As personal technology seems to be shrinking, some technology is getting bigger. This show examines the huge devices and inventions that aid humans in our modern world.
Runtime: 50 minutes
Really Big Things - Big data - Netflix
Big data is data sets that are so big and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are a number of concepts associated with big data: originally there were 3 concepts volume, variety, velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value. Lately, the term “big data” tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.” Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on.” Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research. Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated. Based on an IDC report prediction, the global data volume will grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization. Relational database management systems and desktop statistics and software packages to visualize data often have difficulty handling big data. The work may require “massively parallel software running on tens, hundreds, or even thousands of servers”. What counts as “big data” varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. “For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.”
Really Big Things - Critiques of the big data paradigm - Netflix
“A crucial problem is that we do not know much about the underlying empirical micro-processes that lead to the emergence of the[se] typical network characteristics of Big Data”. In their critique, Snijders, Matzat, and Reips point out that often very strong assumptions are made about mathematical properties that may not at all reflect what is really going on at the level of micro-processes. Mark Graham has leveled broad critiques at Chris Anderson's assertion that big data will spell the end of theory: focusing in particular on the notion that big data must always be contextualized in their social, economic, and political contexts. Even as companies invest eight- and nine-figure sums to derive insight from information streaming in from suppliers and customers, less than 40% of employees have sufficiently mature processes and skills to do so. To overcome this insight deficit, big data, no matter how comprehensive or well analysed, must be complemented by “big judgment,” according to an article in the Harvard Business Review. Much in the same line, it has been pointed out that the decisions based on the analysis of big data are inevitably “informed by the world as it was in the past, or, at best, as it currently is”. Fed by a large number of data on past experiences, algorithms can predict future development if the future is similar to the past. If the systems dynamics of the future change (if it is not a stationary process), the past can say little about the future. In order to make predictions in changing environments, it would be necessary to have a thorough understanding of the systems dynamic, which requires theory. As a response to this critique Alemany Oliver and Vayre suggested to use “abductive reasoning as a first step in the research process in order to bring context to consumers’ digital traces and make new theories emerge”. Additionally, it has been suggested to combine big data approaches with computer simulations, such as agent-based models and Complex Systems. Agent-based models are increasingly getting better in predicting the outcome of social complexities of even unknown future scenarios through computer simulations that are based on a collection of mutually interdependent algorithms. Finally, use of multivariate methods that probe for the latent structure of the data, such as factor analysis and cluster analysis, have proven useful as analytic approaches that go well beyond the bi-variate approaches (cross-tabs) typically employed with smaller data sets. In health and biology, conventional scientific approaches are based on experimentation. For these approaches, the limiting factor is the relevant data that can confirm or refute the initial hypothesis. A new postulate is accepted now in biosciences: the information provided by the data in huge volumes (omics) without prior hypothesis is complementary and sometimes necessary to conventional approaches based on experimentation. In the massive approaches it is the formulation of a relevant hypothesis to explain the data that is the limiting factor. The search logic is reversed and the limits of induction (“Glory of Science and Philosophy scandal”, C. D. Broad, 1926) are to be considered. Privacy advocates are concerned about the threat to privacy represented by increasing storage and integration of personally identifiable information; expert panels have released various policy recommendations to conform practice to expectations of privacy. The misuse of Big Data in several cases by media, companies and even the government has allowed for abolition of trust in almost every fundamental institution holding up society. Nayef Al-Rodhan argues that a new kind of social contract will be needed to protect individual liberties in a context of Big Data and giant corporations that own vast amounts of information. The use of Big Data should be monitored and better regulated at the national and international levels. Barocas and Nissenbaum argue that one way of protecting individual users is by being informed about the types of information being collected, with whom it is shared, under what constrains and for what purposes.
Really Big Things - References - Netflix