Research on the optimization of knowledge management system for high-tech enterprises in big data environment
Research on the optimization of knowledge management system for high-tech enterprises in big data environment
Zhao Zhongweia Yu Yana Wang Pinga
a Harbin Engineering University,School of Economics & Management, Harbin Nantong Avenue 145, China
a email:zzw961@163.com
Abstract: Based on the influence analyses of big data to enterprise knowledge management,the optimization idea of knowledge management system for high-tech enterprises in big data environment is put forward.The knowledge management system is optimized by infrastructure system and process management system including knowledge discovery,knowledge integration and accumulation, knowledge learn and application, knowledge transformation and sharing,knowledge innovation from technical perspective, organization system, culture system and institutional system in management perspective,so as to realize the purpose of fast and efficient knowledge management in the big data era.
Keywords: Big Data; High-tech Enterprise; Knowledge Management System; Optimization
0 introduction
Into the era of knowledge economy,access to knowledge and management capabilities have gradually become the fundamental foothold of enterprises.High-tech enterprises,as a kind of typical enterprise strongly supported by our country,possess the characteristics of knowledge-intensive and talent-intensive,making knowledge management become their core competitiveness.However,in the new environment,the defects of knowledge management in high-tech enterprises are also increasingly prominent.The development of big data also requires us to rethink and expand the enterprise knowledge management system.This paper attempts to alleviate the high-tech enterprises knowledge discovery and integration is difficult,the low efficiency of knowledge innovation,combined with the background of big data era and the characteristics of high-tech enterprises,high-tech enterprise knowledge management system to optimize the overall framework of the study so that the fine and accurate so as to guide the activities of knowledge discovery,accumulation,application,sharing and innovation.
1 The Impact of Big Data on Enterprise Knowledge Management
Big data is a complex data set that can be transmitted freely and quickly.After Douglas Laney(2001) proposed large,diverse and rapidly updated 3V features of big data,Oracle proposed that big data should have the feature dimension of low value density(De Mauro A，Greco M, &Grimmaldi M.2015, p.97-104).At the same time,some scholars think that the data also has online features(Assunção M D，Calheiros R N&Bianchi S.2015, p.3-15).The characteristics of big data prompted enterprises to transform from data management to data-driven,thus affecting the method and platform of enterprise knowledge management(Figure 1),which are embodied in the following aspects:
Figure.1 big data on the impact of enterprise knowledge management
1.Diversified knowledge carrier
Big data provides a rich source of knowledge for knowledge management. More than 1.8 billion users are using the Internet every day, and various electronic terminals generate and record data non-stop. In short, the knowledge base in the context of big data has been greatly expanded, and the data types and data presentation methods have also become diversified.
2.Knowledge mining deepening
The emergence of big data has spawned a knowledge discovery method based on big data analysis technology. At present, the big data technology that IBM is researching can form new data based on the indefinite mining analysis, and it is estimated that in the future, the simulation of human brain, quantum computation and the like will be performed Mining, cloud computing as intelligent, to further enhance the data reception, processing accuracy, depth and speed. At the same time, big data technology can dig deeper into the tacit knowledge behind the data and thus promote the transformation from tacit knowledge to explicit knowledge.
3.Knowledge Storage Virtualization
On the one hand, enterprises can store data and knowledge resources in the virtualized storage pool. The storage pool can automatically monitor and update automatically and patch at the same time, so that calls can be retrieved at any time(Zeng, Zhao, and Shang,2011, pp.234-239). On the other hand, in the era of big data, some of the memory can be given to tools. This is a shift in the way people think and work because of big data.
4.Application decision-making precision
Decision-making is the end result of people’s application of knowledge. First, the advent of big data allows people to trust more convincing data than feelings and experiences in their judgment. Second, the complexities the brain can not understand, the data can help us deal with it, as long as it allows us to know more than before, can guide us to what to do next, as to why it seems less important(Zheng, 2013, pp.37-41). Finally, the highest expectations of big data lie in its predictive power because decisions are themselves future-proof. The key to predicting system success is that they are based on big data, and the more data, the more accurate the forecast is.
5.Knowledge sharing intelligent
Only knowledge in the process of communication and delivery can add value and innovation. Under the new age background, the main body of sharing is not only human, but also machines. Machines can understand, automate and share data, information and knowledge. Computer networks can effectively connect people, people and machines, machines and machines, people Machine interaction can break the barrier of knowledge flow inside and outside the enterprise so as to maximize the value of data and knowledge, reduce the waste of resources and further realize the collaborative innovation and development.
2Knowledge Management System Optimization of High – tech Enterprises in the Background of Big Data
To optimize the knowledge management system of high-tech enterprises under the background of big data is to reshape the entire knowledge management system with new knowledge management concepts and big data technologies. The basic idea is as follows:
First of all, we should design a knowledge management system around the process of knowledge management, focusing on two aspects. One is to pay attention to the management of explicit knowledge, systematize and systematize the non-systematic explicit knowledge so that more employees can acquire knowledge and make use of it. Second, pay attention to the in-depth mining of tacit knowledge, dig out the hidden rules behind the data, and realize the explicitness of tacit knowledge so that employees can retrieve and invoke them at any time. At the same time, pay attention to knowledge exchange and creation caused by tacit knowledge, Means to create a good atmosphere of knowledge management.
Second, to ensure the availability of knowledge. Employees are the users and beneficiaries of the knowledge management system. Enterprises should ensure that the supply of knowledge matches the needs of employees. To ensure the quality of data and knowledge, employees should be provided with useful knowledge rather than just massive knowledge.
Finally, to improve the operational efficiency of the system. High-tech knowledge itself has strong timeliness, from identifying and processing data to generating knowledge, the efficiency of all aspects of which needs to be further improved, and enterprises should provide powerful software and hardware facilities for knowledge management to meet the market competitive environment data and Dynamic needs of knowledge.
3The Overall Framework Design of High – tech Enterprise Knowledge Management System in the Background of Big Data
Based on the above optimization design ideas, this paper divided the high-tech enterprise knowledge management system under big data into five parts: process management system, infrastructure system, knowledge management organization system, knowledge management culture system, knowledge management system (Figure 2 ).
4Optimized Design of Knowledge Management System by Technical Means
The process management system and the infrastructure system constitute the software system and hardware system in the enterprise knowledge management system. Process management system is the core of the whole system, emphasizing the various technologies supported by the application software. Infrastructure system emphasizes the hardware system, also includes the supporting IT technology.
4.1Process Management System
4.1.1Knowledge discovery system
Knowledge discovery is the core of the process management system. Enterprises should establish a knowledge discovery system based on massive data resources. This system is a bridge between data warehouse and knowledge base, devoting itself to finding out the rules between data and realizing the transformation from data flow to knowledge flow. The content of knowledge discovery system should include:
1.data preparation
Today’s big data exists in a network rather than in a traditional database(Tang, 2017, pp.223-224). Enterprises first need to realize the data perception, identification and transmission through sensors, smart identification and other devices, and real-time monitoring. Because the data collected is tangled, fragmented “dirty data,” pre-processing is also required to eliminate the heterogeneity of the data, the so-called ETL process (E decimation, T conversion, L loading). To reduce the data processing time, scientists proposed to T and L swap to ELT, but when the business has not been able to make such a shift, the scientists have proposed ELT to the EL upgrade, the future can even E, L can be merged. After the above treatment of data will be some form of unified loading into the network data warehouse（Figure 3）, data warehouse construction is mainly for front-end query and analysis of the foundation.
Figure 3. Knowledge discovery flow chart of high-tech enterprises under big data background
2.Knowledge mining
After preliminary preparation, the data in the data warehouse has been transformed into a form suitable for data mining. Enterprises should adopt the data mining technology based on cloud computing as the core of the knowledge identification and capture system, extract the hidden patterns and find the data Internal relations, and then complete the process of knowledge externalization. At the same time, the dynamic learning mechanism can be introduced in the process of data mining so that the knowledge mining model can make dynamic adjustment according to the needs of enterprises so as to timely feedback, correct and update the data content.
4.1.2Knowledge integration and accumulation system
Identify and capture knowledge, managers need knowledge approval, sort out the content of knowledge, and complete the storage and maintenance of knowledge.。
1.Develop knowledge structure
Due to the huge amount of knowledge in big data, enterprises should sort out the content of knowledge according to its own characteristics and needs, create a knowledge classification architecture and make a cloud knowledge architecture map.
2.Knowledge storage
Businesses can take advantage of a “hybrid cloud” storage architecture that uses public cloud to coordinate private cloud operations and knowledge storage without having to purchase additional hardware. Enterprises use cloud storage technology based on distributed storage to upload knowledge to the cloud, form a cloud knowledge base system, and classify the cloud knowledge base according to the established knowledge structure, so as to provide more space for knowledge storage and accumulation (Figure 4) .
Figure 4. High-tech enterprise cloud knowledge base construction content in the background of big data
3Knowledge maintenance
4.1.3Knowledge Learning and Application System
The key to moving the knowledge in the cloud knowledge base is “using”, which is achieved through a knowledge portal system. Enterprise Knowledge Portal provides users with a virtual joint work site, the process of user access to knowledge is the process of continuous learning of knowledge, the explicit knowledge into the tacit knowledge in their mind, to achieve the internalization of knowledge, and ultimately to make the most Good decision(Yang, 2005, pp.137-140).
With the advent of big data, knowledge portals should move toward automation, intelligence and personalization. They should break through the limitations of fuzzy inquiries and full-text search in the past, and create an intelligent search engine that supports word segmentation and knowledge querying to achieve millisecond-level Search, and provide users with interactive services, precise positioning of user knowledge needs, according to the user’s application scenarios and usage habits to integrate knowledge content, the formation of “knowledge map”, the appropriate knowledge at the right time to the appropriate user, rather than blindly Showing a dazzling array of knowledge fragments.
| User interface |
| Personalized cloud interface |
| Process support Exchange management Relationship Resource Management Document management User Management Competitive Intelligence Publish the subscription functional module |
| Cloud knowledge base |
Figure 5. High-tech enterprise knowledge learning and application of big data background flow chart
4.1.4Knowledge transfer and sharing system
Practice shows that the transformation and sharing of tacit knowledge in enterprises is just as important as systematic management of explicit knowledge. High-tech knowledge is implicit and often difficult to transfer among different individuals, which not only hinder the accumulation of individual knowledge of employees, but also hinder the teamwork and knowledge innovation. Therefore, high-tech enterprises should establish a special system of knowledge dissemination and sharing, which focuses on the application of knowledge at the organizational level and is the process of realizing knowledge socialization.
In the past, knowledge sharing within the enterprise was achieved through spontaneous and manual operations, which affected the transmission of tacit knowledge, and the transfer of tacit knowledge deepened Knowledge of knowledge, thereby creating more knowledge(Yan, &Bian, 2017, pp.48-52). Therefore, enterprises should embed the knowledge exchange module in the knowledge portal to create a formal online knowledge community. Unlike traditional groupware systems, knowledge communities should have a dedicated leader who is responsible for regularly arranging discussion topics and defining clear community roles for each community member, establishing relationships between individuals, and allowing users access through knowledge access Timely dialogue to capture fresh information, so as to break the barrier of knowledge transfer within and among various departments and realize the rapid sharing of knowledge.
4.1.5Knowledge Innovation System
High-tech knowledge is the core knowledge of high-tech enterprises, but because of the uncertainty of technology and market changes, high-tech knowledge must continue to be rapidly updated. Therefore, knowledge innovation is an important goal of high-tech enterprise knowledge management.
Knowledge innovation is based on the application and sharing of knowledge. First of all, employees should solve the problem with new methods and ideas. The knowledge workers in high-tech enterprises are very good at thinking and strong in practical ability. They often bring their own ideas into learning and application of knowledge so as to promote knowledge innovation(Qi, Li, &Feng, 2015, pp.48-52).Secondly, the knowledge community can always absorb some new ideas and suggestions, identify potential practices through expert argumentation, and thus the knowledge community becomes the place where knowledge is created.Finally, application software built on the knowledge cloud platform also generates multivariate data when it is used, and the data is returned to the data warehouse for knowledge discovery again to gain new knowledge. Thus, the five sub-systems of the process management system do not exist independently. The interconnection of the five parts of the process management system provides a closed-loop system for the production and utilization of the data cycle, resulting in a steady flow of data and knowledge.
4.2Infrastructure system
Internet/Intranet、PC machine、server and so on provide the hardware support platform for the operation of knowledge management process system, which together constitute the knowledge management infrastructure system.
First of all, knowledge management in the 21st century can only survive if it relies on the network environment. Enterprises should proactively contact the Internet with business and management to promote Internet + construction so as to establish and cover systems for data acquisition and storage, knowledge mining and delivery at low cost All aspects of production and operation of enterprises, and ultimately the establishment of large-scale closed-loop internal and external data.
Second, the process management of enterprise knowledge is realized through software such as data warehouse, knowledge base and knowledge portal. These softwares need to be built on a basic hardware underlying platform that allows access to various types of data resources. Unlike the upper level applications, the cost of replacing the traditional infrastructure is high, and customers are sticky, often without replacement. Nowadays, because of the frequent data interactions and the need for more powerful hardware infrastructures to support these massive amounts of data, enterprises need to build very large cloud-based servers. Cloud server can improve the computer’s computing, storage and data processing capabilities to help companies build stable and secure upper-layer applications, but also effectively reduce the difficulty of development and operation and IT costs and improve the service cost of the host(Liu, 2013, pp.70-73).
5Optimized Design of Knowledge Management System by Means of Management
Knowledge management is a long-term work to promote the transformation of enterprises to knowledge-oriented.Knowledge can create value through “management.” The process management system and infrastructure system focus on the technical aspects. Knowledge management organizations, culture and institutional systems focus on management and policy. “Management” refers to all aspects of the enterprise, and its significance lies in creating conditions for knowledge discovery, accumulation, application, sharing and innovation .
5.1Knowledge Management Organization System
An effective knowledge management system is rooted in the organizational system in terms of function, system and strategy. The knowledge of high-tech enterprises is very difficult to transfer because of their professional complexity, resulting in the need of coordinating knowledge. The knowledge management organization system Due to transport. In high-tech enterprises, work is focused on technologies and products. Knowledge management is more task-oriented or project-oriented. Therefore, enterprises should be equipped with appropriate management organizations around the knowledge management project.
1.Knowledge Management Leadership Council
Currently, many information-intensive organizations have the CIO position, which was gradually replaced by the Data Supervisor (CDO) in the Big Data era. The Enterprise Knowledge Management Leadership Committee is the leadership team composed of data executives and department leaders. It is mainly responsible for creating an environment suitable for knowledge management of big data enterprises, formulating a knowledge management strategy that matches the overall strategy of the enterprise, and arranging various supporting software Hardware facilities, and regular strategic adjustments and reconfiguration of resources based on the knowledge management team’s feedback(Yao,2010). Among them, the data supervisor as a leader in knowledge management should have strong IT skills and business management capabilities, is responsible for controlling and supervising knowledge management, so as to meet the strategic goals of knowledge management expectations.
Figure 6.Knowledge Management Organization System of High-tech Enterprises in the Background of Big Data
2.Knowledge Management Team
The knowledge management team is the executive layer of knowledge management and is the principal person in the knowledge management project, providing training and education to other roles in knowledge management. The knowledge management team focuses on a specific area and has a big data technology department that takes advantage of localized needs and resources to understand industry dynamics and test new ways to find suitable applications for enterprise knowledge needs and to conduct day-to-day management and maintenance . Knowledge management team under the Knowledge Management Committee of Experts, knowledge management experts with knowledge management skills and experience, to coordinate the design and operation of knowledge management system.
3.Knowledge Management Promotion Group
5.2Knowledge Management Culture System
As an informal system, corporate culture guides the value concept of an enterprise. In order to ensure that the construction and operation of knowledge management system can achieve the strategic goal, it is necessary for high-tech enterprises to define the cultural system for knowledge management of big data. This article focuses on the process of knowledge management, combined with the requirements of high-tech enterprises for professional skills and talents, the cultural system is divided as follows.
Figure 7. Knowledge Management Culture System of High-tech Enterprises in the Background of Big Data
Big data concept: Big data is a subversive traditional way of thinking and technological change, can change the mode of economic growth, of course, can become the end result of knowledge management, high-tech enterprises should advance with the times, and actively change the concept of knowledge management, establish The concept of big data under the era of “Internet +”, with the help of internal and external forces, digitized the production and operation of enterprises and tapped more commercial value from the data.
Lifelong Learning Culture: Establish the concept of lifelong learning and encourage employees to constantly upgrade their personal values in order to meet the needs of their survival and development. Mobilize their enthusiasm and encourage them to acquire and acquire the necessary knowledge through the use of a knowledge portal.
Innovative culture: Encourage employees to create new knowledge in the process of application and sharing of knowledge. Emphasize group creativity, encourage and respect individual creation, and allow employees to make mistakes. Because of the professionalism and complexity of high-tech knowledge, it is difficult for enterprises to form systematic innovations by relying solely on the individual’s wisdom. Enterprises should not only attach importance to individual freedom and value realization, but also make collective wisdom cohesive (Zhang, 2015).
Efficient culture of excellence: As market competition continues to intensify, high-tech enterprise knowledge management pursues dual standards of effectiveness and efficiency, and pursues higher knowledge turnover rates. Enterprises should incorporate the spirit of pursuing high efficiency and excellence into the knowledge management culture system and urge knowledge quickly Discover, accumulate, apply, deliver and innovate to create the shortest flow of knowledge。
5.3Knowledge Management System
Institutionalization marks the new beginning of knowledge management system, in which enterprises realize the strategic significance of knowledge management system and guarantee the orderly operation of knowledge management system by establishing codes of conduct and management methods [15]. The fundamental purpose of establishing the system of knowledge management system is to realize the full integration of the knowledge management system and the integrated operating mechanism of the organization. Figure 8 lists the main system of knowledge management of big data.
| Organizational Structure Specification |
| Data classification and standardization system |
| Knowledge points and reward system |
| Incremental update mechanism |
| Safety Management Code |
Figure 8. Institutional System of High-tech Enterprise Knowledge Management in the Background of Big Data
Data classification and standardization system: Classification and standardization are the bases of artificial intelligence. The system defines the basic principles that enterprise data and knowledge resource classification should follow and prepares knowledge storage so as to provide standardized knowledge display results.
6Conclusion
This paper selects high-tech enterprises for targeted research, systematically analyzes the inherent impact of big data on enterprise knowledge management, and optimizes the knowledge management system of high-tech enterprises, which is divided into process management system, infrastructure system, knowledge management organization system, Knowledge management culture system, knowledge management system most of the system 5, the optimization content is described. The results of this paper provide a new perspective for the research of knowledge management in high-tech enterprises, and provide the foundation and reference for the research in related fields. It helps high-tech enterprises to explore fast and efficient knowledge management and take enterprises’ knowledge discovery, accumulation, application, sharing and innovation to a new height.
In the future research, the author will conduct corresponding empirical research through scientific investigation methods and analytical tools to enhance the practicality and reliability of the optimization system. In addition, this paper only conceptualizes the overall structure of the high-tech enterprise knowledge management system, does not involve the specific technical functions of the knowledge management system, and has some limitations. These problems will be further explored in future research.
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