scholarly journals Data Governance Maturity Level at the National Archives of the Republic of Indonesia

2020 ◽  
Vol 10 (1) ◽  
pp. 27-40
Author(s):  
Sari Agustin Wulandari
Keyword(s):  
Data Management ◽  
Good Governance ◽  
Maturity Level ◽  
Data Governance ◽  
Management Planning ◽  
Governance Model ◽  
Master Data ◽  
National Archives ◽  
Grand Design ◽  
The Republic

The National Archives of the Republic of Indonesia (ANRI) as an institution given mandate to carry out state duty in the field of archives has vision as a pillar of good governance and nation’s collective memory. To implement it, the study of the grand design of the archival system arranged. That is very related to the data governance implementation. Therefore, ANRI needs to know the maturity level of the data governance function which had been held. The assessment was done by referring to the Stanford Data Governance Model. The result showed that data governance is still at an initial level. The foundational aspects are on an average of 1,2 which contains awareness, formalization, and metadata. While on project aspects are on average of 1,5 consisting of stewardship, data quality, and master data. In total, ANRI is at the level of 1,35. ANRI needs to make improvements for data management planning activities referring to Data Management Body of Knowledge (DMBOK) with a focus on people, policies, and capabilities dimensions in all aspects. This research is expected to be helpful for ANRI to make improvements corresponding to the recommendations thus ANRI could implement national data archival properly.

scholarly journals Data Governance Maturity Level at the National Archives of the Republic of Indonesia

2020 ◽  
Vol 10 (1) ◽  
pp. 27
Author(s):  
Sari Agustin Wulandari
Keyword(s):  
Data Management ◽  
Good Governance ◽  
Maturity Level ◽  
Data Governance ◽  
Management Planning ◽  
Governance Model ◽  
Master Data ◽  
National Archives ◽  
Grand Design ◽  
The Republic

<em><span>The National Archives of the Republic of Indonesia (ANRI) as an institution given mandate to carry out state duty in the field of archives has vision as a pillar of good governance and nation’s collective memory. To implement it, the study of the grand design of the archival system arranged. That is very related to the data governance implementation. Therefore, ANRI needs to know the maturity level of the data governance function which had been held. The assessment was done by referring to the Stanford Data Governance Model. The result showed that data governance is still at an initial level. The foundational aspects are on an average of 1,2 which contains awareness, formalization, and metadata. While on project aspects are on average of 1,5 consisting of stewardship, data quality, and master data. In total, ANRI is at the level of 1,35. ANRI needs to make improvements for data management planning activities referring to Data Management Body of Knowledge (DMBOK) with a focus on people, policies, and capabilities dimensions in all aspects. This research is expected to be helpful for ANRI to make improvements corresponding to the recommendations thus ANRI could implement national data archival properly</span><span lang="EN-US">.</span></em>

scholarly journals Control Combating Corruption: A Governance Model from Indonesians' Perspective

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Ali Muktiyanto ◽  
Rini Dwiyani ◽  
Noorina Hartati ◽  
Halim Dedy Perdana ◽  
Bayu Taufiq Possumah
Keyword(s):  
Agency Theory ◽  
Good Governance ◽  
Data Governance ◽  
Transparency International ◽  
Governance Model ◽  
The World ◽  
Corruption Perception ◽  
Political Conditions ◽  
Using Data ◽  
Inference Tests

The purpose of this study is to propose an appropriate governance model to deal with corruption. This study reveals the indicators, from where and how corruption will be resisted. By using data governance and corruption control as well as the corruption perception index from WGI and TII in 2008-2018, this study proposed a quantitative approach to strengthen the results of the inference tests of the effect of good governance on the potential for corruption, confirming and expanding on work carried out with critical informants by Transparency International Indonesia (TII). The study found that, from the perspective of agency theory, the influence of governance on corruption has been proved, both in the context of the world and Indonesia. Good governance will make the trustee (agent) not arbitrarily follow their wishes through corruption to enrich themselves or other parties, but instead follow the mandate given by the principal (community). This study also shows, in the world context, that by adherence to ethical rules being followed by effective government, in stable political conditions, and public voices being heard, corruption can be eradicated. In the context of Indonesia, to suppress criminal acts of corruption, stable political conditions and guarantees for public votes must be done first, then effective government and compliance with regulations can follow.

scholarly journals MASTER DATA MANAGEMENT IMPLEMENTATION IN DISTRIBUTED INFORMATION SYSTEM CASE STUDY DIRECTORATE GENERAL OF TAX, MINISTRY OF FINANCE OF REPUBLIC OF INDONESIA

2019 ◽  
Vol 15 (1) ◽  
pp. 18-27
Author(s):  
Aris Budi Santoso ◽  
Yoga Pamungkas ◽  
Yova Ruldeviyani
Keyword(s):  
Information System ◽  
Data Management ◽  
Reference Data ◽  
Maturity Model ◽  
Maturity Level ◽  
Distributed Information System ◽  
Distributed Information ◽  
Master Data ◽  
Master Data Management ◽  
Directorate General

Information system architecture of Directorate General of Tax (DGT) is centralized with distributed data. The main problem are replication of master and reference data which spread among applications which vary on data structure and the synchronization jobs that spread in many locations and not well managed. Therefore, Master Data Management (MDM) needs to be implemented with accordance to characteristic of centralized distributed information system. Master data management maturity evaluation is conducted using MDM maturity model (MD3M) Spruit dan Pietzka, the result is Data Protection, Data Quality and Maintenance topic have maturity level 3 or defined process stage, while Data Model, Usage and Ownership topic have maturity level 2 or repeatable stage. Solutions to solve MDM issues and to enhance the master data management maturity level are proposed based on Data Management Body of Knowledge (DMBOK). DGT’s MDM issues are related to insufficiency of policy and architecture of MDM system. Policy and architectural approach of centralized MDM system is required to solve that issues. Proposed solution involves the use of data virtualization to enable implementation of centralized system of MDM without consolidate all master and reference data into new database.

scholarly journals Master Data Management Planning: A Case Study of Flight Information System at PT Angkasa Pura I (Persero)

2020 ◽  
Vol 1444 ◽  
pp. 012017
Author(s):  
R I P Putra ◽  
J P Nurahman ◽  
R R Yana ◽  
H Winarno ◽  
A N Hidayanto ◽  
...  
Keyword(s):  
Information System ◽  
Data Management ◽  
Management Planning ◽  
Master Data ◽  
Master Data Management

scholarly journals Erfolgsfaktor Data Governance

2021 ◽  
Vol 2021 (3) ◽  
pp. 24-26
Author(s):  
Christiana Klingenberg ◽  
◽  
Kristin Weber
Keyword(s):  
Data Management ◽  
Data Governance ◽  
Governance Framework ◽  
Master Data ◽  
Master Data Management

Von Master Data Management (MDM) versprechen sich Unternehmen Effizienz, Transparenz und Risikominimierung im Umgang mit ihren Stammdaten. MDM soll dazu beitragen, Stammdaten als „Asset“ im Unternehmen zu bewirtschaften. Der vorliegende Beitrag liefert praktische Tipps, wie MDM-Implementierungen nachhaltig gestaltet werden können, damit die Daten einen Beitrag zum Unternehmenserfolg leisten. Er stellt das qualitätsorientierte Data Governance Framework vor. Das Framework stellt sicher, dass bei einer Implementierung alle Aspekte von MDM adressiert werden inkl. strategischer und organisatorischer Fragestellungen. Die konsequente Ausrichtung an der Datenqualität sorgt dafür, dass alle Unternehmensbereiche Stammdaten nutzenstiftend einsetzen können.

scholarly journals Improving Data Quality and Data Governance Using Master Data Management: A Review

2021 ◽  
Vol 5 (3) ◽  
pp. 90
Author(s):  
Sanny Hikmawati ◽  
Paulus Insap Santosa ◽  
Indriana Hidayah
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Incomplete Data ◽  
Data Governance ◽  
Consistent System ◽  
Roles And Responsibilities ◽  
Master Data ◽  
Master Data Management ◽  
System Information ◽  
Poor Management

Master data management (MDM) is a method of maintaining, integrating, and harmonizing master data to ensure consistent system information. The primary function of MDM is to control master data to keep it consistent, accurate, current, relevant, and contextual to meet different business needs across applications and divisions. MDM also affects data governance, which is related to establishing organizational actors’ roles, functions, and responsibilities in maintaining data quality. Poor management of master data can lead to inaccurate and incomplete data, leading to lousy stakeholder decision-making. This article is a literature review that aims to determine how MDM improves the data quality and data governance and assess the success of MDM implementation. The review results show that MDM can overcome data quality problems through the MDM process caused by data originating from various scattered sources. MDM encourages organizations to improve data management by adjusting the roles and responsibilities of business actors and information technology (IT) staff documented through data governance. Assessment of the success of MDM implementation can be carried out by organizations to improve data quality and data governance by following the existing framework.

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