“It seems like everyone been talking about “big data” recently, speculating on the future of AI and intelligent systems. Big data has been characterized in many ways, from Doug Laney’s original 2001 “3Vs” model to the various recent extended “4Vs” descriptions. Laney’s three Vs are volume, velocity, A Big-Data Perspective on AI: Newton, Merton, and Analytics Intelligence for Complex Systems and variety; the fourth V could be variability, virtual, or value, depending on whom you ask. To most, those Vs indicate “bigness”—big size, fast movement, many types, and significant impact. To me, the “bigness” of big data is derived from its “smallness,” or more precisely, for its inclusion and use of data stemming from all degrees of volume, velocity, variety, value, variability, and so on, whether virtual or real. In particular, big data implies that the long-tail effects on personal living and business operations will be a normal mode in the future. But what does big data really mean in the era of cyberspace?”
Useful to sofisticate current (trivial) discourses about data visualization and knowledge
“As data-intensive and computational science become increasingly established as the dominant mode of conducting scientific research, visualisations of data and of the outcomes of science become increasingly prominent in mediating knowledge in the scientific arena. This position piece advocates that more attention should be paid to the epistemological role of visualisations beyond their being a cognitive aid to understanding, but as playing a crucial role in the formation of evidence for scientific claims. The new generation of computational and informational visualisations and imaging techniques challenges the philosophy of science to re-think its position on three key distinctions: the qualitative/quantitative distinction, the subjective/objective distinction, and the causal/non-causal distinction”
“The tools through which people make inquiries about society are central to the way they come to understand it and the possibility of harnessing ´big data´ are currently giving rise to a set of semiautomated tools that promise new ways to organize social inquiry. They work by programming algorithms to harness massive amounts of behavioral data on the web and synthesize it into manageable visualizations of the social. On the basis of interviews and document analyses this paper provides an analysis of the ways in which such visualizations are constructed and made sense of by project leaders across the areas of public governance, advertizing, military intelligence, strategic foresight and the social sciences. The theoretical framework of the paper is grounded in Social Construction of Technology, Actor-Network Theory and Software Studies in order to focus analytical attention on technological and discursive elements that are playing an influential role in the ´production-chain´ behind these new tools. Looking for similar elements across different industries allows for identifying ´core elements´ that are inevitably salient when constructing visualizations. Similarities in the way these elements are approached will be defined as signs of ´stabilization´ in the trend of visualizing big data whereas elements and approaches that are unique to subsets of cases and specific professional cultures will be denoted as ´flexible addons ´ to these core elements. By following the process of organizing the meaning and use of these emerging tools the paper intend open up these assemblages and specifically ponder the role of algorithms in organizing visibilit”
“While we have been preoccupied with the latest i-gadget from Apple and with Google’s ongoing expansion, we may have missed something: the fundamental transformation of whole firms and industries into giant information-processing machines. Today, more than eighty percent of workers collect and analyze information (often in digital form) in the course of doing their jobs. This book offers a guide to the role of information in modern business, mapping the use of information within work processes and tracing flows of information across supply-chain management, product development, customer relations, and sales. The emphasis is on information itself, not on information technology. Information, overshadowed for a while by the glamour and novelty of IT, is the fundamental component of the modern corporation. In Information and the Modern Corporation, longtime IBM manager and consultant James Cortada clarifies the differences among data, facts, information, and knowledge and describes how the art of analytics has all but eliminated decision making based on gut feeling, replacing it with fact-based decisions. He describes the working style of “road warriors,” whose offices are anywhere their laptops and cell phones are and whose deep knowledge of a given topic becomes their medium of exchange”
“Again, these pessimistic conclusions concern liquid markets with a large number of very simple and visible transactions. In other information markets this effect is far less relevant. In fact, even financial information markets have seen a boom in the last decades thanks to all the market inefficiencies and the “noise” produced by random traders with random goals and objectives. In sum, consistent with the reality in which information markets flourish, the rest of our discussion assumes the proper functioning of information markets, that is, where ownership can be protected by copyright laws and private information does not get revealed in people’s actions. While information sellers need to be aware of their market’s potential vulnerability, these market conditions do apply to the vast majority of information products”.
“To understand the rise of marketing and market research, we need to understand what these two overlapping fields are about. We must understand what problems they tried to grapple with and what their goals and methods were. Put into simple language, marketing deals with three broad complexes: information, institutions, and markets. First, it is important to stress that there is no such thing as objective information in marketing. Such information is inherently ambiguous. Its production and interpretation are deeply value-laden and influenced by the gatherer’s intentions. It directly relates to the reduction of complexity in Luhmann’s sense, as the number of categories for consumers and products has to be small, and these categories tend to ignore finer differentiations. Market research does not just rely on developing consumer profiles, although these are certainly necessary for gaining a rough understanding of the marketplace and making informed investment decisions. Market research also actively fashions and transforms consumers and markets”
“Fueled by big data collected by a wide range of high-throughput tools and technologies, a new wave of data-driven, interdisciplinary science have rapidly proliferated during the past decade, impacting a wide array of disciplines, from physics and computer science to cell biology and economics. In particular, the ICT’s are inundating us with huge amounts of information about human activities, oering access to observing and measuring human behavior at an unprecedented level of details. These large-scale datasets, oering objective description on human activity pat- terns, have started to reshape, and are expected to fundamentally alter, our discussions on quantifying and understanding human behavior. An impressive shift has been witnessed in statistical physics and complex system theory since the beginning of the new millennium, when the possibility of analyzing large datasets of human activities and social interactions has boosted a renewed interest in the study of human mobility on one side, and of social networks on the other side”