Measuring the Maturity Level of Social Assistance Distribution Data Governance Using the 2019 Cobit Framework

This study aims to measure the maturity level of data governance in the distribution of social assistance using the COBIT 2019 framework. We chose COBIT 2019 as the methodology due to its comprehensive guidance for information technology governance. Measurements were made by assessing researchers focused on 4 domains APO, BAI, and DSS. The results of the study indicate variations in the level of maturity across processes. Some processes, such as risk management and operational efficiency, have reached the desired level of maturity, while other processes, such as information security and organizational change management, still require significant improvement. The gaps found in this measurement provide valuable insights into areas that require further attention. Recommendations for improvement include improving user training, strengthening security procedures, and improving communication and stakeholder engagement. Implementation of the proposed improvements is expected to improve data governance maturity and, in turn, improve the quality and accuracy of social assistance distribution. This study makes a significant contribution to understanding and improving data governance in the public sector, especially in the context of social assistance distribution. The results and recommendations from this study can be used by related organizations to optimize their data management processes so that they can be more effective and efficient in serving the public.

Authors

Institutions

Publication Details

Journal
EXPERT Jurnal Manajemen Sistem Informasi dan Teknologi
Published
2024-12-31
DOI
https://doi.org/10.36448/expert.v14i2.4074
Primary Topic
Social and Behavioral Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Measuring the Maturity Level of Social Assistance Distribution Data Governance Using the 2019 Cobit Framework

Robby Yuli Endra, Usman Rizal
EXPERT Jurnal Manajemen Sistem Informasi dan Teknologi
Social and Behavioral Studies
article

Measuring the Maturity Level of Social Assistance Distribution Data Governance Using the 2019 Cobit Framework

Robby Yuli Endra, Usman Rizal
article en

Abstract

This study aims to measure the maturity level of data governance in the distribution of social assistance using the COBIT 2019 framework. We chose COBIT 2019 as the methodology due to its comprehensive guidance for information technology governance. Measurements were made by assessing researchers focused on 4 domains APO, BAI, and DSS. The results of the study indicate variations in the level of maturity across processes. Some processes, such as risk management and operational efficiency, have reached the desired level of maturity, while other processes, such as information security and organizational change management, still require significant improvement. The gaps found in this measurement provide valuable insights into areas that require further attention. Recommendations for improvement include improving user training, strengthening security procedures, and improving communication and stakeholder engagement. Implementation of the proposed improvements is expected to improve data governance maturity and, in turn, improve the quality and accuracy of social assistance distribution. This study makes a significant contribution to understanding and improving data governance in the public sector, especially in the context of social assistance distribution. The results and recommendations from this study can be used by related organizations to optimize their data management processes so that they can be more effective and efficient in serving the public.

EXPERT Jurnal Manajemen Sistem Informasi dan TeknologiVol. 14(2)
Universitas Bandar Lampung (ID)
Reduced inequalities
Openalex Percentile: Top 66%
Social and Behavioral Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.