Big Data : pourquoi nos métadonnées sont-elles plus personnelles que nos empreintes digitales A l’occasion du colloque « la politique des données personnelles : Big Data ou contrôle individuel « organisé par l’Institut des systèmes complexes et l’Ecole normale supérieure de Lyon qui se tenait le 21 novembre dernier, Yves-Alexandre de Montjoye (@yvesalexandre) était venu présenter ses travaux, et à travers lui, ceux du MediaLab sur ce sujet (Cf. « D’autres outils et règles pour mieux contrôler les données » ). Yves-Alexandre de Montjoye est doctorant au MIT. Il travaille au laboratoire de dynamique humaine du Media Lab, aux côtés de Sandy Pentland, dont nous avons plusieurs fois fait part des travaux. Nos données de déplacements sont encore plus personnelles que nos empreintes digitales Faire correspondre des empreintes digitales n’est pas si simple, rappelle Yves-Alexandre de Montjoye. Image : illustration de l’unicité de nos parcours repérés via des antennes mobiles. Et Yves-Alexandre de nous inviter à retrouver un de ses collègues du Media Lab. Hubert Guillaud
A Reality Check on Big Data A Reality Check on Big Data By Peter Sweeney (@petersweeney) The fervor around big data continues to grow. The World Economic Forum and The New York Times are jumping on the bandwagon. While we share their enthusiasm for the potential, big data needs a reality check. Here are just a few of the how-do-you-get-there-from-here questions for anyone considering big data projects. Data costs Big data means high costs. Figure: Big data complexity Data-driven applications are inherently complex. Poor performance in the long tail Big data is plagued with small data problems in the long tail of interests. Privacy concerns Big data approaches need large amounts of detailed data about users and their usage patterns. Misleading statistics The vernacular of big data analysis—trends, correlations, inferences, and frames—betray their bias towards generalization over specialization. Talent shortages Perhaps the biggest bottleneck to big data is people. Time to market Primal’s Solution
Big Data Is Less About Size, And More About Freedom Editor’s note: Big Data has been around for a long time between credit card transactions, phone call records and financial markets. Companies like AT&T, Visa, Bank of America, Ebay, Google, Amazon and more have massive databases they mine for competitive advantage. But lately, Big Data is finding its way to the smallest startups. The Web and cloud computing brings Big Data everywhere. To answer that we brought in an expert, Bradford Cross. We are in a Renaissance for computer science, engineering, and learning from data right now. The recent big data trend is about the democratization of large data more than its growth. Now focused early stage startups can get up and running faster than ever. Why Now? We do not have to build all our own hardware and software infrastructure anymore. Pioneers such as Amazon have given us the cloud, where we have the capability to run very large server clusters at a low startup cost. Supersize my data. Big Data & The Cloud: Viral Buzzwords 4.0!
The Coming Era of Big Data for the Little Guy Big data is big news these days. Yet much of the discussion has been about how big data will help big business. At Intuit, we believe the biggest opportunity is giving consumers and small businesses the power of data. It is a new data era that we like to call Big Data for the Little Guy. Today, Intuit released “The New Data Democracy: How Big Data Will Revolutionize the Lives of Small Businesses and Consumer.” The report examines three key data trends that will impact small businesses and individuals over the coming decade: The New Data Democracy: Over the next decade, analysts expect the global volume of digital data to increase more than 40-fold. At Intuit, we are living on the front lines of many of these shifts. I encourage everyone to read The New Data Democracy. Intuit President and CEO Brad Smith has his head in the cloud. Rate: Loading ...
Big Data : définition, enjeux et études de cas l Data-Business.fr La Vénérable grille de lecture établie en 2001 par Doug Laney du cabinet Gartner – les fameux 3V du Big Data – fait encore référence. Les 3V, ce sont le Volume (des échanges de plus en plus massifs), la Variété (plus de types de contenus) et la Vélocité (la collecte et le traitement en temps-réel). Depuis, d’autres ont essayé d’approfondir le sujet, en incluant des éléments comme la Validité, la Véracité, la Valeur ou la Visibilité des data. Le Big Data, c’est d’abord l’explosion du VOLUME de données, qui met à l’épreuve les infrastructures de stockage classiques des des entreprises D’après le magazine Fortune: Des dinosaures jusqu’en 2003 nous avions créé, en tout, 5 exabytes de données (5 Mds de GB). Cette explosion est due à un ensemble de progrès technologiques (objets connectés, infrastructures d’échanges de données notamment), qui va de pair avec des évolutions sociétales vers un plus grand partage d’information (opendata, Data as a Service).
NEW: Big Data vs Open Data - Mapping It Out - Open Data Now Follow this site on Twitter and subscribe to biweekly Open Data Now Insights As I’ve worked on my upcoming book, Open Data Now – to be published by McGraw-Hill on January 10 – I’ve had to think through and explain how Open Data, Big Data, and Open Government are related to each other. Lately I’ve seen a number of others, like the authors of the new McKinsey Open Data report (see page 4), try to map the territory in similar ways. The Open Data community is producing a lot of Venn diagrams these days, with a lot of colorful overlapping circles. (Some also deal with the use of Personal Data, but that’s one circle too many for me.) For my own contribution to the discussion, I’m proposing the model shown here. My starting point is the evolving understanding of these three areas. While others have produced thoughtful and comprehensive descriptions of Open Data, some refer largely to open government data, which I think of as a category of Open Data (though perhaps the most important one). 1.
Big Data is the new Artificial Intelligence This is the first of a couple columns about a growing trend in Artificial Intelligence (AI) and how it is likely to be integrated in our culture. Computerworld ran an interesting overview article on the subject yesterday that got me thinking not only about where this technology is going but how it is likely to affect us not just as a people. but as individuals. How is AI likely to affect me? The answer is scary. Today we consider the general case and tomorrow the very specific. The failure of Artificial Intelligence. It didn’t work. Artificial Intelligence or AI, as it was called, absorbed hundreds of millions of Silicon Valley VC dollars before being declared a failure. The human speed bump. You see in today’s version of Artificial Intelligence we don’t need to teach our computers to perform human tasks: they teach themselves. Google Translate, for example, can be used online for free by anyone to translate text back and forth between more than 70 languages. Google Brain. 4) rinse repeat
The Rise of Big Data Everyone knows that the Internet has changed how businesses operate, governments function, and people live. But a new, less visible technological trend is just as transformative: “big data.” Big data starts with the fact that there is a lot more information floating around these days than ever before, and it is being put to extraordinary new uses. In the third century BC, the Library of Alexandria was believed to house the sum of human knowledge. This explosion of data is relatively new. Given this massive scale, it is tempting to understand big data solely in terms of size. To continue reading, please log in. Don't have an account? Register Register now to get three articles each month. As a subscriber, you get unrestricted access to ForeignAffairs.com. Register for free to continue reading. Registered users get access to three free articles every month. Have an account?
Big Data : comparatif des technos, actualités, conseils... Pour faire face à l'explosion du volume des données, un nouveau domaine technologique a vu le jour : le Big Data. Inventées par les géants du web, ces solutions sont dessinées pour offrir un accès en temps réel à des bases de données géantes. Comment définir le Big Data, et quelle est sa promesse ? Face à l'explosion du volume d'informations, le Big Data vise à proposer une alternative aux solutions traditionnelles de bases de données et d'analyse (serveur SQL, plateforme de Business Intelligence...). Quelles sont les principales technologies de Big Data ? Elles sont nombreuses. Des bases de données NoSQL (comme MongoDB, Cassandra ou Redis) qui implémentent des systèmes de stockage considérés comme plus performants que le traditionnel SQL pour l'analyse de données en masse (orienté clé/valeur, document, colonne ou graphe). Des infrastructures de serveurs pour distribuer les traitements sur des dizaines, centaines, voire milliers de nœuds.
Big Data Needs a Big Theory to Go with It As the world becomes increasingly complex and interconnected, some of our biggest challenges have begun to seem intractable. What should we do about uncertainty in the financial markets? How can we predict energy supply and demand? How will climate change play out? What does this mean? What makes a “complex system” so vexing is that its collective characteristics cannot easily be predicted from underlying components: the whole is greater than, and often significantly different from, the sum of its parts. The digital revolution is driving much of the increasing complexity and pace of life we are now seeing, but this technology also presents an opportunity. The trouble is, we don't have a unified, conceptual framework for addressing questions of complexity. When the industrial age focused society's attention on energy in its many manifestations—steam, chemical, mechanical, and so on—the universal laws of thermodynamics came as a response.
Big Data - La clé ? La "remise en cause permanente" Une révolution le Big Data ? Plus surement une évolution de techniques et processus existants, et en particulier de la Business Intelligence. Mais concrètement, comment transformer un système décisionnel classique en une déclinaison Big Data ? François Nguyen, directeur du SI connaissance client de SFR, devait apporter des réponses à cette question à l'occasion du salon Big Data Paris 2015. C'est finalement Abed Ajraou, le directeur du décisionnel pour Solocal (groupe PagesJaunes), qui a assuré la présentation. "La BI n'a pas vocation a être silotée" Alors certes le Big Data introduit d'importants changements techniques : "Mais ce n'est pas que technique. "La façon de faire, c'était de prendre des bases de données, de faire des traitements ETL, d'alimenter un datawarehouse. Et pour tirer profit de ces technologies, les équipes Big Data vont devoir notamment se former en interne, être en veille permanente car ces solutions évoluent très rapidement, et ainsi, in fine, monter en compétence.
Big Data | Technology Research Are you trying to figure out how your enterprise can benefit from big data and why it's important? Don't get left behind. Uncover the business opportunities you can derive from big data. You need to make the strategic decisions that will transform your business. Hear Gartner recommendations on how to improve your business performance with big data. Register for your complimentary, on-demand webinar on Big Data and Analytics Strategy Essentials . IT is under pressure to tap into growing quantities of data to help the business make better, informed decisions by combining new sources of big data with existing enterprise dark data. Get more from your big data analytics with advice from Gartner. What are you doing with all of the data your organization collects? Learn how you can augment your underutilized data and deliver more value.
Big Data - B&D France Le Big Data, ce n'est pas simplement utiliser de nouvelles technologies pour la gestion de grande volumétries de données, en comptant sur la créativité de certaines populations d'utilisateurs pour en trouver les bons usages. Cela commence par l'identification, avec les métiers, des questions importantes pour l'amélioration de leurs performances, auxquelles ils ne peuvent trouver de réponse avec les systèmes actuels du fait de données initiales trop volumineuses, trop dispersées ou pas suffisamment structurées pour être utilisables. Cela se poursuit par la mise en oeuvre d'une démarche évolutive "Big Data", destinée à apporter des premières réponses sur des premiers ensembles de données. Cette démarche s'enrichit progressivement en termes de services rendus, de traitements possibles et de prises en compte de nouveaux capteurs de données : nouvelles applications dans l'entreprise, objets intelligents, données dites non structurées (documents, web, intranet...), données externes...