scholarly journals Has demography witnessed a data revolution? Promises and pitfalls of a changing data ecosystem

2021 ◽  
Vol 75 (sup1) ◽  
pp. 47-75
Author(s):  
Ridhi Kashyap
Keyword(s):  
Data Ecosystem

scholarly journals DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

AbstractThe 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.

Trust in open data applications through transparency

2020 ◽  
pp. 146144482097970
Author(s):  
Christian Wiencierz ◽  
Marco Lünich
Keyword(s):  
Smart City ◽  
Smart Cities ◽  
Open Data ◽  
Application Process ◽  
Data Application ◽  
User Data ◽  
Data Ecosystem

Open data provide great potential for society, for example, in the field of smart cities, from which all citizens might profit. The trust of these citizens is important for the integration of various data, like sensitive user data, into an open data ecosystem. In the following study, we analyzed whether transparency about the application of open data promotes trust. Furthermore, we formulated guidelines on how to create transparency regarding open data in an ethical way. Using an open-data-based fictitious smart city app, we conducted an experiment analyzing to what extent communication of the technical open data application process and the ethical self-commitment for the transparent communication of data application affect trust in the app’s provider. The results indicate that the more information users obtain regarding the use of open data, the more trustworthy they perceive the app provider to be, and the more likely they are to use the app.

The Structuration of Digital Ecosystem, Privacy, and Big Data Intelligence

2018 ◽  
Vol 62 (10) ◽  
pp. 1319-1337 ◽  
Author(s):  
Yong Jin Park ◽  
Jae Eun Chung ◽  
Dong Hee Shin
Keyword(s):  
Digital Media ◽  
Personal Data ◽  
Policy Implications ◽  
Surveillance Systems ◽  
Data Practices ◽  
Digital Ecosystem ◽  
Digital Surveillance ◽  
The Status ◽  
Data Surveillance ◽  
Data Ecosystem

This study presents a conceptual model of understanding algorithmic digital surveillance systems, borrowing insight from Giddens, who proposed the notion of structuration as social practices deriving from the intersection between structure and agents. We argue that the status of privacy, or lack of it, is a product of these interactions, of which the personal data practices and related interests constitute the reproduction of a data ecosystem. We trace the process of data production and consumption, dissecting the interactive dynamics between digital media producers (personal data users) and users (personal data producers). Inadequacies, limits, and social and policy implications of data surveillance and its algorithmic reproduction of identities are discussed.

scholarly journals A tipping point for open citation data

2021 ◽  
pp. 1-5
Author(s):  
B. Ian Hutchins
Keyword(s):  
Open Data ◽  
Policy Decision ◽  
Public Domain ◽  
Relative Advantage ◽  
Irreversible Change ◽  
Citation Data ◽  
Commercial Activity ◽  
Policy Decision Making ◽  
Data Ecosystem ◽  
Citation Databases

Abstract Open citation data can improve the transparency and robustness of scientific portfolio analysis, improve science policy decision-making, stimulate downstream commercial activity, and increase the discoverability of scientific articles. Once sparsely populated, public-domain citation databases crossed a threshold of one billion citations in February 2021. Shortly thereafter, the threshold of one billion public domain citations from the Crossref database alone was crossed. As the relative advantage of withholding data in closed databases has diminished with the flood of public domain data, this likely constitutes an irreversible change in the citation data ecosystem. The successes of this movement can guide future open data efforts.

Adapting Big Data Ecosystem for Landscape of Real World Applications

2019 ◽  
pp. 1-14
Author(s):  
Jyotsna Talreja Wassan
Keyword(s):  
Big Data ◽  
Online Education ◽  
Social Networking ◽  
Health Management ◽  
Database Systems ◽  
Internet Age ◽  
Heterogeneous Datasets ◽  
The World ◽  
Relational Database Systems ◽  
Data Ecosystem

Big data is revolutionizing the world in the internet age. The wide variety of areas like online businesses, electronic health management, social networking, demographics, geographic information systems, online education, etc. are gaining insight from big data principles. Big data is comprised of heterogeneous datasets which are too large to be handled by traditional relational database systems. An important reason for explosion of interest in big data is that it has become cheap to store volumes of data and there is a major rise in computation capacity. This chapter gives an overview of big data ecosystems comprising various big data platforms useful in today's competitive world.

Improving Forecasting for Customer Service Supply Chain Using Big Data Analytics

2018 ◽  
pp. 25-41 ◽  
Author(s):  
Kedareshwaran Subramanian ◽  
Kedar Pandurang Joshi ◽  
Sourabh Deshmukh
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Customer Service ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Resource Planning ◽  
Road Map ◽  
Service Supply Chain ◽  
Data Ecosystem ◽  
Manufacturing Organization

In this book chapter, the authors highlight the potential of big data analytics for improving the forecasting capabilities to support the after-sales customer service supply chain for a global manufacturing organization. The forecasting function in customer service drives the downstream resource planning processes to provide the best customer experience at optimal costs. For a mature, global organization, its existing systems and processes have evolved over time and become complex. These complexities result in informational silos that result in sub-optimal use of data thereby creating inaccurate forecasts that adversely affect the planning process in supporting the customer service function. For addressing this problem, the authors argue for the use of frameworks that are best suited for a big data ecosystem. Drawing from existing literature, the concept of data lakes and data value chain have been used as theoretical approaches to devise a road map to implement a better data architecture to improve the forecasting capabilities in the given organizational scenario.

Blending Technology, Human Potential, and Organizational Reality

Big Data ◽  
2016 ◽  
pp. 1422-1451
Author(s):  
Jurgen Janssens
Keyword(s):  
Big Data ◽  
Added Value ◽  
Human Potential ◽  
Project Approach ◽  
True Value ◽  
Big Picture ◽  
Data Ecosystem ◽  
Definition Of ◽  
Complementary Actions ◽  
Organizational Reality

To make the deeply rooted layers of catalyzing technology and optimized modelling gain their true value for education, healthcare or other public services, it is necessary to prepare well the Big Data environment in which the Big Data will be developed, and integrate elements of it into the project approach. It is by integrating and managing these non-technical aspects of project reality that analytics will be accepted. This will enable data power to infuse the organizational processes and offer ultimately real added value. This chapter will shed light on complementary actions required on different levels. It will be analyzed how this layered effort starts by a good understanding of the different elements that contribute to the definition of an organization's Big Data ecosystem. It will be explained how this interacts with the management of expectations, needs, goals and change. Lastly, a closer look will be given at the importance of portfolio based big picture thinking.

Mobile Big Data

2016 ◽  
pp. 138-158
Author(s):  
Shivom Aggarwal ◽  
Abhishek Nayak
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Value Chain ◽  
Mobile Technologies ◽  
Complex Concept ◽  
Current Trends ◽  
Mobile Big Data ◽  
Challenges And Opportunities ◽  
New Challenges ◽  
Data Ecosystem

Mobile technologies have given rise to tremendous amounts of data in real-time, which can be unstructured and uncertain. This growth can be attributed as Mobile Big Data and provides new challenges and opportunities for innovation. This chapter attempts to define the concept of Mobile Big Data, provide description of various sources of Mobile Big Data and discuss SWAI (Sources Warehousing Analytics Insights) model of Big Data processing. To understand this complex concept, it is important to visualize the Big Data ecosystem, respective players. Moreover, mobile computing, Internet of things, and other associated technologies have been discussed in light of marketing and communications based applications. The current trends in Mobile Big Data and associated value chain help us understand where the next frontiers of innovation are and how one can create value. This is linked to the future aspects of the Mobile Big Data and evolution of technologies from now onwards.

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