How big data contributes great value

Yan Xuelin, General Manager of JMP Greater China

The heat of big data

In September in Pudong New Area, the weather was slightly cooler. In a high-end office building surrounded by glass in Zhangjiang High-tech Park, early in the morning, the senior staff of the marketing department, Miss Li, was warming up before starting a day's work in her seat: pouring a glass of water and spending a quarter of an hour to a half Browse the highlights of the day on the big website. In the few forums she visits most often, advertisements for cars, clothing, or travel promotions are usually unsolicited; recently, she has noticed some inadvertent changes in this situation that has not changed for many years, such as the window that just jumped out. : How does big data analysis help you target high-value customers "big data" and the travel forum? She was puzzled.

What she didn't know is that the keywords such as "customer", "promotion", and "address book" that she once entered in the browser search interface have been automatically obtained by the crawler attached to IE and have been associated IE plug-ins, the final advertising push is completed by the IE plugin. In a computer room somewhere in the suburbs of Beijing, analysis software based on statistical analysis methods such as "association rules" and "clustering" constantly processes data generated by thousands of Miss Li.

What she may not have noticed is that big data and analytics are becoming hot concepts around the world. First, a few years ago, Google ’s chief economist declared that data analysts are becoming the sexiest profession in the 21st century; and on March 29, 2012, the Obama administration announced the “Big Data Research and Development IniTIaTIve”, White House The Science and Technology Policy Committee (OSTP) has also established a big data high-level steering group to promote this strategic plan. Then there is the matter of this year. Everyone on earth knows that 2013 is the "International Statistical Year".

A few months ago, Professor Michael Rappa, an old friend of JMP, heard in the US House of Representatives, the topic is "Next GeneraTIon CompuTIng and Big Data Analytics"-the next generation of computing and big data analysis. Professor Rappa, as the founder of IAA (Institute of Advanced Analytics, NCSU, North Carolina State University), was appointed as the academic co-chair by the newly created Big Data Committee of the American Science Foundation last year. The Obama administration ’s big data R & D program funds come from the American Science Foundation.

The domestic academic community has benefited from the continuous and unremitting efforts of the former executive vice president of Renmin University of China, Mr. Yuan Wei, and other well-known professors and scholars in the statistical field. Mathematics, physics, chemistry, computer and other university level "first-level subjects".

Data analysis methods with statistics as the core are showing increasing value in the fields of academia, industry and commerce and government. The change on Miss Li's computer is a small sign of this trend.

Gartner recently released the 2013 Magic Quadrant for Business Intelligence and Analysis. The report clearly states that big data and analysis are becoming the core of enterprise IT planning.

All phenomena are telling people that a new era of technology seems to be coming. Some IT professionals are even excited to think that the "third scientific and technological revolution in human history" is coming.

Big data puzzle

The question is, what is big data? Why does everyone say big data?

"Big big data" is big data. The definition of "big" is constantly refreshing. Ten years ago, 1GB of data was already huge. Today, 1000GB is not too big.

The problem is not big, but value. No matter how big the "big data" is, it's still just data. Without enough effective analysis and application, all data is garbage. New York Times columnist David Brooks believes that the lack of effective analysis is the biggest problem of big data: more and more data brings more and more correlations; in fact, many correlations are meaningless. The nature of data association will lead data managers and users astray, wasting a lot of manpower and material resources to manage and analyze these data.

In addition to the traditional data forms that people think are rows and columns with values ​​or text, IT technology also helps people to collect more and more other types of information, such as videos, voices, pictures, documents, etc. These are called "unstructured data".

Structured and unstructured data are multiplying every day. Taking road video surveillance as an example, there are more than 100,000 cameras in Shanghai, and pictures and videos are recorded every moment. Once a case or event occurs, the data recorded in the hard disk library becomes important evidence in the reconnaissance and trial. Although the technology is not yet supported, the industry still expects to find a specific figure or face in TB and even PB-level video data in the future. This type of search / analysis technology will be the engine that launches video-like big data applications in the future.

Similarly, analysis and data mining based on voice, photos or text can also bring a revolutionary breakthrough to human understanding of data. The problem is that this type of technology is still at the laboratory stage.

Although there are not enough applications, big data is still unstoppable. The big data seems to be falling behind without big data. The days of big data are coming. As for whether this trend will evolve into a bubble like .com, or the third industrial revolution, in the eyes of Mr. Warwick, it is no longer important. Industry, database / storage and other vendors are of course happy to see their success, and enterprise IT managers have another excuse to apply for budgets.

Value of data and enterprise data strategy

Data acquisition and storage are still the infrastructure of IT construction. Once it was decided to start the "big data strategy", the continuous occupation of resources made this work black hole. How to avoid this big data black hole? Combining the success stories of major global industry leaders and some small and strong European companies, I believe that the application (analysis and business decision-making) should be the center to establish the corresponding data strategy, and the corresponding data collection and management data should be established accordingly. A complete set of processes to the final business decision. Rather than data for data-First, we must establish an application-centric data strategy. When it comes to applications, almost all industries such as banking, insurance, automotive, and chemical industries are developing various applications based on data analysis. Take some typical customers in the JMP software global industry case base as examples:

E-commerce is analyzing customer purchasing behavior data for promotion and related product recommendations (cross / upsell)

Airlines are investigating passenger feedback to improve air services (customer retention)

Pharmaceutical companies are analyzing clinical trial data to determine the safety and effectiveness of new drugs (development of new products)

Automobile manufacturers are analyzing maintenance information to improve the reliability of complete vehicles and key components to increase customer satisfaction (retain and acquire customers), reduce customer cost of ownership and vehicle manufacturer's warranty costs (reduce costs)

Mobile phone companies are predicting mobile phone sales to rationally schedule production and optimize inventory (operational optimization)

Health management departments are using data models to describe, monitor and predict epidemic trends

Banks are optimizing and improving customer service processes to increase customer satisfaction

Computer manufacturers are using customers to conduct market research on different configuration combinations for pricing

Insurance companies are dynamically adjusting the policy pricing based on the insurance policy to ensure the basic profitability of the product

Semiconductor companies are analyzing / modeling / optimizing the entire manufacturing process data to improve processes and yields, thereby reducing costs and increasing profits

Food companies are using data analysis and market research methods to develop local customers' favorite tastes

The fast food industry is using JMP map analysis tools combined with demographics for store location selection, customer acquisition and supply chain optimization

Only enough effective applications can obtain the value of the data. Only by establishing the importance of data analysis at the strategic level can the company continue to improve. Taking GE as an example, Six Sigma and the corresponding data analysis process have become GE's global strategy and culture. Not only how, GE also continuously promotes continuous improvement based on data analysis. In the field of high-end aero engine R & D and GE energy system business, GE also keeps pace with the times, introducing the highest level of experimental design (DOE) method represented by JMP in the industry to further enhance its R & D level.

Second, everything is inseparable from people. Corresponding to this exponentially increasing demand for data analysis, statistics and analysis talents are becoming scarce and popular in the workplace. In early March, the Wall Street Journal published the "Top Ranking of the Most Popular Occupations in the United States." Data analysis positions ranked second in the list. This is America. For China, it may be ranked higher because of scarcity.

Finally, establish a set of data analysis and decision-making processes to replace the traditional brain-brained decision-making system. This is especially important for Chinese companies. This is not only an effective implementation of the strategy, but also requires companies to show the determination and courage to "change", and reflects the encouragement and tolerance of "change" at the institutional level.

In this era when applications are king, for enterprises, whether to build infrastructure or application software, whether or not to invest, how to invest, is actually an old topic, nothing more than value and price. Big data / cloud computing, no matter how the name changes, the logic remains the same.

About JMP

JMP is an important business unit of SAS, the world's top statistical software group. It is committed to helping global corporate customers improve quality management, optimize business processes and improve product research and development, and help teachers and students and scientific researchers around the world improve the teaching of statistics-related courses. Quality and effectiveness of scientific research.

JMP software is a well-known high-end desktop "excellent performance statistical discovery engine", its application areas include business visualization, Six Sigma and continuous improvement (visualization of Six Sigma, quality management, process optimization), experimental design DOE, research and development innovation, exploration and discovery, teaching and Scientific research and other aspects.

JMP has more than 200,000 users to date, and corporate customers include Intel, HP, Dell, IBM, Apple, National Semiconductor, Texas Instruments, HSBC, Bank of America, Royal Bank of Scotland, China Merchants Bank, Toyota Motor, Sany Heavy Industry, Bai , Roche, Novo Nordisk, AstraZeneca, Pfizer Pharmaceuticals, Dow Chemical, BASF, Procter & Gamble, Unilever, Sinopec, Microsoft, Google, etc. University users include Wharton Business School, MIT, Oxford University , Yale University, Stanford University, Fudan University, China Europe International Business School, Northwestern Polytechnical University, etc. Click the link to learn more:

About SAS

SAS is the world's top statistical software manufacturer and the world's largest private software company, founded in 1976. As a master of strategic management for global enterprises, SAS is committed to providing a new generation of business intelligence software and services to help customers achieve true business intelligence. SAS industry solutions have been used in more than 40,000 companies worldwide, including more than 94% of the global Fortune 500 companies. Click the link to learn more: http: //

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