Five typical scenarios for large data applications

"Data will become a strategic ingredient, and every business, research team and government has a responsibility to collect, process, analyze and index data purposefully." "Zhou, director of the Internet Center of the Institute of Management, called for companies to devote large numbers to big data. But based on extensive research into 1144 business people and IT professionals in 95 countries, 26 industries, IBM has found that most businesses have recognized the potential of "big data" to improve decision-making processes and business outcomes, but they don't know how to start.

Indeed, in the active or passive welcome to the era of large data, enterprise managers desperately need to work before doing, clear the answer to a lot of questions: 3V beyond the large data have what attributes? What is the key to the big data solution? Is there a chapter to the implementation of large data? ......

With the "Analysis: large data in the real World Application" white paper as a primer, IBM's Big data strategy efforts to solve the enterprise's many doubts. On this basis, with "intelligent analysis Insight" as the core of the IBM large data value system of the five typical business needs and corresponding landing practice, visualization shows how large data to drive the growth of business value.

Clear exerting force point

In large data and analysis areas, IBM is recognized to have sufficient technical advantages. Hu Shizhong, IBM Global vice-president and general manager of Greater China Software group, said: "The data constitute the three major elements of intelligent Earth: instrumented, interconnection (interconnected) and intelligence (intelligent), These three elements have changed the source of data, the mode of transmission and the way of utilization, and brought about the change of the information society of "Big Data". As a leader in the Big data field, IBM is leveraging leading methodologies and comprehensive data technology to help companies rethink their existing it models, and to leverage business transitions based on the information revolution to gain competitive opportunities and immeasurable commercial value. ”

In order to achieve this vision, it is necessary to know the enterprise's application of large data recognition and acceptance. The IBM Institute of Business Values and the Oxford University School of Commerce have teamed up to carry out a research and co-authored the White Paper, "Analysis: The application of large data in the real world."

In the white paper, where companies now have a holistic view of big data, their approach to big data is fully disclosed, and their progress in using large data to gain business value growth is disclosed by one by one.

Based on extensive and informative research, IBM has come up with several useful conclusions: the definition of large data is the main reason for confusing large data, and the adoption of large data by enterprises is still in the initial stage (most of the enterprises are mainly to understand the concept (24%) or to define the road map (47%) related to large data). , customer-centric is the first task of large data to become a consensus; Internal data is the main source of large data in the enterprise but a large amount of untapped value is implied in the internal system; uncertainty and lack of skills make the external data sources such as social media underutilized. Lack of advanced analytical skills is a major obstacle to maximizing value from large data.

De Huain Director Hongtao. Long-term work in the insurance industry, he said, large data in the insurance industry has great potential: the insurance industry uses large data, and now mostly stay in the "intensive use" phase; insurance companies have a wealth of customer data, transaction data and contact data, but the accumulation of data volume, often lead to "data graves" phenomenon occurs The insurance industry has not widely developed a large data intelligence application of awareness and ability. He believes that the insurance industry to use large data, one to use intensively, and two wise to use. The latter refers to the use of data mining, the insurance industry to find new knowledge, in this regard, the insurance industry is still in the development period.

To further clarify the definition of large data, IBM first perfected the new attributes of large data: veracity (authenticity). IBM Global Business Consulting Services Division business Analysis and Optimization service Greater China general manager Santa said: "Authenticity is an important dimension that companies need to consider, and will enable them to use data fusion and advanced mathematical methods to further improve the quality of data, thereby creating higher value." ”

IBM has made five key recommendations for enterprise use of large data, with a view to encouraging companies to start big data: customer-centric initiatives, large data blueprints for the entire enterprise, starting with existing data, achieving near-term goals, and progressively building analytical capabilities based on business priorities Analysis of business investment returns based on measurable metrics.

Enabling "Intelligent Application"

"Since 2010, I have built a data mining system under the auspices of Sunshine Insurance Group, which is the first in the insurance industry," he said. Using this system, we have launched a number of insurance data intelligent applications projects, has achieved some results, and the development of the domestic insurance industry, the first batch of data Mining division. "Hongtao is the first batch of large data, he is the most important to the big data" wisdom use ":" No big Data "intensive use", large data "intelligent use" has no data base. At present, the insurance industry large data in the "intensive use", if there is no "intelligent use" to guide, its side effects are very large. ”

He cited a use of the data is far from the results of an example: "Taobao has a freight insurance, that is, Taobao buyers returned when the return of the freight is originally borne by the buyer, if the buyer purchased the freight insurance, return freight by the insurance company to bear." The result of this purchase is that the operating loss of the insurance company is very serious, which directly leads to their reluctance to further develop and expand the freight insurance. "Is freight insurance really an inevitable loss?" Hongtao's answer is no. He gives a way to avoid this dilemma: "I've done a big data-savvy solution for freight insurance." Because the probability of the return, with the buyer's habits, the habit of the seller, the variety of goods, the value of goods, Taobao promotional activities have a relationship, so, using the above data, the use of data mining methods, the establishment of the probability model of return, the implant system can be in each transaction occurs, give a different rate of insurance , so that the collection of insurance premiums, and the probability of the return occurrence, so the freight insurance will not be lost. On this basis, insurance companies can expand customer coverage through freight insurance. ”

From serious losses to cost control and access to customers, by the analysis, mining the value of large data provided to attract customers. This is with IBM's large data value system advocated by "great minds".

"When it comes to big data, IBM focuses on analyzing customer intelligence," Hu Shizhong said. IBM considers how to participate, including how to make full use of IBM's product consulting, service software, including servers, hardware, and provide end-to-end solutions around "intelligent analytics insights." "Analytical capabilities include both skills and tools, and IBM's Big Data strategy promotes large data levels for businesses from these two dimensions."

IBM software Group Greater China Middleware Group general manager Li Hong said: "In the past it is the existing enterprise data collection, collation, management, the formation of the old core application system." While these core applications still dominate enterprise IT, how can the past assets play a new role, from the original pure structured data applications to the dynamic, diversified use of data, and from the original only use of internal data to comprehensive utilization of internal and external data? This means it has to enter a new era, gain more data resources and tap into its value. Fortunately, technology makes these possible. ”

What is the core End-to-end solution with "Intelligent analytical insight"? Li Hong The IBM Big Data strategy: a fully integrated architecture from two tiers: a platform for large data. The second is the analysis of large data. "The two are complementary and integral, and they are not organically combined, and what we feel is an isolated point in the big data field, not a form of system." ”

corresponding to the overall structure diagram, Li Hong points out that at every level of the enterprise's large data, including infrastructure, analysis, internal control, and decision-making, IBM provides solutions, and these solutions are technically seamless and integrate IBM consulting, service and hardware and software capabilities.

Zhou also points out that large data is not a fragment, nor is it simply a skill, but a synthesis, extending from the conceptual level to technology, science and management. There is no such thing as a universal key to solving problems without large data combined with in-depth analysis.

The market for big data is on the eve of the outbreak. According to IBM's understanding, the big data market is roughly divided into four phases: first, education, we need to make the importance of large data and must be fully recognized; the second is to explore, mainly to explore how to use large data, three is contact, from the practical point of view how to apply large data; Santa is confident that the large data product services provided by IBM will accelerate the implementation phase of the big data market.

In China, "big data gives a big opportunity for China to catch up with developed countries such as Britain and the United States faster." We have always said that Chinese innovation is mainly small-scale local innovation, but in large data areas, China has a good opportunity to achieve deep innovation, large-scale cross-echo data applications. "Zhou wants Chinese companies to seize the opportunity.

Beijing Advanced Digital Information Technology Co., Ltd. focused on the field of financial information, the company's president Kwan very "lucky to choose a large data field." "There is a growing demand for large data from the banking sector, which alone cannot meet demand in all its aspects, so that advanced digital communication has contributed to the widespread use of large numbers in the banks, supported by IBM's overall strength". As a partner, he said, advanced digital communication will be consistent with IBM, based on the understanding of large data in the banking industry, in the construction of data use environment, financial management analysis to provide excellent solutions.

Provide a model for implementation

How much does big data intelligence work? Only the real application can give the answer, also has the formidable persuasive power. In the actual case, I choose the "Ascension Degree" index to evaluate, better reflect the value of large insurance data wisdom application. (ascension = The result of using large data wisdom/not using large data intelligence). "Hongtao that the application scenario allows the enterprise to gain a intuitive sense of the value of the large data, taking the insurance industry as an example, a typical large data intelligence application scenario for customer segmentation, agent selection, marketing response, Cross-selling and two sales, fraud monitoring, loss warning, customer retention, etc.

In the process of communicating with customers, Santa also deeply felt the demand: "Almost every customer is asking, how can I use large data?" The establishment of the application scenario requires the enterprise to start from the enterprise strategy, and carefully consider what effect the large data can have on the operation of the enterprise. ”

IBM believes in the power of example, dedicated to providing a wide range of practical references to the industry, helping its IT departments and business departments to learn more about the applications of large data technologies in different industries, and to explore the business value that large data will bring to their businesses. This time, IBM focused on sharing five business needs and corresponding large data landing practice, for enterprises in the implementation of the rule-based, in the case of similar scenarios can be estimated revenue.

The first scenario is to use large data to explore the fulfillment of the information base. Customer service, insurance, automobiles, maintenance, medicine and other industries need to reserve a large scale of the knowledge base, and the vast complexity of the answer manual and knowledge system will result in repeated queries, resulting in system delays and cost increases. IBM Infosphere Data Explore enables technicians, support personnel, and engineers from a global aviation manufacturer to instantly view information in different applications through a single access point. During the first year of deployment, the company's 24x7 call time was shortened from the past 50 minutes to 15 minutes, saving 36 million dollars a year.

The second scenario enables 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, employees of a multinational FMCG manufacturer can more effectively search for the most relevant information, speed up the decision-making process and reduce duplication of effort.

The third scenario is to achieve operational optimization using operational 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 mobile operator Ufone has deployed IBM's large data solution to effectively reduce 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 fourth scenario is to enhance it efficiency and scale efficiency with data warehouse expansion. Enterprises need to enhance the existing data warehouse infrastructure, to achieve large data transmission, low latency, and query requirements to ensure effective use of predictive analysis and business intelligence to achieve performance and scalability. A car manufacturer uses IBM Infosphere biginsights to enhance its existing data warehouses for rapid deployment and easier management.

The fifth scenario provides crime prevention with security and intelligent extension. The government, insurance and other industries need to use large data technology to supplement and strengthen the traditional security solutions. The Secret intelligence and Surveillance sensor system supplier Terraechos, by deploying IBM Infosphere Streams, is able to analyze and classify streaming acoustic data in real time and to reduce the time it takes to capture and analyze 275MB acoustic data from hours to one-fourteenth seconds, At the same time significantly improve monitoring accuracy.