.png){kind=icon}
.png){kind=icon}
Return
DaLiF: a data lifecycle framework for data-driven governments
.png){kind=icon}
OA
AI
DOI：10.1186/s40537-021-00481-3.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as new oil and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens' engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers' and practitioners' interest.
Keywords:
Big data
Data-driven government
Government Big Data Ecosystem
Data management
Data lifecycle
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.4
Papers:
1.4K
Citations:
1.1W