Five key to large data applications

The wide application of science and technology makes the market environment and social state become more and more object-connected (instrumented), interconnect (interconnected) and intelligent (intelligent), and these three elements change the source of data, transmission way and use way, bring "big Data" The change in the information society.

The big Data age has two distinct differences--a large number of new data is no longer applicable to traditional databases, while analytical capabilities are crucial to enterprise implementation of large data. These two distinctions come mainly from 4V characteristics of large data: quantity (Volume), multiplicity (produced), velocity (velocity), and authenticity (veracity).

Although the first three v covers the key attributes of the large data itself, authenticity is an important dimension that needs to be considered by the current enterprise, prompting them to use data fusion and advanced mathematical methods to further improve the quality of data, thereby creating higher value. But now, the industry has developed from the understanding of the importance of large data to the implementation phase of the need to practice large data, therefore, how to ensure the authenticity of data, how to mining data value is a key issue in the application of large data.

In this respect, the March 12, 2013 issue of the analysis: large data in the real world application of the white paper provides a large data application of the five key recommendations, including:

1, "customer-centric", the development of the early "big data strategic planning";

2, the establishment of a comprehensive and complete enterprise "Big Data Blueprint";

3, starting from the existing data, set up and complete short-term and phased "large data strategic objectives";

4, according to the business priority, the gradual establishment of the analysis system, step-by-Step Upgrade "Large data analysis capabilities";

5, custom measurable indicators to analyze the "large Data Roi" (ROI).

The findings come from a large data study conducted by the IBM Institute of Business Values in conjunction with the Oxford University School of Commerce. The project surveyed 1144 business people and IT professionals in 95 countries, 26 industries, and interviewed over 20 academics, business-themed experts and business executives.

The results showed that nearly two-thirds (63%) of respondents said that the use of information (including large data) and analysis created a competitive advantage for their organizations. This ratio has increased by 70% in just two years, compared with IBM's 2010 global executives of new intelligence companies. According to IDC, the large number of structured and unstructured data is growing at a rate of 60% per cent a year, and by 2020 global data volumes will grow by 44 times times to 35.2ZB.

In view of the current demand for large data, only by helping enterprises to build large data landing strategy can we achieve high value growth through real and effective large data deployment. Among them, the choice of "Big Data platform" and "Big Data analysis" is very important.

IBM shares some of its own cases. This is IBM's five business needs and corresponding large data landing practice, hope to be able to deploy large data for enterprises to provide valuable reference.

Using large data to explore and realize the enrichment of information base. Customer service, insurance, automobiles, maintenance, medicine and other industries need to store a large number of knowledge base, and complex solution manuals and knowledge system will cause repeated inquiries, resulting in system delays and cost increases. In the airline industry, for example, a global airline manufacturer deploys IBM Infosphere Data Explore, enabling technicians, support personnel, and engineers to instantly view information from a single access point in different applications; in the first year of deployment, the company's 24x7 support call time from the past 50 The minutes are shortened to 15 minutes, providing a global range of services for an additional 40 aircraft without adding support staff, saving a cost of 36 million dollars a year.

Achieve customer interaction improvements with an enhanced 360-degree omni-directional customer view. Industries such as telecommunications, retailing, tourism, financial services and automobiles will be the top priority for "quickly grabbing customer information to understand customer needs." By deploying IBM Infosphere Data Explorer, a leading multinational FMCG manufacturer can more effectively search for the most relevant information, thus speeding up the decision-making process, reducing duplication of effort and increasing the productivity of the global workforce.

Operation optimization is realized by operation analysis. Industries such as manufacturing, energy, utilities, telecommunications, travel and transportation need to keep an eye on emergencies, improve operational efficiency through monitoring, and anticipate potential risks. Pakistan's leading mobile operator, Ufone, has deployed IBM's large data solution to effectively reduce the rate of customer churn by identifying user behavior in real time, conducting marketing campaigns against specific targets, and using predictive analysis to design better marketing campaigns and telemarketing programs. The solution includes leading stream computing tools IBM Infoshere Streams, IBM Cognos and predictive analytics tools IBM, and a full range of hardware and service support.

Improve it efficiency and scale efficiency with data warehouse expansion. Enterprises need to enhance the value of the existing Data warehouse infrastructure through new and large data technology, realize the requirements of structured, unstructured and streaming data transmission, low latency, and query, and ensure the efficient use of predictive analysis and business intelligence to achieve performance and scalability. A car manufacturer successfully leveraged the IBM Infosphere biginsights to enhance the existing data warehouse for rapid deployment and easier management.

The use of security and intelligent expansion to achieve crime prevention. Government, insurance and other industries enterprises need to use large data technology to supplement and strengthen the traditional security solutions, analysis of new data. Terraechos, a supplier of secret intelligence and Surveillance sensor systems, provides security monitoring of science and technology and resources for customers in the U.S. Department of Energy (DOE) national Laboratories. The deployment of IBM Infosphere Streams,terraechos enables real-time analysis and classification of streaming acoustic data, reducing the time required to capture and analyze 275MB acoustic data in real time from a few hours to one-fourteenth seconds and greatly improve monitoring accuracy.

From the above examples, we can see that performance management, risk analysis, decision management, and content analysis constitute a "large data analysis" capability requirements, only with these capabilities, large data applications can really work, to provide decision-makers with comprehensive, unified and accurate information to help them gain insights in the surge of data, Explore business opportunities and values, make more effective decisions and ultimately improve performance.