Most large retailers similarly analyse enormous quantities of data from their databases of sales (which are linked to you by credit card numbers and loyalty cards) in order to make uncanny predictions about your future behaviours. In a now famous case, the American retailer, Target, upset a Minneapolis man by knowing more about his teenage daughter's sex life than he did. Target was able to predict his daughter's pregnancy by monitoring her shopping patterns and comparing that information to an enormous database detailing billions of dollars of sales. This ultimately allows the company to make uncanny predictions about its shoppers.
Ict와 사회과학지식간 학제간 연구동향(23 march2013)
ICT와 사회과학 지식간 학제간 연구동향 및 쟁점- 이해를 넘어 활용으로Virtual Knowledge Studio (VKS)박한우 교수영남대 언론정보학과영남대사이버감성연구소장아시아트리플헬릭스학회장대구경북소셜미디어포럼고문영국옥스퍼드인터넷연구소(전)WCU웹보메트릭스사업단네델란드왕립아카데미(전)TEDxPalgong (전)email@example.com
http://novaspivack.typepad.com/nova_spivacks_weblog/2007/02/steps_towards_a.html 에서 재인용
All models are wrong but some are usefulEmergence of data author on dataverse
Andersons claims Data is everything we need. We dont have to settle for models. Agnostic statistics. Out with every theory of human behavior. This approach to science — hypothesize,model, test — is becoming obsolete. Petabytes allow us to say: "Correlation isenough." We can stop looking for models. What can science learn from Google? E-Science.
Computational (Social) SciencePark, H. W., & Leydesdorff, L. (2013 Work-In-Progress). Decomposing a Data-Driven Science Using a Scientometric Method. Focus on the methodological perspective basedon the use of new digital tools to manage thedata deluge. Development of e-science tools to automateresearch process. Experimentation with new types of datavisualization.
Why Data Science?Savage and Burrows (2007, p.886) lament, ―Fifty years ago,academic social scientists mightbe seen as occupying the apexof the – generally limited – socialscience research ‗apparatus‘.Now they occupy an increasinglymarginal position in the hugeresearch infrastructure‖.Bonacich, P. (2004).The Invasion of the Physicists. Social Networks 26(3): 285-288
Global Communication 2team(빅)데이터과학의도전이론의 종말-증거기반 경영Jeffrey Pfeffer, Robert I. Sutton (2006)How companies can bolster performance andtrump the competition through evidence-basedmanagement, an approach to decision-making andaction that is driven by hard facts rather than half-· 빅데이터의 등장으로 전통적인과학 연구방법론 퇴색· 인식의 한계치를 넘어선 데이터(팩트가아닌패턴)
The Signal and the Noise:Why Most Predictions Fail but Some Dont. NateSilverI do not go as far as a Popper in asserting that suchtheories are therefore unscientific or that they lack anyvalue. However, the fact that the few theories we cantest have produced quite poor results suggests thatmany of the ideas we haven‘t tested are very wrong aswell. We are undoubtedly living with many delusions thatwe do not even realize.page 15
OECD (2012). OECD Technology ForesightForum 2012 - Harnessing data as a new sourceof growth: Big data analytics and policies. OECDHeadquarters, Paris, France 22 October 2012
Big data and the end of theory? Does big data have the answers? Maybe some, but not all,says - Mark Graham In 2008, Chris Anderson, then editor of Wired, wrote aprovocative piece titled The End of Theory. Anderson wasreferring to the ways that computers, algorithms, and big datacan potentially generate more insightful, useful, accurate, ortrue results than specialists or domain experts whotraditionally craft carefully targeted hypotheses and researchstrategies. We may one day get to the point where sufficient quantitiesof big data can be harvested to answer all of the socialquestions that most concern us. I doubt it though. There willalways be digital divides; always be uneven data shadows;and always be biases in how information and technology areused and produced. And so we shouldnt forget the important role of specialists tocontextualize and offer insights into what our data do, andmaybe more importantly, dont tell us.http://www.guardian.co.uk/news/datablog/2012/mar/09/big-data-theory
Yet, there still are serious problems to overcome. Atrenchant critique concerning the big data field as it isnowadays came in the form of six statements intendingto temper unbridled enthusiasm.  These sixprovocative statements are: Big data change the definition of knowledge; Claims to accuracy and objectivity are misleading; More data are not always better data; Taken out of context, big data loses its meaning; Just because it is accessible, it does not make it ethical;and (Limited) access to big data creates a new digital divide.Rousseau (2012)
Global Communication 2team빅데이터에 대한 부정적인 시각 등장-빅데이터의 가치-저장, 분석 및 해석기술 한계 존재-현재의 붐은 호들갑스러운 측면 존재빅데이터 갭: Promise VS Capabilities빅데이터의도전
Applied and Basic researchQuest forfundamentalunderstanding?HighPure basic research (Bohr)Use-inspired basicresearch(Pasteur)Low–Pure applied research (Edison)Low HighConsiderations of use?Pasteurs quadrantPasteurs quadrant is a label given to a class of scientific research methods that both seekfundamental understanding of scientific problems, and, at the same time, seek to beeventually beneficial to society. Louis Pasteurs research is thought to exemplify this type ofmethod, which bridges the gap between "basic" and "applied" research. The term wasintroduced by Donald Stokes in his book, Pasteurs Quadranthttp://en.wikipedia.org/wiki/Pasteurs_quadrant
Re-setting science and innovation for the next 20years: New Zealand, new futures,new ways of science engaging with society? yearsNew Zealand, new futures,new ways of science engaging withsociety?New Zealand, new futures,new ways of science engaging withsociety?• New ways of doing scienceThe responsibilities of government, business and citizens may also move intothe realm of post-normal science in which people are credited withmultiple capacities and expertise that can support the co-production ofknowledge about sustainability alongsidee-Science: As society‘s ‗grand challenges‘ such as climatechange and food security demand more complex analysis ofever-larger datasets, and global cooperation between scientistsand other stakeholders, many countries have begun to investin the infrastructure to support the sharing of knowledge (data,models) and high-performance computing resources. T professional expertshttp://scientists.org.nz/files/journal/2011-68/NZSR_68_1.pdf#page=26