Data management practice in 21st century: systematic review

Gizealew Alazie Dagnaw, Sisay Ebabye Tsigie


The aim of this report is to explore about large amount of data spawn from the globe in the order of us reached levels that were previously unbelievable. New technologies are generating data in new ways: as wearable devices quantify individuals’ health, social media sites provide platforms to share details about day-to-day life, and companies across sectors rely on data about their daily business and activities to improve their products and processes. Data Management is a broad collection of practices, concepts, procedures, processes, and a wide range of accompanying systems that allow for an organization to gain control of its data resources. Data Management as an overall practice is involved with the entire lifecycle of a given data asset from its original creation point to its final retirement, how it progresses and changes throughout its lifetime through the internal (and external) data streams of an enterprise. In this report, we consider data control to mean everything intended to inform the extent of confidence in data management, data use and the technologies derived from it. We cannot properly consider this by treating data management or data use individually or separately from each other. While data management and data use may previously have been separate activities, the two are now often tangled with each other, across applications and across the world. To achieve a meaningful discussion about data governance, it is therefore necessary to consider both together. Such integration requires a new approach to framing questions about data governance and, in this context, purpose is of overarching importance. The shifting nature of data management and data use, the evolving technological context, and the shifting meaning of core governance concepts, place today’s systems for data governance under stress. The impact of these changes is further compounded by their speed. Effectively managing data can help to optimize research outputs, increase the impact of research, and support open scientific inquiry


data, big data, data management, data science

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