TY - GEN
T1 - Data Quality Management Maturity Model
T2 - 6th International Conference on Cyber and IT Service Management, CITSM 2018
AU - Sabtiana, Rela
AU - Yudhoatmojo, Satrio Baskoro
AU - Hidayanto, Achmad Nizar
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/3/25
Y1 - 2019/3/25
N2 - Data are widely used in an organization not only for operation but also for strategic level use. Poor data quality can have negative impact for an organization such as poor decision making and planning. Therefore, data quality management becomes an issue growing today not only to the academic but also professional communities. Based on this issue, this paper presents and analyzes a case study developed in a governmental agency, BPS-Statistics of Kaur Regency. For analysis, a data quality maturity model is used to measure the implementation of data quality management in the organization. The results show that for the dimension of 'Data quality expectations' is at a maturity of 4.25. 'Data quality protocol' is at a maturity of 3.50. 'Policies' reaches a maturity of 3.67. 'Data quality protocol' and 'Data standard' are at a maturity of 4.42. 'Data governance' is at a maturity of 3.00. 'Technology' is at a maturity 3.17. 'Performance management' is at a maturity of 3.33. However, this also implies that implementing these particular dimensions will lead to a direct increase in overall maturity.
AB - Data are widely used in an organization not only for operation but also for strategic level use. Poor data quality can have negative impact for an organization such as poor decision making and planning. Therefore, data quality management becomes an issue growing today not only to the academic but also professional communities. Based on this issue, this paper presents and analyzes a case study developed in a governmental agency, BPS-Statistics of Kaur Regency. For analysis, a data quality maturity model is used to measure the implementation of data quality management in the organization. The results show that for the dimension of 'Data quality expectations' is at a maturity of 4.25. 'Data quality protocol' is at a maturity of 3.50. 'Policies' reaches a maturity of 3.67. 'Data quality protocol' and 'Data standard' are at a maturity of 4.42. 'Data governance' is at a maturity of 3.00. 'Technology' is at a maturity 3.17. 'Performance management' is at a maturity of 3.33. However, this also implies that implementing these particular dimensions will lead to a direct increase in overall maturity.
KW - data quality
KW - data quality management
KW - data quality maturity model
KW - maturity model
UR - http://www.scopus.com/inward/record.url?scp=85064353962&partnerID=8YFLogxK
U2 - 10.1109/CITSM.2018.8674323
DO - 10.1109/CITSM.2018.8674323
M3 - Conference contribution
AN - SCOPUS:85064353962
T3 - 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018
BT - 2018 6th International Conference on Cyber and IT Service Management, CITSM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 August 2018 through 9 August 2018
ER -