Master Data Management and Customer Data Integration Defined

2015 ◽  
pp. 33-60
Keyword(s):  
Data Integration ◽  
Data Management ◽  
Customer Data ◽  
Master Data ◽  
Master Data Management ◽  
Customer Data Integration

Master data management and its organizational implementation

2017 ◽  
Vol 30 (3) ◽  
pp. 454-475 ◽  
Author(s):  
Riikka Vilminko-Heikkinen ◽  
Samuli Pekkola
Keyword(s):  
Data Management ◽  
Management Practices ◽  
Ethnographic Study ◽  
Affecting Factors ◽  
Simple Task ◽  
Content Type ◽  
Customer Data ◽  
Complex Concept ◽  
Master Data ◽  
Master Data Management

Purpose Master data management (MDM) aims to improve the value of an organization’s most important data, such as customer data, by bridging the silos between organizational units and information systems. However, incorporating data management practices into an organization is not a simple task. The purpose of this paper is to provide a new understanding of the challenges in establishing and developing the MDM function within an organization. Design/methodology/approach This papers report an ethnographic study within a municipality. The data were collected from two consecutive MDM development projects over the time period of 32 months by observing MDM-related activities and interviewing appropriate actors. Observations, interviews, and impressions were documented to a diary that was later qualitatively analyzed. Various project documentation were also used. Findings In total 15 challenges were identified. Seven of these were not identified earlier in the literature. New challenges included legislation-driven challenges, mutual understanding of master data domains, and the level of granularity for those domains. Eight issues, such as data owner and data definitions, were MDM specific, others being more generic. All of the issues were identified as preconditions or as affecting factors for the others. Three of the issues were identified as pivotal. The issues emphasize strong alignment between the complex concept of MDM and the organization adopting it. Research limitations/implications This research was based on a single qualitative case study, and caution should be exercised with regard to generalizations. The findings increase understanding about the complex organizational phenomena. The study offers public sector and private sector practitioners insights of the organizational issues that establishing a MDM function can encounter. Originality/value The issues discovered in the research shed light on the strong alignment between the complex concept of MDM and the organization. The results of this study assist researchers in their endeavor to understand the organizational aspects of MDM, and to build theoretical models, frameworks, practices, and explanations.

scholarly journals Master Data Management Model in Company: Challenges and Opportunity

2015 ◽  
Vol 6 (4) ◽  
pp. 514 ◽  
Author(s):  
Indrajani Indrajani
Keyword(s):  
Data Management ◽  
Quality Of Data ◽  
Literature Study ◽  
Customer Data ◽  
Single View ◽  
Master Data ◽  
Matching Technique ◽  
Master Data Management ◽  
Fact Finding

The purpose of this research is to analyze, design, and implement Master Data Management (MDM) model for company, which include database processing that will be used in the quality of data customer and produce single view of customer. The research method used is literature study from a variety of journals, books, e-books, and articles on the internet. Also, fact finding techniques are done, such as by analyze, collect, and examine the documents, interviews, and observations. Then, other research methods used to analyze and design MDM model are using cleansing and matching technique. The result obtained from this research is animplementation MDM model for the company, where if implemented, will improve the quality of data significantly. The conclusion which can be obtained from this research is that MDM is one of the factors thatcan improve the quality of customer data.

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.

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