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Hadoop, Business Analytics and Beyond

Hadoop, Business Analytics and Beyond
A Big Data Manifesto from the Wikibon Community Providing effective business analytics tools and technologies to the enterprise is a top priority of CIOs and for good reason. Effective business analytics – from basic reporting to advanced data mining and predictive analytics — allows data analysts and business users alike to extract insights from corporate data that, when translated into action, deliver higher levels of efficiency and profitability to the enterprise. Underlying every business analytics practice is data. Traditionally, this meant structured data created and stored by enterprises themselves, such as customer data housed in CRM applications, operational data stored in ERP systems or financial data tallied in accounting databases. Traditional data management and business analytics tools and technologies are straining under the added weight of Big Data and new approaches are emerging to help enterprises gain actionable insights from Big Data. The Changing Nature of Big Data

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.”

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

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

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

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

Hadoop and NoSQL - Part IV - Architecting for Analytics - Blog: Wayne Eckerson Architecture The term "analytical architecture" is an oxymoron. In most organizations, business analysts are left to their own devices to access, integrate, and analyze data. By necessity, they create their own data sets and reports outside the purview and approval of corporate IT. By definition, there is no analytical architecture in most organizations--just a hodge-podge of analytical silos and spreadmarts, each with conflicting business rules and data definitions. Analytical sandboxes. There are four types of analytical sandboxes: Staging Sandbox. Next-Generation BI Architecture. Figure 1. The next-generation BI architecture is more analytical, giving power users greater options to access and mix corporate data with their own data via various types of analytical sandboxes. Analytical Platforms The analytical platform movement. Table 1. Moreover, many of these analytical platforms contain built-in analytical functions that make life easier for business analysts. Analytical Tools Data

The 7 steps in Big Data delivery Network World - This vendor-written tech primer has been edited by Network World to eliminate product promotion, but readers should note that it will likely favor the submitter's approach. The Big Data trend represents the evolving need to process large amounts of data with a new crop of technology solutions that aren't necessarily your father's database. So, what does a company need to consider when contemplating getting started with Big Data? First, they need to know what Big Data is. "The emerging technologies and practices that enable the collection, processing, discovery and storage of large volumes of structured and unstructured data quickly and cost-effectively." Big Data -- from financial trades to human genomes to telemetry sensors in cars to social media interactions to Web logs and beyond -- is expensive to process and store in traditional databases. MORE: Open source: Leading the way for big data applications ROUNDUP: 9 open source big data technologies to watch

Five legitimate use cases for NoSQL databases NoSQL may be one of the most overhyped technology trends in the past couple of years, and a growing number of companies that left their relational databases behind for a NoSQL fling are rethinking their decisions. Yet organizations continue to adopt NoSQL solutions and investors are still eager to pour money into vendors behind the most popular of them. Are they crazy, or has some of the NoSQL skepticism been overdone? The truth of the matter is that, hype aside, there is a role for NoSQL solutions to play in a world consumed by data, and increasingly companies are making smart decisions about when to use relational databases and when to turn to their NoSQL cousins. For organizations facing NoSQL versus relational decisions of their own, here are five use cases where a NoSQL database may have a legitimate role to play. 1. 2. Thanks to decreasing hardware costs, building a massive server for a relational database is an option for more and more companies. 3. 4. 5.

LDAP Directories: The Forgotten NoSQL - Engine Yard Blog When most Rails developers encounter LDAP, it's usually for user authentication. And most of the time, there's no choice, they're working under a dictate that requires them to use it. Usually, this means Active Directory, but very occasionally something like OpenLDAP or the Sun Java Systems Directory Server. It's hard to imagine now, but there was once great excitement about the potential for LDAP based directory servers to become more than just authentication servers and morph into general purpose datastores. Fast Queries: LDAP directories were heavily indexed, so query speeds were truly impressive—reliably 10x what a relational database could manage. In addition to these benefits, directories like Netscape Directory Server and Microsoft Active Directory had a seemingly endless list of other features like rich, complex configurable access control rules and permissions; multiple ways to define groups; rich query semantics and more. I do know one thing: keep the access control simple.

BI still dependent on IT There's never been a better time for IT to step up and show what tech can do to drive the business. They're the only people who can set the systems in motion to capture data and mine it successfully. A few years ago when IT/business alignment was the buzz phrase that you heard everywhere, the impetus for this coming to pass seemed to belong to IT. It was all about IT pros developing soft skills and being able to talk "business" with the others at the executive table. That still holds true, but what wasn't completely anticipated a few years ago was the way technology itself would propel the alignment of IT with business. In both cases, CEOs and end-users know what they want - numbers and statistics that demonstrate what the business needs to do more or less of to succeed. The Business Intelligence company LogiXML asked more than 750 IT professionals across multiple industries about BI and its users, and it found that 45 percent said users don't understand how much goes into a BI project.

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