Competitive Intelligence Changes in Big Data Era Based on Literature Analysis Meng-ru LI 1,a,*, Ruo-dan SUN 2,b, Hong FU 3,c and Shi-tian SHEN 4,d

Transcription

1 rd International Conference on Economics and Management (ICEM 2016) ISBN: Competitive Intelligence Changes in Big Data Era Based on Literature Analysis Meng-ru LI 1,a,*, Ruo-dan SUN 2,b, Hong FU 3,c and Shi-tian SHEN 4,d 1,2,3 Beijing Institute of Science and Technology Information, Beijing, China 4 Department of Computer Science, NCSU, Raleigh, NC, USA a mengmenglee2011@yeah.net, b sunruodan_0319@163.com, c fuhong2011@sina.com, d sshen@ncsu.edu *Corresponding author Keyword: Big Data, Competitive Intelligence, Informatics, Knowledge Discovery, Literature Analysis. Abstract. The coming of big data has brought opportunities and challenges to competitive intelligence. This paper sums up the research status of competitive intelligence in big data era on the basis of existing research and practice, analyzes the innovation and development of intelligence service, big data applications in the field of intelligence, growing trend of big data, opportunities and challenges that brings to information industry through the method of literature analysis. At last, it puts forward the impact of big data on intelligence research. 1. Competitive Intelligence in Big Data Era Intelligence research is the process of extracting valuable information from bulk data and information, and providing related solutions for decision-making, i.e., it s the process of data processing, organization and interpretation, to reveal the potential knowledge, and then turn into usable information. Innovation oriented intelligence analysis is an important subject in area of intelligence and strategic management, and owns a lot of research results. It is the key value in intelligence activities to transform information to intelligence. However, in the face of big data environment with accumulated information socialization, traditional intelligence analysis based on the experience of simple statistics can no longer abstract semantic content and associated knowledge form vast open source information, which largely affected the depth of the intelligence analysis and efficiency. The only effective way to improve intelligence service under the big data environment is to fuse processing theory and method of knowledge, and realize the intelligent of the information analysis. 2. Big Data Research Status in Intelligence System Since 2011, there are a large number of stories and analysis about the big data on domestic leading and local newspapers, meanwhile, domestic academic research on big data is carried out in different areas. The author collects 1667 papers with key words of big data and intelligence through CNKI, on March 31, There are many main achievements in the fields of big data and intelligence, like opportunities, and challenges of competitive intelligence; intelligence analysis and intelligence technology in view of big data; etc. Domestic intelligence agencies have held academic meetings in order to discuss big data together. Such as: 2012 National Defense Science and Technology Intelligence Research Under the Background of Big Data Seminar by National Institute of Defense Technology; Opportunity, Challenge and Responsibility in Era of Big Data Seminar by Institute Of Economics, Tsinghua University and Ali Research; Integration, Innovation, Beyond Development of Digital Library Academic Conference by Library Society of China; Big Data Sharing and Open Technology by IT China; Library Innovative Service in Big Data Era by Founder Apabi Technology Limited; 2014 Competitive Intelligence Service in Big Data Era [1] by China Society for Scientific and Technical Information; etc.

2 2.1 Opportunities and challenges to intelligence industry There is a coexistence of opportunities and challenges for intelligence industry. Liu Gaoyong [2] holds the view that by using big data, the source are more valuable, the status of competitive intelligence can be improved, the analysis ability can be enhanced, and the work cost can be reduced. Under the environment of big data, competitive intelligence is also facing the challenges of cognition of big data, data processing ability of the competitive intelligence system, the competitive intelligence system organization mode, etc. Wu Jinhong [3] thinks big data brings opportunities to competitive intelligence, including comprehensive data, socialized data, real-time data, which help to improve the integrity, precision and real-time performance of the competitive intelligence. While the challenges posed by big data are the problems of information storage, intelligence analysis, information security and talent shortage. Gu Tao [4] insists that there are many problems in the application of big data technology, like high cost of construction and operation, difficulty of using unstructured data, and talents shortage problem. Huang Xiaobin [5] deems big data put forwards some new problems to competitive intelligence work, such as data volume, data range, data security etc. Liu Hongxia [6] thinks competitive intelligence is facing a brand new challenge of research environment, research object, research method and research skill etc. While Zhao Xuyao [7] considers traditional competitive intelligence will face much more complex data network, diverse data analysis method, precision of user needs and targeted results. And he analyzes the challenge and opportunity of competitive intelligence under big data environment, by studying the development situation of big data in various fields, especially in the field of knowledge. 2.2 The innovation and development of intelligence services in big data era Big data can bring significant change to the whole society, and it will also inject new vitality to the development of intelligence services. Li Guangjian and Yang Lin [8] summarizes the development trend of intelligence research as follows: all-fields intelligence research, comprehensive utilization of multiple data sources, analysis of new information resources. Huang Xiaobin [5] generalizes the future direction of competitive intelligence research, including data integration, data filtering, data mining technology, data visualization and analysis, data analytical techniques as well as application. He Defang [9] comes up with the suggestions of competitive intelligence development combined with big data cases like WorldCat, CAUS, NSTL. Zhang Canying [10] indicates that intelligence agencies of science and technology literature need to build a new scientific paradigm to meet the needs of intensive data. Wang Cuibo [11] suggests that dynamic technology for big data analysis of competitive intelligence should be strengthened, semantic-oriented multi-dimensional competitive intelligence analysis model should be built, and technical analysis model should be established based on the crowdsourcing model. Wang Qing [12] proposes competitive intelligence model should be built in base of Big Data Analysis (BDA), and will focus on dynamic competitive intelligence based on mobile Internet. Hua Bolin [13] states that the core role of data information in the whole analysis chain should be emphasized, including the process of intelligence requirement definitions and plans, information retrieval and data collection, multi-source information fusion and cleaning, information content analysis and mining, results interpretation and information extraction, report writing and information transmission. 2.3 Big data application in intelligence domain There are few references about big data application in intelligence domain. Zhang Chunlei [14] has done the research on the critical techniques about BDA and the related applications, including multiple source information integration, relation analysis. He indicates that the research on the new information generated from BDA needs to be done, such as mobile information, cloud information, socialized information, big data etc. Li Guangjian and Hua Bolin [15] have discussed about the commonalities and difference between big data analysis and information analysis. Particularly, commonalities imply the advantages of big data analysis as well as reinforcement of information analysis while difference verifies the specialty of information analysis. Such uniqueness, integrated

3 the idea from big data, can enlarge the advantage of information analysis and provide a direction for research work though the information analysis theory and methodology under the differentiating strategy, as well as avoiding the situation that the generalization of big data appears because of the over evaluation of impact of big data. Li Guangjian and Yang Lin [8] divide the techniques of information science in big data era into three categories: Visualization analysis, Data mining and Semantic process. Qin Xiaozhu et al [16] indicate that the application of big data knowledge service is able to solve four critical problems including represent ability, practicability, integrality and reliability, as well as to provide conceptual pattern of knowledge service for big data. Guo Zikuan et al [17] state that researchers should focus on constructing the new ecosystem for big data management currently and in the future, and supplying the comprehensive solution for applying ecosystem in library. Huang Xiaobin [5] et al show that data analysis becomes the new support of improving companies' profit. Particularly, Analysis and prediction for the future trend is a good way to promote companies' competitiveness. The strategically high view can be obtained by integrating analyzing methodologies and techniques with information science and generating the new analyzing methods for fitting the particular company. Hua Bolin [13] shows that the applied information science has the widely used application and development space under the cumulative foundation. The new domain contains public management domain, business area, health care domain, environmental protection etc. The development of big data techniques brings qualitative leap in these domains. 2.4 Development trend of big data application In the coming future, big data analysis tools and applications will be the research hotspots in intelligence science and other related domains and the new research achievement and challenge will involve, which contains several critical techniques including data mining, DFS, visualization etc. However, the related techniques and tools in intelligence science domain are limited. There is no doubt that big data techniques provide the new way for information institute to change the knowledge service pattern, to modify the business including the knowledge management pattern, the cooperating and interactive pattern, dynamically supervising knowledge service flow etc. We can predict that real time access, accurate analysis, deep mining from massive, complicated, multiple structural data can be applied in the real world. The big data analysis will follow the commercial pattern, while the core infrastructure will be applied for the service and application. Data analysis and data visualization, as the regular service from original data set, allow users to construct their own pattern. Big data techniques, as the important component in intelligence science domain following the new development, will generate large modification of service concept, method, and management for information institute. However, currently the research and application related to big data techniques is in the preliminary stage. Information institute should not only import and integrate new techniques, collect and process data, train and manage professional workers, but also make positive response and modification for risk management, infrastructure and development strategy. 3. The Influence of Big Data on Intelligence Research In big data era, data as one of components for intelligence, has been increasingly important. Decision making has been generated by data analysis instead of prior knowledge, experience or intuitions. The development of information science, extended from data analysis and signal processing, has encountered the novel possibilities and challenge from the new intelligence pattern. 3.1 Comprehensive data, improving the reliability of intelligence From the prospect of data source, big data contains three major types, including transaction data, interactive data and perception data. Specifically, transaction data, stored in SQL database, is the business data from the ERP, SCM, CRM and Web transaction system of companies. Interactive data is mainly generated from social network, such as Weibo, Facebook, Twitter, Web blog, Clickstream data, etc. Perception data, mainly collected from Internet of Things, such as sensors, RFID,

4 GPS etc, indicates the awareness of surroundings in the real world. The data which is generated from various data source denotes the different aspects of competitors, environment and the sources, and provides sufficient information for apperceiving the trend of competition among companies and hot spots, the weakness and strength of companies. In addition, big data is not only collected simply in quantity, but also mutual connected structurally. Because of the connection among users and behaviors and that between users and their behaviors, data contains extremely high value in analysis and mining. 3.2 Socialized data, increasing the accuracy of intelligence One of significant trends in big data era is the data socialization, which can be observed in the related records from networks, such as blog, game community, Weibo, internet, mobile network and Internet of Things etc. Data socialization facilitates companies to be close to customers and to observe their behaviors, and supplies opportunities for workers to collect first-hand information related to users, which can derive accurate and predictive knowledge for competition. The new social applications such as Twitter, Facebook, Weibo etc generate socialized data, which improve the reliability and accuracy of intelligence. Analyzing socialized data and understanding the trends and points from unstructured contents facilitates workers to immediately capture the change of customers' emotion and trend of market, which help companies to make marketing strategy actively. 3.3 Real-time series data, enhancing the applicability of intelligence Currently the center of gravity of internet has gradually shift to mobile network. Statistical report on the development of the Internet in China shows that netizens in China has reached 6.88 billion until 2015, the penetration rate in 2015 is 50.3 percent, which increases 2.4 percent over 2014, the number of mobile internet users in 2015 is 6.30 billion, which increases about 63 million over [18] Surfing internet though mobile has been a working or studying habit for users. The companies with the increasing number create Weibo accounts, releasing the information about products on time, the notice related to personnel change, while general users post their comments and opinions related to the particular events or news. So far the information generated from internet equipments such as smart phones, tablet computer, camera with internet service etc, take more than 33.3 percent of the whole internet information. The good application of real time series data can promote the applicability of competitive intelligence. 3.4 Widely used application of big data, promoting the role of intelligence In big data era, data can be generated in every domain and applications and gradually become one of important productive factors. The bid data application plays the fundamental role in the future competition. The report in IBM indicates that 58 percent of companies has implemented data analysis techniques into market, obtained advantages for competition and achieved the business worth, which increases 21 percent comparing with that of previous years. The modification of decision making pattern implies the high value of intelligence, which will be re-evaluated though intelligence work. Acknowledgement This research work was supported by National Social Science Foundation of China Under Grant No. 12CTQ046. Research on Formation Mechanism and Organizational Models of Knowledge-based Network Micro Information References [1] 20th China Competitive Intelligence International annual conference paper collection. SCIC [2] Liu Gaoyong, Wang Huiling, Wujinghong. Analysis of Competitive Intelligence in the Era of Big Data. Document, Information & Knowledge (2):

5 [3] Wu Jinhong, Zhang Fei, Ju Xiufang. Big Data: Opportunities, Challenges and Strategies of Enterprise Competitive Intelligence. Journal of Intelligence, 2013, 32 (1): 5-9. [4] Gu Tao. Research on Collaboration Analysis of Competitive Intelligence Based on Big Data. Information Science. 2013, 31 (12): [5] Huang Xiaobin, Zhong Huixin. On the Innovation and Development of Enterprises Competitive Intelligence Analysis in Big data Era. Library and Information (6): [6] Liu Hongxia, Bai Wanhao. Applied Information Science Research under the Background of Big Data. Information and Documentation Services (1): [7] Zhao Xuyao. Development Trend of Competitive Intelligence in Cloud Era. Information Studies: Theory & Application (6): [8] Li Guangjian, Yang Lin. Intelligence Analysis and Intelligence Technology in View of Big Data. Library & Information, 2012 (6): 1-8. [9] He Defang. The Development Trends of Informatics under Big Data and Cloud Service. Digital Library Forum (11): 2-9. [10] Zhang Canying, Liu Dehong. CTI Service Model Research for Enterprise Open Technological Innovation. Journal of Intelligence, 2012, 31 (7): [11] Wang Cuibo, Wu Jinhong. Competitive Technical Intelligence Analysis in the Big Data Era: Challenges and Responses. Journal of Intelligence, (3): [12] Wang Qing. Research on the Opportunities, Challenges and Strategies of Enterprise Competitive Intelligence System in the Era of Big Data. Journal of Tianjin College of Commerce (4): [13] Hua Bolin, Li Guangjian. Research on Multi-source Information Fusion Competitive Intelligence under the Background of Big Data. Information Studies: Theory & Application. 2015, 38 (4): 1-5. [14] Zhang Chun lei, Yang Xiao niu. Big Data Analytics and its Application in the Field of Intelligence. Journal of China Academy of Electronics and Information Technology. 2013, 8 (1): [15] Li Guangjian, Hua Bolin. Relationship between Big Data Analysis and Intelligence Analysis. Journal of Library Science in China. 2014, 40 (213): [16] Qin Xiaozhu, Li Chenhui, Mai Fanjin. An Investigation of the Connotation, Typical Characteristics and Conceptual Model of the Big Data Knowledge Service. Information and Documentation Services (2): [17] Guo Zikuan, Zhang Xingwang, Mai Fanjin. Research on the Big Data Ecosystem Application in Libraries. Information and Documentation Services (2): [18] Statistic Report of China Internet Network Development. China Internet Network Information Center