web 2.0 The newest video from Michael Wensch’s Digital Ethnography @ Kansas State project is titled Information R/evolution. It picks up where the first video that we watched, “The Machine is Us/ing Us,” left off. Go ahead and watch it; I’ll wait: There is so much to love about this video, not least of which that it begins in a library and is seemingly pro-librarian: “Managing information IS managing categories; it requires experts.” Fast forward through the concepts teased out in David Weinburger’s excellent Everything is Miscellaneous. Another idea that hit me full in the face from this video was the idea that there are 5 trillion words on today’s web, which is about 15 years old, a microsecond on the timeline of human history. Wensch points to Wikipedia as an example of the idea that “together, we create more information than the experts.”
Livres blancs sur l'eCRM Ressources Accédez à toutes nos ressources : vous trouverez ci-dessous nos livres blancs, bonnes pratiques, avis d’experts et cas clients couvrant l’ensemble de notre expertise. CONTACTMENTIONS LÉGALESPLAN DU SITE© Copyright Cabestan 2013. Tous droits réservés. Think BIG, Act SMALL: Use Case Series – Part 4 of 5 (Using Big Data in Telecommunications and Media & Entertainment) « Big Data Big Data use-cases in Telecommunications In recent decade, telecom industry has seen data explosion due to increase in subscription, voice data record, wireless information, geo-location details, social media and data usages. Telecom companies who used legacy systems to gain insights from internally generated data often face issues of high storage costs, long data loading time, long administration process, complex queries, outdated compression techniques, and high support costs. Many organizations are beginning to wake to the reality of big data. Here are some of the use cases for Big Data in Telco business. 1. 2. 3. 4. 5. 6. Big Data use-cases in Media & Entertainment The media/entertainment industry moved to digital recording, production, and delivery in the past few years and is now collecting large amounts of rich content and user viewing behaviors on real-time basis. 1. 2. 3. 4. 5. 6. 7. 8. 9. Next time: Part 5 of 5 (Using Big Data in Utilities, Hi-Tech and ECommerce) About Author:
Social CRM: Learning the Social CRM Data-Management Ropes If there's one thing social media offers, it's data. The trick is knowing how to access, manage and use it. "You truly unleash the power of social media when you are able to combine it with the business' own unique data sets," said Wilson Raj, global customer intelligence director with SAS. "This helps the business filter out a huge volume of distracting, meaningless social media conversations." ManageEngine OpManager, a powerful NMS for monitoring your network, physical & virtual (VMware/ HyperV) servers, apps & other IT devices. Every time people post an update, "like" a page, or otherwise engage with social media, their movements are tracked, followed and recorded. "Businesses should view social media as a treasure trove of data and insights that can virtually impact every business function of their organization," Wilson Raj, global customer intelligence director with SAS, told CRM Buyer. 2-Way Data Street Ever-Changing Landscape Social media is not a simple realm. Mining Tools
Histoire du cloud computing de la naissance à nos jours Selon les prévisions, d'ici 2020, le marché du cloud computing devrait dépasser les 241 milliards de dollars… Mais comment en est-on arrivé là ? Par quoi tout a commencé ? Même si le cloud computing est en réalité assez récent (les premiers sites Web grand public et professionnels de services de cloud computing, Google et salesforce.com, ont été lancés en 1999), son histoire est directement liée au développement d'Internet et des technologies d'entreprise. Le cloud représente en effet la solution idéale permettant d'allier les bénéfices de l'Internet et les améliorations des technologies d'entreprise. L'histoire des technologies d'entreprise est longue et fascinante, presque aussi longue que celle du commerce lui-même. Les balbutiements d'Internet La naissance des technologies d'entreprise et le rêve d'un réseau mondial Le premier circuit intégré fonctionnel est développé à la fin des années 1950, permettant ainsi aux ordinateurs d'effectuer des calculs plus complexes. Le saviez-vous ?
The Vendor Landscape of BI and Analytics by Ravi Kalakota “In God we trust, all others bring data” The “Raw Data -> Aggregated Data -> Intelligence -> Insights -> Decisions” is a differentiating causal chain in business today. The Business Intelligence, Performance Management and Data Analytics is a large confusing software category with multiple sub-categories — mega-vendors (full stack, niche vendors, data discovery, visualization, data appliances, Open Source, Cloud – SaaS, Data Integration, Data Quality, Mobile BI, Services and Custom Analytics). But the interest in BI and analytics is surging. Here is a list of vendors who participate in this marketspace: Big Data Startup and Existing Companies to Watch Emerging Players — Cloudera, DataStax, Northscale, Splunk, Palantir, Factual, Kognitio, Datameer, TellApart, Paraccel, HortonworksEstablished Players — EMC Greenplum , HP Vertica, IBM/Netezza, Microsoft, Oracle ExaData, SAP HANA, Teradata (acquired Aster Data)Big Data as a Service – Opera Solutions Like this: Like Loading...
Says Big Data Makes Organizations Smarter, But Open Data Makes Them Richer STAMFORD, Conn., August 22, 2012 View All Press Releases Open Data on the Agenda for Gartner Symposium/ITxpo, October 21-25, Orlando, Florida Whereas "big data" will make organizations smarter, open data will be far more consequential for increasing revenue and business value in today's highly competitive environments, according to Gartner, Inc. "Big data is a topic of growing interest for many business and IT leaders, and there is little doubt that it creates business value by enabling organizations to uncover previously unseen patterns and develop sharper insights about their businesses and environments," said David Newman, research vice president at Gartner. "However, for clients seeking competitive advantage through direct interactions with customers, partners and suppliers, open data is the solution. Gartner analysts believe an open data strategy should be a top priority for any organization that uses the Web as a channel for delivering goods and services. Contacts About Gartner
Ces DSI qui prennent le virage du Big Data : Catalina Marketing : le Big Data au cœur du métier du couponing Catalina Marketing a mis le Big Data directement au service de son cœur de métier. A savoir : la gestion de campagnes marketing en magasin sous la forme d'opérations de couponing ciblées. La société s'adosse à une infrastructure de traitement de données en masse pour générer et calculer ses bons de réduction lors du passage des clients en caisse, en réalisant une analyse en temps réel du contenu des caddies. Une autre application Big Data déployée cible la réalisation d'études pour connaitre le potentiel d'un nouveau produit. Une infrastructure massivement parallèle de serveurs Netezza En vue de répondre aux besoins de traitement temps réel, la plate-forme Big Data déployée par Catalina Marketing est basée sur une infrastructure massivement parallèle de boîtiers de base de données IBM Netezza. Faire en sorte que l'informatique s'efface au profit des métiers La gestion du réseau et des entrées et sorties sont aussi optimisées.
Oracle Exadata and Exalogic Sales When Oracle announced the Sun technologies-based Exadata in 2010, it claimed to have a backlog of $1 billion. Since then we have seen a steady retreat in optimism about Sun sales in general (including Exadata and Exalogic products) illustrated by a 14% drop in Sun sales in the last quarter. Recent Oracle statements have combined Exadata and Exalogic sales together. Oracle stated that it sold 200 Exadata/Exalogic systems in 2Q 2012 and approximately 1,000 since these systems were introduced. Table 2 shows that the installed hardware sales to date are about $400m, and if service and software are included, about $900m. This analysis suggests a significant erosion of the original $1B Exadata-only backlog. Action Item: This analysis together with the Wikibon comparison of Exadata costs with other solutions indicates the market for hardware sales driven only by development is limited. Footnotes:
Big Data Market Size And Vendor Revenues By Jeff Kelly with David Vellante and David Floyer This is the 2011 report, originally published on February 15, 2012. See Big Data Vendor Revenue and Market Forecast 2012-2017 for the 2012 update. The Big Data market is on the verge of a rapid growth spurt that will see it top the $50 billion mark worldwide within the next five years. As of early 2012, the Big Data market stands at just over $5 billion based on related software, hardware, and services revenue. As explained in our Big Data Manifesto, Big Data is the new definitive source of competitive advantage across all industries. Below is Wikibon’s five-year forecast for the Big Data market as a whole: Figure 1 - Source: Wikibon 2012 Of the current market, Big Data pure-play vendors account for $480 million in revenue. Wikibon considers Big Data pure-plays as those independent hardware, software, or services vendors whose Big Data-related revenue accounts for 50% or more of total revenue. Figure 2 - Source: Wikibon 2012
Path Intelligence | Retail measurement technology Hadoop and Netezza: Differences & Similarities Most of the time vendor videos are emphasizing the superiority of their own commercial platform. But this short video gives a fair overview of the similarities and differences between Hadoop and Netezza. The video is 5 minutes long and well worth watching. Krishnan Parasuraman (IBM Netezza Chief Architect) also mentiones a couple of scenarios where using both solutions would deliver an optimal solution: Hadoop used as a data ingestion layer for large volumes of dataHadoop is a system of archive In both these scenarios, Netezza would would be the tool for performing deep data analysis, while Hadoop would be used as both a cost-effective storage solution and ETL processing system. Original title and link: Hadoop and Netezza: Differences & Similarities (NoSQL database©myNoSQL)
54 Free Social Media Monitoring Tools [Update2012] If you want to know what’s happening with your brand’s social networking sites you need social media monitoring tools. Before you reach for your wallet and start to spend money try out some of the free social media monitoring services. This way you will get an understanding of what is available and if you need any paid services to monitoring social media. Social media monitoring definitionThe activity of tracking social media channels. This time we have collected a lot of free social media monitoring tools. Group A HootSuite Twitter account: HootSuite Monitor and post to multiple social networks, including Facebook and Twitter. TwitterCounter Twitter account: Thecounter Twitter Counter is the number one site to track your Twitter stats. Social Mention Twitter account: socialmention Social Mention is a social media search and social media analytics tool that aggregates user generated content into a single stream of information. Klout Twitter analytics Twitter account: twitter (doh!) Crowdfire
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.