Big Data in the Public Sector

Munné, Ricard

doi:10.1007/978-3-319-21569-3_11

Cited by 52 publications

(19 citation statements)

References 3 publications

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“…However, similar examples can be found in other countries (see e.g., (Gamage 2016;Munné 2016)) with equivalent capacities to get value out of data and information. Those countries can be considered as countries with high data collection and high information use (Amankwah-Amoah 2015).…”

Section: Methodsmentioning

confidence: 99%

Evolving Government Information Processes for Service Delivery: Identifying Types & Impact

Heijlen

Crompvoets

Bouckaert

et al. 2018

Administrative Sciences

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Abstract:The fulfilment of government objectives is dependent on information. Traditionally, this information originates from a classic process of input, (processing) activities, and output. However, in light of increased intergovernmental data sharing, the movement towards open data and opportunities concerning big data, traditional government information processes are evolving. This article aims to bring insight into these evolutions by creating a typology. Based on the typology, the paper further reflects on the impact these evolutions have on the processes and creation, delivery and improvement of public service.

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Section: Methodsmentioning

confidence: 99%

Evolving Government Information Processes for Service Delivery: Identifying Types & Impact

Heijlen

Crompvoets

Bouckaert

et al. 2018

Administrative Sciences

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“…Munne has explained the technological trends for current social and economical status. This paper highlighted BDI technologies and applications in the public sector [5]. Table 2 presents a summary of the highlights of the papers reviewed the public sector.…”

Section: Review Of the Public Sectormentioning

confidence: 99%

Big Data Integration Solutions in Organizations: A Domain-Specific Analysis

Karanam¹,

Kamath²,

Kulkarni³

et al. 2021

Data Integrity and Quality

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Big Data Integration (BDI) process integrates the big data arising from many diverse data sources, data formats presents a unified, valuable, customized, holistic view of data. BDI process is essential to build confidence, facilitate high-quality insights and trends for intelligent decision making in organizations. Integration of big data is a very complex process with many challenges. The data sources for BDI are traditional data warehouses, social networks, Internet of Things (IoT) and online transactions. BDI solutions are deployed on Master Data Management (MDM) systems to support collecting, aggregating and delivering reliable information across the organization. This chapter has conducted an exhaustive review of BDI literature and classified BDI applications based on their domain. The methods, applications, advantages and disadvantage of the research in each paper are tabulated. Taxonomy of concepts, table of acronyms and the organization of the chapter are presented. The number of papers reviewed industry-wise is depicted as a pie chart. A comparative analysis of curated survey papers with specific parameters to discover the research gaps were also tabulated. The research issues, implementation challenges and future trends are highlighted. A case study of BDI solutions implemented in various organizations was also discussed. This chapter concludes with a holistic view of BDI concepts and solutions implemented in organizations.

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“…Big data has traditionally been stored offline and analyzed in batch mode, but increasingly real-time collection and analysis of data is being used to enable more timely decision making [13]. In the public sector, big data has not traditionally been much used, although governments are becoming increasingly aware of the value to be gained from it [14]. The big data technologies of potential value in the public sector include real-time data applications, predictive analytics, and natural language analytics.…”

Section: Local Government Big Data Projects In Other Countriesmentioning

confidence: 99%

Big Data Analytics in Australian Local Government

Watson¹,

Ryan

2020

Smart Cities

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Australian governments at all three levels—local (council), state, and federal—are beginning to exploit the massive amounts of data they collect through sensors and recording systems. Their aim is to enable Australian communities to benefit from “smart city” initiatives by providing greater efficiencies in their operations and strategic planning. Increasing numbers of datasets are being made freely available to the public. These so-called big data are amenable to data science analysis techniques including machine learning. While there are many cases of data use at the federal and state level, local councils are not taking full advantage of their data for a variety of reasons. This paper reviews the status of open datasets of Australian local governments and reports progress being made in several student and other projects to develop open data web services using machine learning for smart cities.

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Big Data in the Public Sector

Cited by 52 publications

References 3 publications

Evolving Government Information Processes for Service Delivery: Identifying Types & Impact

Evolving Government Information Processes for Service Delivery: Identifying Types & Impact

Big Data Integration Solutions in Organizations: A Domain-Specific Analysis

Big Data Analytics in Australian Local Government

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