Data Quality Management in Educational Data

Every varsity in Indonesia is responsible for ensuring the completeness, the validity, the accuracy, and the currency of its educational data. The educational data is used for implementing higher-education quality assurance system and formulating policies related to universities and majors in Indonesia. Data quality assessment result indicates that educational data in Statistics Polytechnic did not meet completeness, validity, accuracy, and currency criteria. Data quality management maturity has been measured using Loshin’s Data Quality Maturity Model which result is in level 1 to level 2 of maturity. Only the data quality dimensions component has achieved the expected target. Thus, recommendations have been proposed based on the DAMA-DMBOK framework. The activities needed to be carried out are developing and promoting awareness of data quality; defining data quality requirements; profiling, analyzing, and evaluating data quality; define business rules for data quality, establish, and evaluate the data quality services levels, manage problems related to data quality, design and implement operational procedures for data quality management, and monitor operations and performance of data quality management procedures.

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A Maturity Model for Enterprise Data Quality Management

Enterprise Modelling and Information Systems Architectures ◽

10.1007/s40786-013-0002-z ◽

2013 ◽

Vol 8 (2) ◽

pp. 4-24 ◽

Cited By ~ 6

Author(s):

Martin Ofner ◽

Boris Otto ◽

Hubert Österle

Keyword(s):

Quality Management ◽

Data Quality ◽

Maturity Model ◽

Data Quality Management

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A Data Quality Management Maturity Model

ETRI Journal ◽

10.4218/etrij.06.0105.0026 ◽

2006 ◽

Vol 28 (2) ◽

pp. 191-204 ◽

Cited By ~ 25

Author(s):

Kyung Seok Ryu ◽

Joo Seok Park ◽

Jae Hong Park

Keyword(s):

Quality Management ◽

Data Quality ◽

Maturity Model ◽

Data Quality Management

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Data Quality Management Maturity Measurement of Government-Owned Property Transaction in BMKG

CommIT (Communication and Information Technology) Journal ◽

10.21512/commit.v12i2.4470 ◽

2018 ◽

Vol 12 (2) ◽

pp. 59

Author(s):

Septian Bagus Wibisono ◽

Achmad Nizar Hidayanto ◽

Widijanto Satyo Nugroho

Keyword(s):

Quality Management ◽

Data Quality ◽

Central Government ◽

Maturity Model ◽

Data Quality Management ◽

Financial Report ◽

Transaction Data ◽

Body Of Knowledge ◽

Inaccurate Data

Government-Owned Property (GOP) management, including the bookkeeping of GOP transaction, is part of GOP Officer responsibility to ensure the quality of transaction data. This responsibility also applies to GOP Officer in Indonesian Agency for Meteorological, Climatological and Geophysics ‘Badan Meteorologi, Klimatologi, dan Geofisika’ (BMKG). GOP data as the source for the Central Government Financial Report is expected to be well-maintained. It must be presented as accurate as possible, although there are still inaccurate data presented in the latest BMKG GOP Report. This qualitative research using document study and some interview sessions aims to measure how well the Data Quality Management (DQM) maturity of GOP transaction in BMKG using Loshin’s Data Quality Maturity model. Thus, the result of maturity assessment is analyzed to recommend and implement DQM activities from the Data Management Body of Knowledge (DMBOK). The purpose is to improve GOP DQM. The research shows that the level of DQM maturity is at a repeatable level to defined level. Moreover, 52 maturity characteristics need to be followed through with DQM activities.

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Depicting Data Quality Issues in Business Intelligence Environment through a Metadata Framework

International Journal of Business Intelligence Research ◽

10.4018/ijbir.2016070102 ◽

2016 ◽

Vol 7 (2) ◽

pp. 20-31

Author(s):

Te-Wei Wang ◽

Yuriy Verbitskiy ◽

William Yeoh

Keyword(s):

Quality Management ◽

Data Quality ◽

Business Intelligence ◽

Quality Data ◽

Data Quality Management ◽

Metadata Model ◽

Modern Business ◽

Quality Framework ◽

Promising Solution ◽

Quality Dimensions

Modern business intelligence systems depend highly on high quality data. The core of data quality management is to identify all possible sources of data quality problems. To achieve this goal, an extensive metadata infrastructure is the most promising solution. Through theoretical metadata model investigation, the authors identified a set of data quality dimensions by carefully examining the data quality management principles and applied those principles to current BI environment. They summarize their analysis by proposing a BI data quality framework.

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Depicting Data Quality Issues in Business Intelligence Environment Through a Metadata Framework

Advances in Business Strategy and Competitive Advantage - Applying Business Intelligence Initiatives in Healthcare and Organizational Settings ◽

10.4018/978-1-5225-5718-0.ch016 ◽

2019 ◽

pp. 291-304

Author(s):

Te-Wei Wang ◽

Yuriy Verbitskiy ◽

William Yeoh

Keyword(s):

Quality Management ◽

Data Quality ◽

Business Intelligence ◽

Quality Data ◽

Data Quality Management ◽

Metadata Model ◽

Modern Business ◽

Quality Framework ◽

Promising Solution ◽

Quality Dimensions

Modern business intelligence systems depend highly on high quality data. The core of data quality management is to identify all possible sources of data quality problems. To achieve this goal, an extensive metadata infrastructure is the most promising solution. Through theoretical metadata model investigation, the authors identified a set of data quality dimensions by carefully examining the data quality management principles and applied those principles to current BI environment. They summarize the analysis by proposing a BI data quality framework.

Download Full-text