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* Legner C., Pentek T., Otto B. Accumulating Design Knowledge with Reference Models: Insights from 12 Years’ Research into Data Management // Journal of the Association for Information Systems, 2020, 21(3): 735–770. DOI: 10.17705/1jais.00618. – URL: https://www.researchgate.net/publication/341684789_Accumulating_Design _Knowledge_with_Reference_Models_Insights_from_12_Years%27_Resear ch_into_Data_Management.
Как уже было отмечено в главе 6, в настоящее время, по мнению ряда специалистов, наиболее полные и ценные с методической точки зрения (а также не зависящие от поставщика соответствующих решений) референтные модели управления данными – это DAMADMBOK и CMMI DMM. При этом первая ориентирована на формирование способностей организации по управлению данными, а вторая – на оценку зрелости этих способностей[347].
Литература к главе 8
•Aiken P., Harbour T. Data Strategy and the Enterprise Data Executive: Ensuring that Business and IT are in Synch in the Post-Big Data Era. Technics Publications, 2017.
•Deng Z. MIS2502: Data Analytics: Semi-structured Data Analytics. Fox School of Business. Temple University, 2019. – URL: https://slidetodoc.com/mis-2502-data-analyticssemistructured-data- analytics-zhe/.
•Ladley J. Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program: 2nd Edition. Academic Press, 2020.
•Управление данными в госсекторе. Навигатор для начинающих / под ред. О. М. Гиацинтова, В. А. Сазонова, М. С. Шклярук. – М.: РАНХиГС, 2022.