The DataSift Platform Social data is noisy. Whether you’re trying to social analyze trends within an industry, or mentions of your products or brands, you need a platform that can filter out the noise and allow you to focus on the data that’s most relevant to you. This is especially important when you are paying for the social data you receive. At the heart of the DataSift platform is a high-performance filtering engine with which you can find the exact content and conversations that are relevant to your business. Go beyond keywords and filter on more than over 300 unique fields including author, location, language, and demographics.
Rapid application development Rapid Application Development (RAD) Model Rapid application development (RAD) is a software development methodology that uses minimal planning in favor of rapid prototyping. The "planning" of software developed using RAD is interleaved with writing the software itself. The lack of extensive pre-planning generally allows software to be written much faster, and makes it easier to change requirements. History[edit] Rapid Application Development (RAD) is a term originally used for describing a software development process first developed and successfully deployed during the mid-1970s by the New York Telephone Co's Systems Development Center under the direction of Dan Gielan. Rapid application development is a response to processes developed in the 1970s and 1980s, such as the Structured Systems Analysis and Design Method and other Waterfall models. Four phases of RAD[edit] Relative effectiveness[edit] Criticism[edit] Practical implications[edit] References[edit] Jump up ^ Maurer and S.
Big data analytics: From data scientists to business analysts - Data The growing popularity of Big Data management tools (Hadoop; MPP, real-time SQL, NoSQL databases; and others1) means many more companies can handle large amounts of data. But how do companies analyze and mine their vast amounts of data? The cutting-edge (social) web companies employ teams of data scientists2 who comb through data using different Hadoop interfaces and use custom analysis and visualization tools. A startup aims to expose Big Data to analysts charged with producing most routine reports. Datameer’s workflow uses the familiar spreadsheet interface as a data processing pipeline. What’s intriguing about DAS is that it opens up Big Data analysis to large sets of business users. The buzz over Big Data has so far centered largely on (new) data management tools6. (1) Splunk is a tool that does both Big Data management and analytics. (2) In fact data scientist is a title that’s increasingly used in companies like Yahoo!
About Kaggle and Crowdsourcing Data Modeling Kaggle is the world's largest community of data scientists. They compete with each other to solve complex data science problems, and the top competitors are invited to work on the most interesting and sensitive business problems from some of the world’s biggest companies through Masters competitions. Kaggle provides cutting-edge data science results to companies of all sizes. We have a proven track-record of solving real-world problems across a diverse array of industries including life sciences, financial services, energy, information technology, and retail. Read more about our solutions » Our community Our community of data scientists comprises tens of thousands of PhDs from quantitative fields such as computer science, statistics, econometrics, maths and physics, and industries such as insurance, finance, science, and technology. Interested in competing on Kaggle? Our Business Kaggle is a two-sided marketplace that bridges the gap between data problems and data solutions. Our story
Microsoft SharePoint 2010, más implantación pero sin estrategia La cuota de mercado de la plataforma de colaboración de Microsoft crece. Así lo revela un estudio de OpenText donde se pone de relieve la creciente preocupación por la falta de estrategia clara de las empresas a la hora de implantar SharePoint. El informe, realizado entre 362 personas habituadas a utilizar Microsoft SharePoint, demuestra que cada día es más frecuente dentro de las corporaciones, sobre todo, en la gestión de procesos de negocio y el flujo de trabajo. Sin embargo, el alcance del despliegue todavía no es claro. En este sentido, Lubor Ptacek, vicepresidente de marketing estratégico y director general de soluciones Microsoft en OpenText explica que “Con esta información tenemos un mejor conocimiento de las necesidades de nuestros clientes lo que nos permite mejorar nuestros productos, resolver los problemas de los clientes y aumentar la presencia de OpenText en el mercado SharePoint”. Las principales conclusiones del estudio son las siguientes:
Market basket analysis - identifying products and content that go well together Affinity analysis and association rule learning encompasses a broad set of analytics techniques aimed at uncovering the associations and connections between specific objects: these might be visitors to your website (customers or audience), products in your store, or content items on your media site. Of these, “market basket analysis” is perhaps the most famous example. In a market basket analysis, you look to see if there are combinations of products that frequently co-occur in transactions. For example, maybe people who buy flour and casting sugar, also tend to buy eggs (because a high proportion of them are planning on baking a cake). A retailer can use this information to inform: Store layout (put products that co-occur together close to one another, to improve the customer shopping experience) Marketing (e.g. target customers who buy flour with offers on eggs, to encourage them to spend more on their shopping basket) Online retailers and publishers can use this type of analysis to: ! !
Rocket Fuel » Digital Advertising Campaigns Run Better on Rocket Fuel » Rocket Fuel Estudio de mercado sharepoint en chile Big Data: 9 Steps to Extract Insight from Unstructured Data The increasing digitization of information in recent years, coupled with the proliferation of multi-channel processes and transactions, has resulted in a data deluge. The ever-increasing pace of digital information has led the world's aggregate creation of data to double in even shorter intervals than ever before. According to Gartner, about 80% of data held by an organization is unstructured data, comprised of information from customer calls, emails and social media feeds. This is in addition to the voluminous diagnostic information logged by embedded and user devices. While it would be a daunting to even make a proper analysis from organized data, it is very difficult to make sense of unstructured data. As a result, organizations have to study both structured and unstructured data to arrive at meaningful business decisions, including determining customer sentiment, cooperating with e-discovery requirements and personalizing their product for their customers. 1. 2. 3. 4. 5. 6. 7. 8.
Big Data Jobs at Jive Software Jive is on a singular mission to transform the way we work. We’re the first company to bring the social innovation of the consumer web to the enterprise. And in doing so, we’re making work great. We’re breaking down the barriers separating employees, customers, and partners making it possible for the first time to engage socially and genuinely around what matters most to them. That’s a Big Data problem. We work extensively with open source software on big data, social graph, and machine learning problems and creating an active, adaptive platform tuned to solve key business challenges. We contribute to various big data open source technologies: Apache Hadoop, HBase, Pig, ZooKeeper, Flume, Mahout, Neo4j and other open source projects. We’re also inventing new technologies that harness the power of Big Data and the enterprise social graph. And we’re hiring. Current Big Data Job Openings