Big Data, Bigger Questions: Data-based Business Models and Their Implications for Organizational Boundaries, Data Governance, and Society

Big data (BD) is making advances in promoting sustainable consumption behaviour and has attracted the attention of researchers worldwide. Despite the increased focus, the findings of studies on this topic are fragmented, and future researchers need a systematic understanding of the existing literature for identification of the research scope. This study offers a systematic review of the role of BD in promoting sustainable-consumption behaviour with the help of a bibliometric analysis, followed by a thematic analysis. The findings suggest that businesses deploy BD to create sustainable consumer experiences, predict consumer buying patterns, design and alter business models and create nudges for sustainable consumption, while consumers are forcing businesses to develop green operations and supply chains to reduce the latter’s carbon footprint. The major research gaps for future researchers are in the following areas: the impact of big data analytics (BDA) on consumerism, the role of BD in the formation of sustainable habits and consumer knowledge creation for sustainable consumption and prediction of green consumer behaviour.

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Application of big data in healthcare: examination of the military experience

Health and Technology ◽

10.1007/s12553-020-00513-7 ◽

2021 ◽

Author(s):

David Berry

Keyword(s):

Big Data ◽

Vietnam War ◽

Business Models ◽

Model Development ◽

The United States ◽

Practical Implementation ◽

Systems Research ◽

System Versus ◽

Military Systems ◽

The Military

AbstractHealthcare is fully embracing the promise of Big Data for improving performance and efficiency. Such a paradigm shift, however, brings many unforeseen impacts both positive and negative. Healthcare has largely looked at business models for inspiration to guide model development and practical implementation of Big Data. Business models, however, are limited in their application to healthcare as the two represent a complicated system versus a complex system respectively. Healthcare must, therefore, look toward other examples of complex systems to better gauge the potential impacts of Big Data. Military systems have many similarities with healthcare with a wealth of systems research, as well as practical field experience, from which healthcare can draw. The experience of the United States Military with Big Data during the Vietnam War is a case study with striking parallels to issues described in modern healthcare literature. Core principles can be extracted from this analysis that will need to be considered as healthcare seeks to integrate Big Data into its active operations.

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Exploring data governance in big data age

Systems and Computer Technology ◽

10.1201/b18777-33 ◽

2015 ◽

pp. 189-194

Keyword(s):

Big Data ◽

Data Governance

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Cloud Computing in Singapore: Key Drivers and Recommendations for a Smart Nation

Politics and Governance ◽

10.17645/pag.v6i4.1757 ◽

2018 ◽

Vol 6 (4) ◽

pp. 39-47 ◽

Cited By ~ 2

Author(s):

Reuben Ng

Keyword(s):

Public Policy ◽

Cloud Computing ◽

Big Data ◽

Business Models ◽

Fog Computing ◽

Novel Technologies ◽

Cloud Computing Adoption ◽

Big Data Applications ◽

Key Drivers ◽

The Government

Cloud computing adoption enables big data applications in governance and policy. Singapore’s adoption of cloud computing is propelled by five key drivers: (1) public demand for and satisfaction with e-government services; (2) focus on whole-of-government policies and practices; (3) restructuring of technology agencies to integrate strategy and implementation; (4) building the Smart Nation Platform; (5) purpose-driven cloud applications especially in healthcare. This commentary also provides recommendations to propel big data applications in public policy and management: (a) technologically, embrace cloud analytics, and explore “fog computing”—an emerging technology that enables on-site data sense-making before transmission to the cloud; (b) promote regulatory sandboxes to experiment with policies that proactively manage novel technologies and business models that may radically change society; (c) on the collaboration front, establish unconventional partnerships to co-innovate on challenges like the skills-gap—an example is the unprecedented partnership led by the Lee Kuan Yew School of Public Policy with the government, private sector and unions.

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Data Lake Ecosystem Workflow

10.21079/11681/40203 ◽

2021 ◽

Author(s):

R. Salter ◽

Quyen Dong ◽

Cody Coleman ◽

Maria Seale ◽

Alicia Ruvinsky ◽

...

Keyword(s):

Big Data ◽

Language Processing ◽

Data Analytics ◽

Large Scale ◽

Big Data Analytics ◽

Lake Ecosystem ◽

Data Governance ◽

Government Organizations ◽

Large Scale Data ◽

Scale Data

The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer between government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.

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Moving from Big Data to Smart Data for Enhanced Performance, Business Efficiency, and New Business Models

Journal of International Business and Management ◽

10.37227/jibm-2020-02-16 ◽

2020 ◽

Keyword(s):

Big Data ◽

Business Models ◽

Business Efficiency ◽

New Business ◽

Smart Data ◽

New Business Models

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Data governance is diverging across largest markets

Emerald Expert Briefings ◽

10.1108/oxan-db260023 ◽

2021 ◽

Keyword(s):

Business Models ◽

The United States ◽

Social Value ◽

Data Governance ◽

Content Type ◽

Technology Sector ◽

Privacy Law ◽

Data Framework ◽

The Uk ◽

Individual Human

Significance The UK survey captures a sentiment now common across many countries that while data-enabled business models, products and services have generated tremendous economic and social value, they have also created new risks for privacy and data monopolies. Three of the largest digital markets -- the United States, Europe and China -- are responding with new frameworks for personal and business data, their responses profoundly influenced by their context. Impacts Concerns about individual human rights will guide the EU’s future revisions to the GDPR. China’s data framework will prioritise national security and the Communist Party’s interests. A US federal privacy law would seek to balance the interests of its technology sector and of online consumers.

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