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.

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 Data Profiling Model for Assessing the Quality Traits of Master Data Management

2020 ◽  
Vol 8 (6) ◽  
pp. 446-450
Keyword(s):  
Data Management ◽  
Resource Planning ◽  
Quality Data ◽  
Quality Traits ◽  
High Quality ◽  
Wrong Decision ◽  
Data Profiling ◽  
Master Data ◽  
Master Data Management

Enterprise Resource Planning (ERP) and Business Intelligence (BI) system demand progressive rules for maintaining the valuable information about customers, products, suppliers and vendors as data captured through different sources may not be of high quality due to human errors, in many cases. The problem encounters when this information is accessible across multiple systems, within same organization. Providing adequacy to this scattered data is a top agenda for any organization as maintaining the data is complicated, as having high quality data. Master Data Management (MDM) provides a solution to these problems by maintaining “a single reference of truth” with authoritative source of master data (Customer, products, employees etc). Master Data Management (MDM) is a highlighted concern now a day as valid data is the demand for strategic, tactical and operational steering of every organization. The lane to MDM initiates with the quality of data which demands for discovery of master data, profiling and analysis. As inadequacy of data may leads to adverse effects such as wrong decision, loss of time, bad results and unnecessary risk. Thus there is a need to deal with master data and quality of this specific data in a successful and efficient manner. For ensuring this purpose, an approach is proposed in this paper. The research focuses on development of a Model for Data Profiling to assess the level of Quality Traits for Master Data Management. Results are shown by executing the defined steps on TALEND tool over collected dataset. Thus, level of quality traits processes directly correlates with an organization’s ability to make the proper decisions and better outcomes.

scholarly journals PERANCANGAN MASTER DATA MANAGEMENT PADA REGULATOR INSTITUSI FINANSIAL

2018 ◽  
Vol 4 (2) ◽  
pp. 28-33
Author(s):  
Sugiharto Hartono
Keyword(s):  
Data Management ◽  
Object Oriented ◽  
Single View ◽  
Analysis And Design ◽  
Master Data ◽  
Master Data Management ◽  
Object Oriented Analysis

Regulator Institusi Finansial di Indonesia mengemban amanat dan wewenang untuk menyelenggarakan sistem pengaturan dan pengawasan yang terintegrasi terhadap keseluruhan kegiatan di pelaku Industri Jasa Keuangan (IJK). Tujuan penelitian ini adalah untuk menghasilkan single view data Akuntan dan Penilai, untuk melakukan migrasi data Akuntan dan Penilai yang ada pada excel ke sistem, dan dapat dijadikan pondasi dan langkah awal dalam penerapan MDM untuk sektor lainnya di Regulator Institusi Finansial. Metode yang digunakan dalam penelitian ini adalah studi literatur, studi lapangan (wawancara dan dokumentasi), serta menggunakan Object Oriented Analysis and Design. Solusi yang diberikan adalah dengan melakukan perancangan master data management agar tidak terjadi kesalahan serta mempercepat dalam pengambilan keputusan (dalam hal redudansi data pada profesi penunjang lainnya) dikarenakan media penyimpanan data masing masing profesi berbeda.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

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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