Trasforming business (big data and beyond, 2013)

“Based on the findings of an extensive research project that surveyed more than 5,500 enterprise employees and functional decision makers across the United States and China, Transforming Business: Big Data, Mobility and Globalization explores the influence of technology in the workplace and the implications to company culture, functional responsibilities and competitive advantage. This in-depth analysis illuminates emerging technological trends, the changing workforce, and the shifting face of business and industry while offering prescriptive guidance to leaders”

http://media.wiley.com/product_data/excerpt/8X/11185196/111851968X-49.pdf

Big Data (a book preview, 2013)

“The data we deal with is diverse. Users create content like blog posts, tweets, social network interactions, and photos. Servers continuously log messages about what they’re doing. Scientists create detailed measurements of the world around us. The internet, the ultimate source of data, is almost incomprehensibly large. This astonishing growth in data has profoundly affected businesses. Traditional database systems, such as relational databases, have been pushed to the limit. In an increasing number of cases these systems are breaking under the pressures of “Big Data.” Traditional systems, and the data management techniques associated with them, have failed to scale to Big Data”

http://www.manning.com/marz/BD_meap_ch01.pdf

 

The rise of marketing/market research (2012)

“As the complexity of markets is too great for any individual to understand, intermediaries and interpreters who concentrate on a limited number of the markets’ core features are needed. Their work, which bridges the gap between the subsystems of markets and corporations, is generally called marketing. Marketing tries to process information on markets and translate it into a language that corporations can use to steer their activities. It is about reconciling the imperatives of production  with the needs and desires of customers. The agency of consumers generates insecurity among marketers, who have to develop methods to overcome information asymmetries. This task calls for professional, even scientific expertise, as well as special instruments and institutions”

http://www.amazon.com/Marketing-Market-Research-Worlds-Consumption/dp/0230341…

 

 

Into the river: big data and market insights

“The move from a deductive approach to an inductive approach runs parallel to the move from hunch-based management to evidence-based management. In the past, managers who came up with the best story and convinced people of their hypothesis, regardless of what the data said, created action in the organization. More and more, such a situation is being replaced by evidence-based management where decisions are driven by true market information and mere hunches lose their importance. Given this trend, the well positioned Market Insights function becomes the brain of the organization”.

http://www.amazon.com/INTO-THE-RIVER-Situated-Cognition/dp/1614347212/ref=sr_…

Honest Signals (social science/big data)

Honest Signals (Petland, 2010, The MIT Press)

“For the fi rst time, we can precisely map the behavior of large numbers of people as they go about their normal lives. By using cell phones and electronic badges with integrated sensors, my students and I have observed hundreds of participants for periods of up to a year. In the process we amassed hundreds of thousands of hours of detailed, quantitative data about natural, day-to-day human behavior—far more data of these kind than have ever been available before. A new measurement tool such as this often brings with it a new understanding of what you are measuring. What we have found is that many types of human behavior can be reliably predicted from biologically based honest signaling behaviors. These ancient primate signaling mechanisms, such as the amount of synchrony, mimicry, activity, and emphasis, form an unconscious channel of communication between people—a channel almost unexplored except in other apes”

http://www.amazon.com/Honest-Signals-Shape-World-Bradford/dp/0262515121/ref=s…

 

Marketing as surveillance

Marketing as surveillance (from Inside Marketing, 2012, ed. Zwick and Cayla)

“The consumer becomes knownin the compartmentalization of their data, assembledin the production of a consumer brand,and all of this is made possible by forms of commercial sociology that operate as complex andeffective surveillance systems. This notion of commercial sociology describesthe kinds of analysis done by contemporary marketing companies. Customers are reduced to datasets, as information about purchasing preferences and proclivities is detailed to provide marketers with useful profiles. Such commercial sociology thus simulates consumers for analysis. But it also interprets these behaviors and normalizes them as a part of the surveillance process and, in addition, incorporates further information on the activities and preferences of consumers as feedback”

http://ebooks.narotama.ac.id/files/Inside%20Marketing;%20Practices,%20Ideolog… 

Origin(s) and Development of (the concept of) "Big Data"

A Personal Perspective on the Origin(s) and Development of Big Data”: The Phenomenon, the Term, and the Discipline (Francis X. Diebold, 2012)

Abstract: I investigate Big Data, the phenomenon, the term, and the discipline, with emphasis on origins of the term, in industry and academics, in computer science and statistics/econometrics. Big Data the phenomenon continues unabated, Big Data the term is now  rmly entrenched, and Big Data the discipline is emerging.

http://www.ssc.upenn.edu/~fdiebold/papers/paper112/Diebold_Big_Data.pdf

Shift to comput social science (big data)

Understanding the paradigm shift to computational social science in presence of big data (Chang et alii, 2012)

 

“The era of big data has created exciting new opportunities for research to achieve high relevance and impact amid changes and transformations in how we study social science phenomena. With the emergence of very large-scale data collection techniques and the related new technological support, there seem to be fundamental changes that are occurring with the research questions we can ask, and the research methods we can apply. The contexts include social networks and blogs, political discourse on the Internet, corporate announcements and digital journalism, mobile telephony and digital home entertainment, online gaming and social shopping, and social advertising and social commerce – and much more. The increasingly advantageous costs of data collection, and the new capabilities that researchers have to conduct research that leverages the spectrum of micro-, meso and macro-level data suggest the possibility of a scientific paradigm shift toward computational social science with big data”

 

http://www2.sis.smu.edu.sg/icec2012/downloads/BCSI_Theme_Paper.pdf

 

A planetary nervous system for social mining

A planetary nervous system for social mining and collective awareness (Giannotti et alii, 2012)

 

“We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis”

http://link.springer.com/content/pdf/10.1140/epjst/e2012-01688-9