A Survey of Maturity Models in Data Management

被引:9
作者
Belghith, Oumaima [1 ]
Skhiri, Sabri [1 ]
Zitoun, Sirine [1 ]
Ferjaoui, Syrine [1 ]
机构
[1] Eura Nova, R&D Eura Nova, Tunis, Tunisia
来源
2021 IEEE 12TH INTERNATIONAL CONFERENCE ON MECHANICAL AND INTELLIGENT MANUFACTURING TECHNOLOGIES, ICMIMT | 2021年
关键词
maturity models; data management; maturity assessment; digital transformation;
D O I
10.1109/ICMIMT52186.2021.9476197
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Maturity models are helpful business tools that refine and develop the way organizations conduct their businesses and benchmark their maturity status against a scale or with industry peers. They serve to better prioritize the actions for improvement and control the progress in reaching the target maturity stage. To the best of our knowledge, very few survey papers are available on data management maturity models in academia, from which we studied their data and findings. In this context, our paper summarizes and organizes a variety of research that is related to or encompasses the data management field. Consequently, this paper is of interest both for scientists as well as practitioners from different industries and fields as it aims to highlight the importance of maturity models in the field of data management. From an academic perspective, our survey delivers a thorough literature review as it investigates maturity models that are either for or related to data management. Moreover, it offers a comparative analysis in terms of the main concepts and features associated with these models through a developed metamodel. This proposed framework describes the functional coverage of data management maturity models where models can be compared and evaluated based on their approaches to identify and categorize the data management related functions. As a result, this metamodel can serve as a tool for researchers who can exploit this framework to position future maturity models.
引用
收藏
页码:298 / 309
页数:12
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