Big Data Failures

Now, the industry and academia have been hot on the big data, whether it is academia or it circle, as long as can talk about some big data is very tall. However, big Data mining, big data analysis, big Data marketing and so on are just the beginning of course, there are many people directly criticize the big data or large data marketing to create privacy threats to us, what is the big data in the end? What is the value of it? Today, the industry and academia have been talking about a word, which is big data. Whether it's academia or it, it's big enough to talk about a little bit of data. However, large data mining ...

"Through the excavation of large data, from the integrity of the data, the sincerity of friends and account security rating users ' reliability '." "A famous matchmaking website recently advertised that it could use" big data "technology to crack down on dating crooks. Today, "Big data" is a fashionable word, many businesses have launched "Big data" services. Large data technology research and development institutions around the world attract many VCs and eyeballs. Big Data makes a person bright, also let a person two eyes one black. September, the famous Information Technology analysis company Gartner published the "2013 large data dissemination of hype behind the" Report ...

"Through the excavation of large data, from the integrity of the data, the sincerity of friends and account security rating users ' reliability '." "A famous matchmaking website recently advertised that it could use" big data "technology to crack down on dating crooks. Today, "Big data" is a fashionable word, many businesses have launched "Big data" services. Large data technology research and development institutions around the world attract many VCs and eyeballs. Big Data makes a person bright, also let a person two eyes one black. September, the famous Information Technology analysis company Gartner published the "2013 large data dissemination of hype behind the" Report ...

Large data technology is exciting, innovative and powerful. Large data technology can definitely bring business analytics to a new level ... But not now. The skills and best practices of business Intelligence (BI) have accumulated for many years in 1000 major global companies and countless smaller companies, and the relational database management system (RDBMS) is up to dozens of years. The products in these categories have excellent tool technology, manageability, and fault tolerance, providing interfaces for non-developer design, with carefully created data models that represent a great deal of input over the years. ...

Big Data has developed into a critical phase. By 2017, the big data market will have grown to 50 billion dollars, but unfortunately 55% of the big data projects have failed. With the opportunity is the hype and false information, we are in the large data of the West drought stage. 　　The big data industry is in a bit of a stalemate: people who understand it invest in the industry to collect, store and benefit from large data, while others pay for it with a dubious attitude and wonder how big data will affect their business. Benign failure Big data allow error ...

2012, if one of the hottest it words of the year, then not large data. Data has been around for a long period of years, but large numbers have been appearing frequently in the media in the last 1 decades. Big data is of great value, and that seems to be the point everyone agrees on. But people always pay attention to the good side of things, but ignore the disadvantages of large data. There is no doubt that large data can bring a lot of economic value and benefit to enterprises and institutions, and directly affect their future trend. In fact, large data is a double-edged sword, in the enterprise to bring the momentum of the advance, often also on the enterprise ...

Big Data has now become the focus of attention, many companies are struggling to put their own data to use, hoping to provide support for important decisions. While big data hype and hype are raging, 92% of companies remain neutral, planning to start at "the right time" or to say they don't intend to touch big data projects. 　　In companies that have already hands-on big data projects, most fail and often fall into the same trap. The key to success with large data projects is to build an iterative program that encourages existing members ...

Lead: The New York Times Printing Paper published an article on the 30th that big data will evolve into a new chapter in the history of human commerce, most likely to replace the paradigm of ideas, examples, organizations, and even human thinking of the world. But at the same time, experience and intuition are as indispensable. The following is an excerpt from the article: "Big data is important and intuitive as well." This was the subject of an industry conference held at the Massachusetts Institute of Technology earlier this month. Andrew McAfee, chief scientist at the Massachusetts Institute of Technology Digital Commerce Center, said big data will be a human business history ...

In the age of large data, "big data" has become the most frequently used word, large data received more attention, "big data" is basically an all-encompassing term, refers to the ability to collect and analyze a large amount of information, and this information relates to all aspects of human life. 　　Past data analysis may require expensive databases and specialized technology, and in recent years startups that have been linked to big data concepts have sprung up and, if you are willing, are fully qualified to use large data analysis at low cost. Whether from the general business sector, or retail, medical, climate ...

This article describes ways to perform large data analysis using the R language and similar tools, and to extend large data services in the cloud. In this paper, a kind of digital photo management which is a simple and large data service is analyzed in detail, and the key elements of searching, analyzing and machine learning are applied to the unstructured data. This article focuses on applications that use large data, explains the basic concepts behind large data analysis, and how to combine these concepts with business intelligence (BI) applications and parallel technologies, such as the computer Vision (CV) and ... as described in part 3rd of the Cloud Extensions series.