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.
Organizations measure and use social media analytics tools to find out what is being discussed about them, their competition and other topics of interest. Social media monitoring includes measuring opinions and sentiment of groups and influencers. 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. Klout. Build a Culture around Data for Analytics. Bhattacharya-karyar_paper.pdf (application/pdf Object) Transforming Noise into Signal: Isolating Social Business Results. One of the biggest obstacles I see that businesses engaging in social media have today is clearly understanding the effect their efforts — particularly in externally facing processes such as marketing — are actually having.
There are a number of reasons for this and together they conspire to create an environment that leads to uncertainty and an inability to claim the fair results of a company’s hard work at social engagement with the marketplace. The first challenge to isolating results is the sheer scale of social conversation today. There are over a billion Facebook users today, hundreds of millions of people on Twitter and LinkedIn alone, and there are thousands of smaller yet often more important social networks, online communities, and other niche, special interest and industry-specific community forums where relevant social activity takes place. Additional Reading: Eight Ways to Prepare for Social Engagement At Scale The final issue goes to timeliness. Can’t Isolate Impact? Linked-data-connecting-and-exploiting-big-data-(v1.0).pdf (application/pdf Object) How google is using linked data today and vision for tomorrow.
How Linked Lifecycle Data can transform your systems engineering environment. Background: The web of documents Everyone who has ever used a browser is familiar with the World Wide Web that we've been enjoying for many years. This Web — really a web of documents — has provided a foundation for us to share previously unimaginable amounts of information, yet it has some key implementation details that ultimately impose a limit on its usefulness.
The Web represents information as text on pages. It was designed to allow humans to read, filter out redundant information, and infer meaning based on the natural language used, the context of the information, and the existing knowledge of the reader. The document-centric Web does contain a lot of useful data, though. When we search the Web, we rely on algorithms employed by search engine indexers to provide links to documents that the indexer believes are relevant, but those might or might not contain the information that we seek. Linked Data has four main supporting principles, defined by Tim Berners-Lee: Listing 1. Part 2. Does Big Data Need Bigger Data Quality and Data Management? By Virginia Prevosto and Peter Marotta Running faster won't get you to the right place if you don't know where you're going.
Even if you do know your destination, you need the right road markers to help you on the way. Big Data and, more important, the analytics that Big Data fuels are the technology du jour. But creating better, faster, more robust means of accessing and analyzing large data sets can lead to disaster if your data management and data quality processes don't keep pace. Traditionally, data management and data quality principles and processes have included: Will those methods continue to work with Big Data, or are new principles and processes needed?
A 2011 McKinsey Global Institute study outlines a number of Big Data techniques and technologies. Metadata Metadata is important to any data management activity. Data Acquisition In acquiring data, it is critical for data to be organized to be more readily assessable. The greatest impact of Big Data is on data quality. How to Repair Your Data - Thomas C. Redman. By Thomas C. Redman | 11:00 AM September 27, 2012 One can’t help being impressed with the effort biologists, physicists, and other scientists devote to data quality. From careful design of experiments and data collection processes, to explicit definition of terms, to comprehensive efforts to ensure the data are correct, no effort is spared. This is not surprising. After all, data are the lifeblood of science. Increasingly, data are also the lifeblood of business and government.
Simply put, bad data make everything about Big Data — from discovering something truly novel, to building a product or service around that discovery, to monetizing the discovery — more difficult. The data are poorly defined, leading to incorrect interpretations. Worse, in business bad data can be downright dangerous. Early computer programmers recognized that having bad data was an issue and coined the expression, “garbage in, garbage out.” Address preexisting issues. More >>
Union-metrics-case-study.pdf (application/pdf Object) Beyond Big Data: Q&A with Alistair Croll. You write extensively about big data (and wrote a book on web monitoring). How do you think data is changing the way we understand businesses? There’s a book by Eric Ries called The Lean Startup. The whole principle of lean is build, measure then learn. The lean model says identify the thing that’s most uncertain, and then build in experiments just enough to verify or repudiate that uncertainty and then iterate. And the way you do that measuring part is data. So if you learn to iterate quickly and you collect data and learn from it and build those lessons into the next iteration faster than the competition, you will win. This is true of any industry, from press to start-ups to big companies, and this data-driven intel is absolutely exploding because of the sheer volume of new sources of data we’ve created.
There are people out there who see this vast amount of data and their eyes glaze over. It’s absolutely essential. In Philip K. In a dozen years it will be unthinkable not to have one. BuzzFeed's Social Media Editor on Why Twitter is the New Press Scrum. This post is part of the Social Media Editor Series, featuring interviews with social media editors from news organizations about what they do and where they see social media in journalism going. Michael Hayes’ ambition to make BuzzFeed the number one social news organization is predicated on the idea that social media is the new news. “Twitter is the new press scrum,” BuzzFeed’s social media editor told The Content Strategist. “It’s the place where news goes to break first.” He’s not wrong. The world of news has largely become intertwined with that of social media, so much so that Hayes said BuzzFeed readers come to the site — or, more appropriately, to its Twitter feed — with the intention of sharing media. The BuzzFeed staff curates viral content with the idea that such content is what people inherently want to read about.
“Basically we want to be a device for people to share news and content with friends,” Hayes said, “because that’s how people prefer to get their news these days.” Monitoring Protests and Unrest - Recorded Future Webcast. Big Data Solution Offering. From MIKE2.0 Methodology Introduction The Big Data Solution Offering provides an approach for storing, managing and accessing data of very high volumes, variety or complexity.
Storing large volumes of data from a large variety of data sources in traditional relational data stores is cost-prohibitive. And regular data modeling approaches and statistical tools cannot handle data structures with such high complexity. Executive Summary Big Data can be defined as data that will be stored in data stores categorized as “NoSQL” due to their lack of compatibility with the SQL language that is so ubiquitous in the relational database world. Most NoSQL is open source and most of the well over one hundred NoSQL open source projects are not data stores.
NoSQL solutions originated out of a need by data-oriented companies like Google, Facebook, eBay and Yahoo to store the massive amounts of information their systems generate. Certain aspects of NoSQL are common across all the categories and projects: 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.
Business movements like BYOD (Bring Your Own Device) and Big Data have paved the way for IT pros who want to sit at the "big table. " 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. So who knows what systems to put in place to collect this all-important data? 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.
LDAP directories promised a single, scalable, high performance data store that could be queried for common information across multiple applications. After all, directories had a lot of virtues: Fast Queries: LDAP directories were heavily indexed, so query speeds were truly impressive—reliably 10x what a relational database could manage. Telecom protocols FTL: LDAP, in my own humble opinion, was fatally crippled by its telecom parentage. Tagged: 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.
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. Increased interest in and awareness of the power of Big Data and related analytic capabilities to gain competitive advantage and to improve operational efficiencies, coupled with developments in the technologies and services that make Big Data a practical reality, will result in a super-charged CAGR of 58% between now and 2016.
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: HP “All in on Big Data.” More Acquisitions Ahead? HP has watched Big Data develop over several years and believes that it will cause major disruption in the storage and systems market, says Manoj Goyal, Senior Director of Data Management Solutions in HP’s Enterprise Group. As a result, “HP is all in on Big Data,” he said in an interview in The SiliconAngle Cube at HP Discover 2012 (full video below). It is working to develop a complete ecosystem around Hadoop that includes hardware, middleware, software, and services to “make Big Data more accessible to customers.” And, he said, “While HP does not make its M&A plans public, we are always looking for partners and ways to fill in gaps.
Over time big things will happen.” HP, he said, is solving the “Big Data consumption problem. What was Hadoop/Map Reduce has become an ecosystem.” The first step in productizing Big Data was simplifying the underlying infrastructure users need to support their Big Data installations. HP also realized early that Big Data goes hand-in-hand with cloud. Big Data Manifesto | 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. EnablingAnalysis_Synygy_Summer08.pdf (application/pdf Object) 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. Web 2.0. Memetics. Susan Blackmore - Memetic Evolution. Memes on the Net. Quotes-kdd09.pdf (application/pdf Object) Fredmcclimans.com. Study: Social media is 'brain candy' - Vote for the best company in Austin's business competition.
Wide-Open Search - Computerworld. BigQuery. 2011-04.pdf (application/pdf Object) Twitter’s Redesigned Discover Tab Is Watching You. Big Data: Open to Definition « opencollaborarchy. Understanding Big Data « opencollaborarchy. The 7 steps in Big Data delivery. Why Big Data Needs To Be Functional. How Recorded Future Works To Unlock The Predictive Power Of The Web. How to Fix Location-Based People Discovery. Decomposing Twitter (Database Perspective) 5 QUESTIONS with Gnip Inc. CEO Jud Valeski. 10 Ways to Discover Social Media Content. Google's Knowledge Graph Has An Error 20% Of The Time. Jasper Soft eBook Five Levels of Embedded Bi PDF 16098. State Street’s Chief Scientist on How to Tame Big Data Using Semantics.
Social CRM: Learning the Social CRM Data-Management Ropes.