Enterprise Information System and Data Mining

2010 ◽  
Vol 1 (3) ◽  
pp. 34-41 ◽  
Author(s):  
Kenneth D. Lawrence ◽  
Dinesh R. Pai ◽  
Ronald Klimberg ◽  
Sheila M. Lawrence
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Information Systems ◽  
Business Intelligence ◽  
Large Data ◽  
Customer Relationship ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
Competitive Edge ◽  
Organizational Functions

The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.

Enterprise Information System and Data Mining

2012 ◽  
pp. 228-235
Author(s):  
Kenneth D. Lawrence ◽  
Dinesh R. Pai ◽  
Ronald Klimberg ◽  
Sheila M. Lawrence
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Information Systems ◽  
Business Intelligence ◽  
Large Data ◽  
Customer Relationship ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Competitive Edge ◽  
Organizational Functions

The advent of information technology and the consequent proliferation of information systems have lead to generation of vast amounts of data, both within the organization and across its supply chain. Enterprise information systems (EIS) have added to organizational complexity, and at the same time, created opportunities for enhancing its competitive advantage by utilizing this data for business intelligence purposes. Various data mining tools have been used to gain a competitive edge through these large data bases. In this paper, the authors discuss EIS-aided business intelligence and data mining as applicable to organizational functions, such as supply chain management (SCM), marketing, and customer relationship management (CRM) in the context of EIS.

scholarly journals EXPERT EVALUATION OF DAMAGE THROUGH ENTERPRISE INFORMATION SYSTEM FAILURE

2020 ◽  
Vol 2020 (4) ◽  
pp. 112-120
Author(s):  
Valeriy Fedorovich Shurshev ◽  
Iurii Gostiunin
Keyword(s):  
Information System ◽  
Information Systems ◽  
Level Of Service ◽  
Service Availability ◽  
System Failure ◽  
Damage Evaluation ◽  
Critical Systems ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
Operational Impacts

The article considers the problem of damage evaluation in case of failure of the information system. There have been analyzed the practical methods of assessing damage. It has been stated that the methods can reveal the dependence of the damage on the downtime of the information system, but they are unable to evaluate reputation, administrative or any other consequences. An algorithm is proposed by which specialists can conduct a comparative assessment of damage in case of failure of various information systems using expert information. Applying the proposed algorithm to different information systems, it is possible to determine the most critical systems and, on this basis, effectively plan operational impacts to increase the level of service availability.

scholarly journals DATA SOURCES FOR BUSINESS INTELLIGENCE

2021 ◽  
Author(s):  
Naveen Kunnathuvalappil Hariharan
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Personal Data ◽  
Data Sources ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
External Data ◽  
Operational Systems ◽  
Internal Sources ◽  
Key Questions

As organizations' desire for data grows, so does their search for data sources that are both usable and reliable.Businesses can obtain and collect big data in a variety of locations, both inside and outside their own walls.This study aims to investigate the various data sources for business intelligence. For business intelligence,there are three types of data: internal data, external data, and personal data. Internal data is mostly kept indatabases, which serve as the backbone of an enterprise information system and are known as transactionalsystems or operational systems. This information, however, is not always sufficient. If the company wants toanswer market and industry questions or better understand future customers, the analytics team may need to look beyond the company's own data sources. Organizations must have access to a variety of data sources in order to answer the key questions that guide their initiatives. Internal sources, external public sources, andcollaboration with a big data expert could all be beneficial. Companies who are able to extract relevant datafrom their mountain of data acquire new perspectives on their business, allowing them to become morecompetitive

Implementation Management of an E-Commerce-Enabled Enterprise Information System

2011 ◽  
pp. 1851-1855
Author(s):  
Joseph Sarkis ◽  
R.P. Sundarraj
Keyword(s):  
Enterprise Resource Planning ◽  
Resource Planning ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
Erp System ◽  
Implementation Model ◽  
Competitive Edge ◽  
Consulting Services ◽  
Systems Implementation ◽  
Implementation Management

The integration of enterprise systems and the supply chain to an organization is becoming more critical in an ever-changing, globally competitive environment. Quick response will require close relationships, especially communications and information sharing among integrated internal functional groups as well as the suppliers and customers of an organization. Texas Instruments (TI), headquartered in Dallas, Texas, has come to realize this requirement for building and maintaining its competitive edge. Thus, it sought to implement an enterprise resource planning (ERP) system with a focus on linking it with a global electronic commerce (e-commerce) setting, an innovative and current issue (Weston, 2003). There were a number of major players, including project management direction from Andersen Consulting Services, software vendors such as SAP and i2 Technologies, hardware vendors such as Sun Microsystems, and various suppliers and customers of TI. The purpose of this case is to provide some aspects of implementation of strategic systems that provide valuable lessons for success. We begin and rely on the foundation of a strategic systems implementation model, which is initially described. A description of the case follows, with the various stages as related to strategic systems implementation described. We complete our discussion with implications and conclusions.

Biological Data Mining

2008 ◽  
pp. 1696-1705
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Data Mining ◽  
New Technologies ◽  
Noisy Data ◽  
Large Data ◽  
Biological Data ◽  
Customer Relationship ◽  
Mass Storage ◽  
Stock Management ◽  
Management Fraud ◽  
Storage Media

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).

Biological Data Mining

2005 ◽  
pp. 35-41 ◽  
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Data Mining ◽  
New Technologies ◽  
Noisy Data ◽  
Large Data ◽  
Biological Data ◽  
Customer Relationship ◽  
Mass Storage ◽  
Stock Management ◽  
Management Fraud ◽  
Storage Media

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).

scholarly journals The impact of supply chain management on business intelligence

2017 ◽  
Vol 7 (2) ◽  
Author(s):  
Audrey Langlois ◽  
Benjamin Chauvel
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Private Companies ◽  
Chain Management ◽  
Conceptual Paper ◽  
The Impact

This conceptual paper investigates the impact of the supply chain on businessintelligence (BI) in private companies. The article focuses on these two subjects in order tobroadly understand the concept of business intelligence, supply chain and characteristicsimplement such as OLAP, data warehouse or data mining. It looks at the joint advantages ofthe business intelligence and supply chain concepts and revisits the traditional BI concept. Wefound that the supply chain includes many data samples collected from the first supplier to thelast customer, which have to be analysed by the company in order to be more efficient. Basedon these observations the authors argue for why it makes sense to see the BI function as anextension of supply chain management, but moreover they show how difficult it has become toseparate BI from other IT intensive processes in the organization.

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