• Title/Summary/Keyword: Business Data

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A Business Application of the Business Intelligence and the Big Data Analytics (비즈니스 인텔리전스와 빅데이터 분석의 비즈니스 응용)

  • Lee, Ki-Kwang;Kim, Tae-Hwan
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.42 no.4
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    • pp.84-90
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    • 2019
  • Lately, there have been tremendous shifts in the business technology landscape. Advances in cloud technology and mobile applications have enabled businesses and IT users to interact in entirely new ways. One of the most rapidly growing technologies in this sphere is business intelligence, and associated concepts such as big data and data mining. BI is the collection of systems and products that have been implemented in various business practices, but not the information derived from the systems and products. On the other hand, big data has come to mean various things to different people. When comparing big data vs business intelligence, some people use the term big data when referring to the size of data, while others use the term in reference to specific approaches to analytics. As the volume of data grows, businesses will also ask more questions to better understand the data analytics process. As a result, the analysis team will have to keep up with the rising demands on the infrastructure that supports analytics applications brought by these additional requirements. It's also a good way to ascertain if we have built a valuable analysis system. Thus, Business Intelligence and Big Data technology can be adapted to the business' changing requirements, if they prove to be highly valuable to business environment.

ERP Application Development Using Business Data Dictionary (데이터사전을 이용한 ERP애플리케이션 개발)

  • Minsu Jang;Joo-Chan Sohn;Jong-Myoung Baik
    • The Journal of Society for e-Business Studies
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    • v.7 no.1
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    • pp.141-152
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    • 2002
  • Data dictionary is a collection of meta-data, which describes data produced and consumed while performing business processes. Data dictionary is an essential element for business process standardization and automation, and has a fundamental role in ERP application management and customization. Also, data dictionary facilitates B2B processes by enabling painless integration of business processes between various enterprises. We implemented data dictionary support in SEA+, a component- based scalable ERP system developed in ETRI, and found out that it's a plausible feature of business information system. We discovered that data dictionary promotes semantic, not syntactic, data management, which can make it possible to leverage viability of the tool in the coming age of more meta-data oriented computing world. We envision that business data dictionary is a firm foundation of adapting business knowledge, applications and processes into the semantic web based enterprise infra-structure.

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Data Dependency Graph : A Representation of Data Requirements for Business Process Modeling (데이터 의존성 그래프 : 비즈니스 프로세스 설계를 위한 데이터 요구사항의 표현)

  • Jang, Moo-Kyung
    • Journal of the Korea Safety Management & Science
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    • v.13 no.2
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    • pp.231-241
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    • 2011
  • Business processes are often of long duration, and include internal worker's decision making, which makes business processes to be exposed to many exceptional situations. These properties of business processes makes it difficult to guarantee successful termination of business processes at the design phase. The behavioral properties of business processes mainly depends on the data aspects of business processes. To formalize the data aspect of process modeling, this paper proposes a graph-based model, called Data Dependency Graph (DDG), constructed from dependency relationships specified between business data. The paper also defines a mechanism of describing a set of mapping rules that generates a process model semantically equivalent to a DDG, which is accomplished by allocating data dependencies to component activities.

A Leading Study of Data Lake Platform based on Big Data to support Business Intelligence (Business Intelligence를 지원하기 위한 Big Data 기반 Data Lake 플랫폼의 선행 연구)

  • Lee, Sang-Beom
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2018.01a
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    • pp.31-34
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    • 2018
  • We live in the digital era, and the characteristics of our customers in the digital era are constantly changing. That's why understanding business requirements and converting them to technical requirements is essential, and you have to understand the data model behind the business layout. Moreover, BI(Business Intelligence) is at the crux of revolutionizing enterprise to minimize losses and maximize profits. In this paper, we have described a leading study about the situation of desk-top BI(software product & programming language) in aspect of front-end side and the Data Lake platform based on Big Data by data modeling in aspect of back-end side to support the business intelligence.

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The Data Quality Management Framework and it's Business Scenario (데이터 품질관리 프레임워크와 비즈니스 시나리오)

  • Lee, Chang-Soo;Kim, Sun-Ho
    • The Journal of Society for e-Business Studies
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    • v.15 no.4
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    • pp.79-99
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    • 2010
  • As data exchange between business partners in e-business becomes more active, obtaining and managing reliable data is emerging as a pressing issue for corporations and organizations. For the resolution of data quality, this paper proposes a framework for data quality management with its scenario. The data quality management framework consists of three phases: data quality monitoring, data quality improvement and data application, each of which has three processes. In each process, necessity, functions, roles, and relationships among processes are specified. In order for users to directly apply the framework to the business field, a business scenario is given with examples of product identification and classification code systems widely used in e-business.

A Modeling Approach to Integrate Business Processes and Data Requirements (업무 프로세스와 데이터 요구사항의 통합 모델링)

  • Jang, Mu-Gyeong
    • Proceedings of the Safety Management and Science Conference
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    • 2011.04a
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    • pp.329-338
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    • 2011
  • Business processes are often of long duration, and include internal worker's decision making, which makes business processes to be exposed to many exceptional situations. These properties of business processes makes it difficult to design processes to support uncertainties from internal or external environments. The behavioral properties of business processes mainly depends on the data aspects of business processes. To formalize the data aspect of process modeling, this paper proposes a graph-based model, called Data Dependency Graph (DDG), constructed from dependency relationships specified between business data. The paper also defines a mechanism of describing a set of mapping rules that generates a process model semantically equivalent to a DDG, which is accomplished by allocating data dependencies to component activities.

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A Study on Big Data-Driven Business in the Financial Industry: Focus on the Organization and Process of Using Big Data in Banking Industry (금융산업의 빅데이터 경영 사례에 관한 연구: 은행의 빅데이터 활용 조직 및 프로세스를 중심으로)

  • Gyu-Bae Kim;Yong Cheol Kim;Moon Seop Kim
    • Asia-Pacific Journal of Business
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    • v.15 no.1
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    • pp.131-143
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    • 2024
  • Purpose - The purpose of this study was to analyze cases of big data-driven business in the financial industry, focusing on organizational structure and business processes using big data in banking industry. Design/methodology/approach - This study used a case study approach. To this end, cases of two banks implementing big data-driven business were collected and analyzed. Findings - There are two things in common between the two cases. One is that the central tasks for big data-driven business are performed by a centralized organization. The other is that the role distribution and work collaboration between the headquarters and business departments are well established. On the other hand, there are two differences between the two banks. One marketing campaign is led by the headquarters and the other marketing campaign is led by the business departments. The two banks differ in how they carry out marketing campaigns and how they carry out big data-related tasks. Research implications or Originality - When banks plan and implement big data-driven business, the common aspects of the two banks analyzed through this case study can be fully referenced when creating an organization and process. In addition, it will be necessary to create an organizational structure and work process that best fit the special situation considering the company's environment or capabilities.

The research of Decision Matrix design methodologies for business data protection and protection by data leveling (비즈니스 데이터 보호를 위한 decision matrix 설계 방법론 및 등급별 보호조치 기준 연구)

  • Shin, Dong Hyuk;Choi, Jin-Gu
    • Convergence Security Journal
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    • v.16 no.4
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    • pp.3-15
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    • 2016
  • Business data means data of all the documents and electronically generated on / off-line form, storage, use, and transfer the company work process. Business, organization, sales, marketing, means any data related to shipping. Many companies are investing in privacy. But not so for business data. In most companies, secret, confidential rating already exists, the basis is insufficient to establish that decisions can be analyzed in detail to reflect the actual business data in use. In this paper we want to present the criteria that can offer ways to design your business data decision matrix to establish the qualitative and quantitative criteria (evaluation indicators) that can be classified business data and protected by each class.

The Impact of Business Intelligence on the Relationship Between Big Data Analytics and Financial Performance: An Empirical Study in Egypt

  • Mostafa Zaki, HUSSEIN;Samhi Abdelaty, DIFALLA;Hussein Abdelaal, SALEM
    • The Journal of Asian Finance, Economics and Business
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    • v.10 no.2
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    • pp.15-27
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    • 2023
  • The purpose of this research is to investigate the impact of Business Intelligence (BI) on the relation between Big Data Analytics (BDA) and Financial Performance (FP), at the beginning we reviewed the academic accounting and finance literature to develop the theoretical framework of business intelligence, big data and financial performance in terms of definition, motivations and theories, then we conduct an empirical analysis based on questionnaire-base survey data collected. The researchers identified the study population in the joint-stock companies listed on the Egyptian Stock Exchange and operating in the sectors and activities related to modern technologies in information systems, big data analytics, and business intelligence, in addition to the auditing offices that review the financial reports of these companies, and The sector closest to the research objective is the communications, media, and information technology sector, where the survey list was distributed among the sample companies with (15) lists for each company, and (15) lists for each audit office, so that the total sample becomes (120) individuals (with a response rate 83.3%), The results show, First, Big data analytics significantly affect organizations' financial performance, second, Business intelligence mediates (partial) the relationship between big data analytics and financial performance.

ERP Application Development Using Business Data Dictionary

  • Jang, Min-Su;Sohn, Joo-Chan;Baik, Jong-Myoung
    • Proceedings of the CALSEC Conference
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    • 2001.08a
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    • pp.483-491
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    • 2001
  • Data dictionary is a collection of metadata about data defined, produced and consumed while performing business processes. Data dictionary is an essential element for business process standardization and automation. Data dictionary also has a fundamental role in ERP application management and customization. Finally, data dictionary helps B2B by gracefully integrating intra-enterprise business processes and inter-enterprise business processes. This paper gives some clues about the importance of data dictionary in ERP and B2B, and introduces data dictionary support of SEA+, a component-based scalable ERP package system.

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