scholarly journals A Framework for Improving Business Intelligence through Master Data Management

2016 ◽  
pp. 1-12 ◽  
Author(s):  
Ray M. Kekwaletswe ◽  
Tshegofatso Lesole
Keyword(s):  
Data Management ◽  
Business Intelligence ◽  
Master Data ◽  
Master Data Management

Experiences in Establishing Trustworthy Digital Repositories Within a Large Multi-National Pipeline Company

2012 ◽  
Author(s):  
David A. Weir ◽  
Stephen Murray ◽  
Pankaj Bhawnani ◽  
Douglas Rosenberg
Keyword(s):  
Data Management ◽  
Lessons Learned ◽  
Records Management ◽  
Digital Repositories ◽  
Geographically Dispersed ◽  
Local Business ◽  
Master Data ◽  
Business Area ◽  
Master Data Management ◽  
Trusted Data

Traditionally business areas within an organization individually manage data essential for their operation. This data may be incorporated into specialized software applications, MS Excel or MS Access etc., e-mail filing, and hardcopy documents. These applications and data stores support the local business area decision-making and add to its knowledge. There have been problems with this approach. Data, knowledge and decisions are only captured locally within the business area and in many cases this information is not easily identifiable or available for enterprise-wide sharing. Furthermore, individuals within the business areas often keep “shadow files” of data and information. The state of accuracy, completeness, and timeliness of the data contained within these files is often questionable. Information created and managed at a local business level can be lost when a staff member leaves his or her role. This is especially significant given ongoing changes in today’s workforce. Data must be properly managed and maintained to retain its value within the organization. The development and execution of “single version of the truth” or master data management requires a partnership between the business areas, records management, legal, and the information technology groups of an organization. Master data management is expected to yield significant gains in staff effectiveness, efficiency, and productivity. In 2011, Enbridge Pipelines applied the principles of master data management and trusted data digital repositories to a widely used, geographically dispersed small database (less than 10,000 records) that had noted data shortcomings such as incomplete or incorrect data, multiple shadow files, and inconsistent usage throughout the organization of the application that stewards the data. This paper provides an overview of best practices in developing an authoritative single source of data and Enbridge experience in applying these practices to a real-world example. Challenges of the approach used by Enbridge and lessons learned will be examined and discussed.

Urban Master Data Management

2017 ◽  
pp. 441-465
Author(s):  
Adolphe Ayissi Eteme ◽  
Justin Moskolai Ngossaha
Keyword(s):  
Information Technology ◽  
Information Systems ◽  
Data Management ◽  
Data Repository ◽  
Distributed Data ◽  
Data Systems ◽  
Policy Makers ◽  
Master Data ◽  
Master Data Management

The use of information technology in council management has resulted in the generation of a large amount of data through various autonomous urban bodies. The relevant bodies barely or never reuse such locally-generated data. This may be due particularly to managers', policy makers' and users' lack of awareness of existing information. The Platform for the Integration and Interoperability of the Yaounde Urban Information Systems (YUSIIP) project seeks to reduce this deficit by establishing a federated operational platform of heterogeneous and distributed data systems based on a distributed data repository. The position developed in this paper is that Master Data Management (MDM) will contribute to achieving this objective in a context marked by the dispersion and duplication of data and diversity of information systems.

Automation of data normalization for implementing master data management systems

2013 ◽  
Vol 39 (3) ◽  
pp. 115-123 ◽  
Author(s):  
Ya. R. Nedumov ◽  
D. Yu. Turdakov ◽  
V. D. Maiorov ◽  
P. E. Ovchinnikov
Keyword(s):  
Data Management ◽  
Management Systems ◽  
Data Normalization ◽  
Data Management Systems ◽  
Master Data ◽  
Master Data Management
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