adding a classification label lends some inherent credibility to information when judged by legitimate professionals.
The methodologies of these studies don’t exactly lend themselves to authoritative results. Perhaps if individuals from the Intelligence services were recruited for comparison it may be slightly more informative but even within those groups the ability to discern credibility (and the responsibility to make that judgement) run a very broad spectrum. Further, gauging between classified and unclassified sources is probably not meaningful as decisions are made from multiple lines of evidence in _any_ field meaning that a bias, should it actually exist, would likely not be a factor in real-world decisions. I would be very interested to see a study performed with a more valid population and a measure inserted to test if these biases actually influenced any decision in a meaningful way.
Anyone know of any studies analyzing these factors?
From Whence are the Dangers of Intelligence Collection on American Citizens?
The ubiquity of cell phones has made the capturing of actual behavior (versus behavior stated in surveys) a multi-billion dollar enterprise. A boon to legitimate enterprises and researchers, there are nagging questions regarding the ethics of collecting personal information solely on the basis of a (unapproachably worded) legal disclaimer. The powerful sensor package carried in our pockets may rival those of a military drone aircraft or a manufacturing robot. Further, collection and resale of sensor data in mobile devices will continue to expand as more sensors are added. A highly insightful article in the Wall Street Journal posted recently in their blog section displays a fantastic analysis, hinting at one small aspect of surreptitious consumer information gathering. There has been a significant volume of emotional arguments expressed by those concerned with the US Government’s Prism project–the law and ethics of which are well-controlled and well-understood, the individual risk of disclosure negligible, and the threat imposed by disclosure minimal. The sheer volume of information, the cost of analysis, the lack of actionable intelligence, and high degree of noise are a huge barrier to actual violation of individual liberties and in practice likely preclude the US Government spying activity on citizens. In contrast, the WSJ article above highlights a few of the major commercial collectors of consumer intelligence, the contents of which are very typically acted upon. Perhaps articles like these may do something to educate people to one reason some mobile services are free. Then consumers may choose by whom and to what degree they wish to be surveilled.