Big Data: A New Empiricism and its Epistemic and Socio-Political Consequences


The paper investigates the rise of Big Data in contemporary society. It examines the most prominent epistemological claims made by Big Data proponents, calls attention to the potential socio-political consequences of blind data trust, and proposes a possible way forward. The paper’s main focus is on the interplay between an emerging new empiricism and an increasingly opaque algorithmic environment that challenges democratic demands for transparency and accountability. It concludes that a responsible culture of quantification requires epistemic vigilance as well as a greater awareness of the potential dangers and pitfalls of an ever more data-driven society.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Adams, Susan. 2015. The World’s Most Reputable Companies in 2015. Forbes, April 21. Accessed 30 May 2016.
  2. Al-Rodhan, Nayef. 2014. The Social Contract 2.0: Big Data and the Need to Guarantee Privacy and Civil Liberties. Harvard International Review, September 16. Accessed 30 May 2016.
  3. Anderson, Chris. 2008. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. Wired, June 23. Accessed 30 May 2016.
  4. Andrejevic, Mark. 2013. Infoglut: How Too Much Information Is Changing the Way We Think and Know. New York: Routledge.Google Scholar
  5. AppDynamics. 2014. AppDynamics Announces New Application Intelligence Platform. Accessed 30 May 2016.
  6. Bachner, Jennifer. 2013. Predictive Policing: Preventing Crime with Data and Analytics. IBM Center for the Business of Government. Improving Performance Series. Accessed 30 May 2016.
  7. Barocas, Solon, and Andrew D. Selbst. 2015. Big Data’s Disparate Impact. California Law Review 104.Google Scholar
  8. Bell, Gordon. 2009. Foreword. In The Fourth Paradigm. Data-Intensive Scientific Discovery, ed. Tony Hey, Stewart Tansley, and Kristin Tolle, XI-XV. Redmond: Microsoft Research.Google Scholar
  9. Bellezza, Marco, and Federica De Santis. 2013. Google Not Liable for Autocomplete and Related Search Results, Italian Court Rules. CGCS Media Wire, April 22. Accessed 30 May 2016.
  10. Berman, Jules J. 2013. Principles of Big Data. Preparing, Sharing, and Analyzing Complex Information. Amsterdam: Elsevier, Morgan Kaufmann.Google Scholar
  11. boyd, danah, and Kate Crawford. 2012. Critical Question for Big Data. Provocations for a Cultural, Technological, and Scholarly Phenomenon. Information, Communication & Society 15(5).Google Scholar
  12. Brooks, David. 2013. The Philosophy of Data. The New York Times, February 4. Accessed 30 May 2016.
  13. Burrell, Jenna. 2016. How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms. Big Data & Society. doi:  10.1177/2053951715622512.
  14. Byrne, Robert F. 2012. The Three Laws of Big Data. SupplyBrainChain, April 27. Accessed 30 May 2016.
  15. Calcagno, Cristiano, Dino Distefano, and Peter O’Hearn. 2015. Open-Sourcing Facebook Infer: Identify Bugs Before You Ship. Accessed 30 May 2016.
  16. Castro, Daniel. 2014. Big Data Is a Powerful Weapon in the Fight for Equality. The Hill, October 23. Accessed 30 May 2016.
  17. Centre for Internet and Human Rights (CIHR). 2015. Should Algorithms Decide Your Future? Accessed 30 May 2016.
  18. Cisco. 2015. Cisco Visual Networking Index: Forecast and Methodology, 2014–2019. Accessed 30 May 2016.
  19. Clark, Jack. 2011. Facebook, Google: Welcome to the New Feudalism. ZDNet, September 10. Accessed 30 May 2016.
  20. Clinton, Rachel. 2016. What’s the Difference between Business Intelligence and Predictive Analytics? Smart Vision Europe, January 06. Accessed 30 May 2016.
  21. Cohen, Bernard I. 2005. Triumph of Numbers: How Counting Shaped Modern Life. New York: W. W. Norton & Company.Google Scholar
  22. Crawford, Kate. 2013. The Hidden Biases in Big Data. Harvard Business Review, April 1. Accessed 30 May 2016.
  23. Davenport, Thomas H., and D.j. Patil. 2012. Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, October 2012. Accessed 30 May 2016.
  24. Dede, Chris. 2015. Data-Intensive Research in Education: Current Work and Next Steps. Accessed 30 May 2016.
  25. Duhigg, Charles. 2012. How Companies Learn Your Secrets. The New York Times, February 16. Accessed 30 May 2016.
  26. Eagle, Nathan, and Kate Greene. 2014. Reality Mining: Using Big Data to Engineer a Better World. Cambridge, MA: MIT Press.Google Scholar
  27. Eagle, Nathan, and Alex Pentland. 2006. Reality Mining: Sensing Complex Social Systems. Personal and Ubiquitous Computing 10(4): 255-268.Google Scholar
  28. Ekbia, Hamid, Michael Mattioli, Inna Kouper, G. Arave, Ali Ghazinejad, Timothy Bowman, Venkata Ratandeep Suri, Andrew Tsou, Scott Weingart, and Cassidy R. Sugimoto. 2015. Big Data, Bigger Dilemmas: A Critical Review. Journal of the Association for Information and Technology 66(8).Google Scholar
  29. Ember, Sydney. 2015. Tech Giants Top Best Global Brands List. The New York Times, October 4. Accessed 30 May 2016.
  30. Ericsson. 2015. Ericsson Mobility Report. On the Pulse of the Networked Society. Accessed 30 May 2016.
  31. European Commission (EC). 2015a. Big Data. Accessed 30 May 2016.
  32. European Commission (EC). 2015b. Data For Policy: When The Haystack Is Made of Needles. A Call for Contributions. Accessed 30 May 2016.
  33. European Commission (EC). 2014a. European Commission and Data Industry Launch €2.5 Billion Partnership to Master Big Data. Accessed 30 May 2016.
  34. European Commission (EC). 2014b. Data Technologies for Evidence-Informed Policy Making (Including Big Data). Accessed 30 May 2016.
  35. European Commission (EC). 2014c. Towards a Thriving Data-Driven Economy. Accessed 30 May 2016.
  36. European Commission (EC). 2012a. From Crisis of Trust to Open Governing. Accessed 30 May 2016.
  37. European Commission (EC). 2012b. Proposal for a Regulation of the European Parliament and of the Council on the Protection of Individuals with Regard to the Processing of Personal Data and on the Free Movement of such Data (General Data Protection Regulation). Accessed 30 May 2016.
  38. European Commission – Business Innovation Observatory (EC-BIO). 2013a. Big Data: Analytics and Decision Making. Accessed 30 May 2016.
  39. European Commission – Business Innovation Observatory (EC-BIO). 2013b. Big Data: Artificial Intelligence. Accessed 30 May 2016.
  40. European Data Protection Supervisor (EDPS). 2015. Meeting the Challenges of Big Data. A Call for Transparency, User Control, Data Protection by Design and Accountability. Accessed 30 May 2016.
  41. Executive Office of the President (EOP). 2014. Big Data and Privacy: A Technological Perspective. Accessed 30 May 2016.
  42. Fitzgerald, Michael. 2012. Sensing the Future Before it Occurs. MIT Sloan Management Review, December 20. Accessed 30 May 2016.
  43. Floridi, Luciano. 2013. Distributed Morality in an Information Society. Science and Engineering Ethics 19(3): 727-743.Google Scholar
  44. Future of Privacy Forum (FPF). 2014. Big Data: A Tool for Fighting Discrimination and Empowering Groups. Accessed 30 May 2016.
  45. Gillespie, Tarleton. 2014. The Relevance of Algorithms. In Media Technologies: Essays on Communication, Materiality, and Society, ed. Tarleton Gillespie, Pablo J. Boczkowski, and Kirsten A. Foot, 167-194. Cambridge, MA: MIT Press.Google Scholar
  46. Google. 2016. Search Help: Autocomplete. Accessed 30 May 2016.
  47. Gorner, Jeremy. 2013. Chicago Police Use ‘Heat List’ as Strategy to Prevent Violence. Chicago Tribune, August 21. Accessed 30 May 2016.
  48. Greenwald, Glenn. 2014. No Place to Hide: Edward Snowden, the NSA and the Surveillance State. London: Penguin Books.Google Scholar
  49. Gutierrez, Daniel. 2015. Will Big Data Kill the Art of Marketing? Inside Big Data, January 16. Accessed 30 May 2016.
  50. Hacking, Ian. 1990. The Taming of Chance. Cambridge: Cambridge University Press.Google Scholar
  51. Halevi, Gali, and Henk Moed. 2012. The Evolution of Big Data as a Research and Scientific Topic. Research Trends 30.Google Scholar
  52. Hardt, Moritz. 2014. How Big Data Is Unfair: Understanding Sources of Unfairness in Data Driven Decision Making. Medium, September 26. Accessed 30 May 2016.
  53. Hardy, Quentin. 2013. Why Big Data is not Truth. The New York Times, June 1. Accessed 30 May 2016.
  54. Harris, Shane. 2015. Your Samsung SmartTV Is Spying on You, Basically. The Daily Beast, June 02. Accessed 30 May 2016.
  55. Hartzog, Woodrow, and Evan Selinger. 2013. Big Data in Small Hands. Stanford Law Review Online. Accessed 30 May 2016.
  56. Hey, Tony, Steward Tansley, and Kristin Tolle, ed. 2009. The Fourth Paradigm. Data-Intensive Scientific Discovery. Redmond: Microsoft Research.Google Scholar
  57. Hildebrandt, Mireille. 2013. Slaves to Big Data. Or Are We? IDP. Revista De Internet, Derecho Y Política, October. Accessed 30 May 2016.
  58. Housley, William. 2015. Focus: The Emerging Contours of Data Science. Discovery Society, August 3. Accessed 30 May 2016.
  59. Howard, Alex. 2014. Data-Driven Policy and Commerce Requires Algorithmic Transparency. TechRepublic, January 31. Accessed 30 May 2016.
  60. IBM. 2014. IBM Watson Explorer: Search, Analyze, and Interpret to Enable Cognitive Exploration. Accessed 30 May 2016.
  61. IBM. 2013. The Four V’s of Big Data. Big Data & Analytics Hub. Accessed 30 May 2016.
  62. International Data Corporation (IDC). 2015. New IDC Forecast Sees Worldwide Big Data Technology and Services Market Growing to $48.6 Billion in 2019, Driven by Wide Adoption Across Industries. Accessed 30 May 2016.
  63. Isaac, Mike. 2014. Facebook Says It’s Sorry. We’ve Heard That Before. The New York Times, June 30. Accessed 30 May 2016.
  64. Jenkins, Tiffany. 2013. Tiffany Jenkins: Don’t Count on Big Data for Answers. The Scotsman, February 12. Accessed 30 May 2016.
  65. Jurgenson, Nathan. 2014. View From Nowhere. The New Inquiry, October 9. Accessed 30 May 2016.
  66. Kanter, James, and Mark Scott. 2015. Europe Challenges Google, Seeing Violations of Its Antitrust Law. The New York Times, April 15. Accessed 30 May 2016.
  67. Kelly, Kevin. 2007. What Is the Quantified Self? Quantified Self. Self Knowledge Through Numbers, October 5. Access via the Internet Archive’s Wayback Machine.
  68. Kitchin, Rob. 2014a. Big Data, New Epistemologies and Paradigm Shifts. Big Data & Society. doi:  10.1177/2053951714528481.
  69. Kitchin, Rob. 2014b. The Data Revolution. Big Data, Open Data, Data Infrastructures & Their Limitations. London: Sage.Google Scholar
  70. Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks. Proceedings of the National Academy of Science of the United States of America 111(24).Google Scholar
  71. Kun, Jeremy. 2015. Big Data Algorithms Can Discriminate, and It’s Not Clear What to Do About It. The Conversation, August 13. Accessed 30 May 2016.
  72. Lagoze, Carl. 2014. Big Data, Data Integrity, and the Fracturing of the Control Zone. Big Data & Society. doi:  10.1177/2053951714558281.
  73. Laney, Doug. 2012. Deja VVVu: Others Claiming Gartner’s Construct for Big Data. Gartner Blog Network. Accessed 30 May 2016.
  74. Laney, Doug. 2001. 3D Data Management: Controlling Data Volume, Velocity, and Variety. META Group. Application Delivery Strategies. Accessed 30 May 2016.
  75. Lazer, David; Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. The Parable of Google Flu: Traps in Big Data Analysis. Science 343, March 14.Google Scholar
  76. Leonelli, Sabina. 2014. What Difference Does Quantity Make? On the Epistemology of Big Data in Biology. Big Data & Society. doi:  10.1177/2053951714534395.
  77. Lohr, Steve. 2015. Data-ism. The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else. New York: Harper Collins.Google Scholar
  78. Lohr, Steve. 2013. The Origins of ‘Big Data’: An Etymological Detective Story. The New York Times, February 1. Accessed 30 May 2016.
  79. Lohr, Steve. 2012. The Age of Big Data. The New York Times, February 11. Accessed 30 May 2016.
  80. Manovich, Lev. 2013. The Algorithms of Our Lives. The Chronicle Review, December 16. Accessed 30 May 2016.
  81. Marr, Bernard. 2014. Big Data: The 5 Vs Everyone Must Know. Accessed 30 May 2016.
  82. Matthias, Andreas. 2004. The Responsibility Gap: Ascribing Responsibility for the Actions of Learning Automata. Ethics and Information Technology 6: 175-183.Google Scholar
  83. Mayer-Schönberger, Viktor, and Kenneth Cukier. 2013. Big Data. A Revolution That Will Transform How We Live, Work, And Think. New York: Houghton Mifflin Harcourt.Google Scholar
  84. McFarland, Daniel A., and Richard H. McFarland. 2015. Big Data and the Danger of Being Precisely Inaccurate. Big Data & Society. doi:  10.1177/2053951715602495.
  85. McLannahan, Ben. 2015. Being ‘Wasted’ on Facebook May Damage Your Credit Score. The Financial Times, October 15. Accessed 30 May 2016.
  86. Meyer, Robinson. 2014. Everything We Know About Facebook’s Secret Mood Manipulation Experiment. The Atlantic, June 28. Accessed 30 May 2016.
  87. Moz. 2016. Google Algorithm Change History. Accessed 30 May 2016.
  88. Müller, Martin U., Marcel Rosenbach, and Thomas Schulz. 2013. Living by the Numbers: Big Data Knows What Your Future Holds. Spiegel Online International, May 17. Accessed 30 May 2016.
  89. Musiani, Francesca. 2013. Governance by Algorithms. Internet Policy Review 2(3). Accessed 30 May 2016.
  90. National Science Foundation (NSF). 2012. NSF Leads Federal Efforts in Big Data. Accessed 30 May 2016.
  91. New, Joshua. 2015. It’s Humans, Not Algorithms, That Have a Bias Problem. Center for Data Innovation, November 16. Accessed 30 May 2016.
  92. Newman, Nathan. 2015. Data Justice: Taking on Big Data as an Economic Justice Issue. Accessed 30 May 2016.
  93. Niggemeier, Stefan. 2012. Autocompleting Bettina Wulff: Can a Google Function Be Libelous? Spiegel Online, September 20. Accessed 30 May 2016.
  94. Nissenbaum, Helen. 1996. Accountability in a Computerized Society. Science and Engineering Ethics 2(1): 25-42.Google Scholar
  95. Obama, Barack. 2015. President Barack Obama’s Big Data Keynote. Accessed 30 May 2016.
  96. O’Neil, Cathy. 2016. The Ethical Data Scientist. Slate, February 4. Accessed 30 May 2016.
  97. Oracle. 2012. Integrate for Insight: Combining Big Data Tools with Traditional Data Management. Accessed 30 May 2016.
  98. Organisation for Economic Co-Operation and Development (OECD). 2015. Data-Driven Innovation for Growth and Well-Being. Accessed 30 May 2016.
  99. Pagallo, Ugo. 2015. Good Onlife Governance: On Law, Spontaneuous Orders, and Design. In The Onlife Manifesto: Being Human in a Hyperconnected Era, ed. Luciano Floridi, 161-177. Cham: Springer.Google Scholar
  100. Pasquale, Frank. 2015. The Black Box Society: The Secret Algorithms That Control Money and Information. Cambridge, MA: Harvard University Press.Google Scholar
  101. Patel, Nilay. 2014. Screwing With Your Emotions is Facebook’s Entire Business. Vox, July 3. Accessed 30 May 2016.
  102. Pentland, Alex. 2014. Social Physics: How Good Ideas Spread – The Lessons from a New Science. New York: Penguin Press.Google Scholar
  103. Pew Research Center. 2014. Public Perceptions of Privacy and Security in the Post-Snowden Era. Accessed 30 May 2016.
  104. Phillips Mandaville, Alicia. 2014. The Revolution Is Not Everywhere Yet – and That’s a Challenge for Global Development. Millenium Challenge Corporation, January 15. Accessed 30 May 2016.
  105. Pigliucci, Massimo. 2009. The End of Theory in Science? EMBO Reports 10(6). Accessed 30 May 2016.
  106. Porter, Theodore M. 2011. Statistics and the Career of Public Reason: Engagement and Detachment in a Quantified World. In Statistics and the Public Sphere: Numbers and People in Modern Britain c. 1800-2000, ed. Tom Crook, and Glen O’Hara, 32-48. New York: Routledge.Google Scholar
  107. Porter, Theodore M. 1995. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life. Princeton, NJ: Princeton University Press.Google Scholar
  108. Quantacast. 2013. Know Ahead. Act Before. Accessed 30 May 2016.
  109. Richtel, Matt. 2013. How Big Data Is Playing Recruiter for Specialized Workers. The New York Times, April 27. Accessed 30 May 2016.
  110. Rieder, Gernot, and Judith Simon. 2016. Datatrust: Or, The Political Quest for Numerical Evidence and the Epistemologies of Big Data. Big Data & Society, June.Google Scholar
  111. Rosenblat, Alex, Tamara Kneese, danah boyd. 2014. Algorithmic Accountability. Accessed 30 May 2016.
  112. Rubinstein, Ira S. 2013. Big Data: The End of Privacy or a New Beginning? International Data Privacy Law. doi:  10.1093/idpl/ips036.
  113. Rudder, Christian. 2014. Dataclysm: Who We Are (When We Think No One’s Looking). New York: Crown Publishers.Google Scholar
  114. Samuel, Arthur L. 1959. Some Studies in Machine Learning Using the Game of Checkers. IBM Journal and Development 3(3): 210-229.Google Scholar
  115. Schaeffer, Donna M., and Patrick Olson. 2014. Big Data Options for Small and Medium Enterprises. Review of Business Information Systems 18(1).Google Scholar
  116. Schneier, Bruce. 2015. Data and Goliath. The Hidden Battles to Collect Your Data and Control the World. New York: W. W. Norton & Company, Inc.Google Scholar
  117. Schroeder, Ralph. 2014. Big Data and the Brave New World of Social Media Research. Big Data & Society. doi:  10.1177/2053951714563194.
  118. Science Europe. 2014. How to Transform Big Data into Better Health: Envisioning a Health Big Data Exosystem for Advancing Biomedical Research and Improving Health Outcomes in Europe. Accessed 30 May 2016.
  119. Silver, Nate. 2012. The Signal and the Noise. Why So Many Predictions Fail and Some Don’t. New York: The Penguin Press.Google Scholar
  120. Simon, Judith. 2015. Distributed Epistemic Responsibility in a Hyperconnected Era. In The Onlife Manifesto: Being Human in a Hyperconnected Era, ed. Luciano Floridi, 145-159. Cham: Springer.Google Scholar
  121. Sperber, Dan, Fabrice Clément, Christophe Heintz, Olivier Mascaro, Hugo Mercier, Gloria Origgi, and Deirdre Wilson. 2010. Epistemic Vigilance. Mind & Language 25(4): 359-393.Google Scholar
  122. Steadman, Ian. 2013. Big Data and the Death of the Terrorist. Wired, January 25. Accessed 30 May 2016.
  123. Strawn, George O. 2012. Scientific Research: How Many Paradigms? Educause Review. Accessed 30 May 2016.
  124. Striphas, Ted. 2015. Algorithmic Culture. European Journal of Cultural Studies 18(4-5): 395-412.Google Scholar
  125. Strong, Colin. 2015. Humanizing Big Data: Marketing at the Meeting of Data, Social Science and Consumer Insight. London: Kogan Page.Google Scholar
  126. Szal, Andy. 2015. MIT System Successfully Processes Big Data Without Human Involvement., October 19. Accessed 30 May 2016.
  127. United Nations Economic Commission for Europe (UNECE). 2014. MSIS Wiki: Big Data. Accessed 30 May 2016.
  128. UN Women. 2013. UN Women Ad Series Reveals Widespread Sexism. Accessed 30 May 2016.
  129. Valinsky, Jordan. 2013. Japanese Court Orders Google to Pay Fine for Embarrassing Autocomplete Results. Observer, April 16. Accessed 30 May 2016.
  130. Van Rijmenam. 2013. Why the 3V’s Are Not Sufficient to Describe Big Data. Datafloq. Connectiong Data and People. Accessed 30 May 2016.
  131. Venturini, Tommaso, Nicolas Baya Laffite, Jean-Philippe Cointet, Ian Gray, Vinciane Zabban, and Kari de Pryck. 2014. Three Maps and Three Misunderstandings: A Digital Mapping of Climate Diplomacy. Big Data & Society. doi:  10.1177/2053951714543804.
  132. Wilbanks, John. 2009. I Have Seen the Paradigm Shift and It Is US. In The Fourth Paradigm. Data-Intensive Scientific Discovery, ed. Tony Hey, Stewart Tansley, and Kristin Tolle, 209-214. Redmond: Microsoft Research.Google Scholar
  133. Williamson, Ben. 2015. Smarter Learning Software: Education and the Big Data Imaginary. Accessed 30 May 2016.
  134. Williamson, Ben. 2014. Knowing Public Services: Cross-Sector Intermediaries and Algorithmic Governance in Public Sector Reform. Public Policy and Administration 29(4): 292-312.Google Scholar
  135. Zarsky, Tal. 2016. The Trouble with Algorithmic Decisions: An Analytical Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making. Science, Technology, & Human Values 41(1): 118-132.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH 2017

Authors and Affiliations

  1. 1.IT University of CopenhagenCopenhagenDänemark

Personalised recommendations