Big, Open and Linked Data: Effects and Value for the Economy (Business Information Systems)

✍ Scribed by Krzysztof Węcel

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 267
• Edition: 1st ed. 2022
• Category: Library

✦ Synopsis

This book examines the recent evolution of the concept of data as an economic and managerial phenomenon. The author first describes and discusses open data and then introduces the concept of linked data, with a focus on assets for reuse. Furthermore, he addresses the main challenges of big data. Value is identified as the main incentive for the adoption of linked data; accordingly, the next two chapters study sources of data value from a macroeconomic and micro economic perspective, respectively. This contributes to the systematization of important issues at the crossroads of enterprise data and data sharing: data ownership, personal data, and data privacy. In turn, the book reveals the role of innovation as a main vehicle for creating value by unifying big, open, and linked data. It studies the ways in which value can be created, transferred, and captured in the form of business models, before the closing chapter verifies the data unification model by combining open and linked geographical data with big data from a major telecom company.

✦ Table of Contents

Contents
List of Figures
List of Tables
1 Introduction
1.1 Background and Motivation
1.2 Objectives and Research Hypotheses
1.3 Structure of the Book
Reference
2 Open Data as an Economic, Political, and Technical Phenomenon
2.1 Introduction
2.2 Open Data Movement
2.2.1 Open Data Definition
2.2.2 Open Government Data Principles
2.2.3 Open Data as Infrastructure
2.3 Open Data Initiatives
2.3.1 European Data Economy
2.3.2 International Activities
2.4 Open Data Supply
2.4.1 Obligation to Provide Information
2.4.2 Open Government Data Publishing
2.5 Adoption of Open Data
2.5.1 Open Data Complexity
2.5.2 Barriers for Adoption
2.6 Macroeconomic Information
2.6.1 Statistical Data
2.6.2 Industry Classifications
2.6.3 Open Geographical Data
2.7 Summary
References
3 Linked Data for Enrichment of Data Assets
3.1 Introduction
3.2 Linked Data Definition
3.2.1 Definition
3.2.2 Features of Linked Data
3.2.3 Linked Data Life Cycles
3.2.4 Linked Data Contribution
3.3 Linked Data Assets for Reuse
3.3.1 People and Organizations
3.3.2 Vocabularies for E-Business
3.3.3 Geospatial Data
3.4 Contexts and Disambiguation
3.4.1 Background Knowledge
3.4.2 Contextual Ontologies
3.5 Quality of Data
3.5.1 Classification of Quality Issues
3.5.2 Data Curation and Repair
3.6 Discoverability of Datasets
3.6.1 Data Profiling
3.6.2 Dataset Annotation and Cataloging
3.6.3 Discovery of Vocabulary
3.6.4 Vocabularies for Description of Datasets
3.7 Summary
References
4 Big Data Organization Challenge
4.1 Introduction
4.2 Contemporary Solutions for Data Organization
4.2.1 Types of Data in Organizations
4.2.2 Time, Value, and Analytics
4.3 Big Data Definition
4.4 Towards Big Data Understanding
4.4.1 Big Data Issues
4.4.2 Big Data Granularity and Self-Similarity
4.4.3 Privacy, Ethical, and Social Issues
4.4.4 Visualization and Big Data
4.5 Data Resources
4.5.1 Big Versus Open
4.5.2 Big Data and Semantics
4.5.3 Alternative Data
4.6 Data Unification Challenge
4.6.1 External Data Integration
4.6.2 Wiig Knowledge Management Model
4.6.3 New Theory of Data
4.6.4 Variety and Discoverability
4.7 The Model for Linked-Data-Based Unification of Data
4.7.1 General Benefits of Linked Data
4.7.2 Emerging Structuring
4.7.3 Specific Contribution of Linked Data
4.7.4 Validity of the Model
4.8 Modern Enterprise Solutions Leveraging Linked Data
4.8.1 Data Governance
4.8.2 Data Lakes
4.8.3 Semantic Compliance
4.8.4 Enterprise Knowledge Graphs
4.9 Summary
References
5 Macroeconomic Aspects of Data Value
5.1 Introduction
5.2 Macroeconomic Impact
5.2.1 Statistics Collected
5.2.2 Public Sector Information
5.2.3 Benefits by Sectors
5.2.4 Data as Infrastructural Resource
5.2.5 Costs, Investments, and Pricing
5.3 Direct Value
5.3.1 Value of Information
5.3.2 Value of Big Data
5.3.3 Value of Open Data
5.3.4 Value of Linked Data
5.3.5 Value of Alternative Data
5.4 Multiplier Effects
5.4.1 Returns to Scale and Returns to Scope
5.4.2 Network Effects and Two-Sided Markets
5.4.3 Disruptive Innovation
5.5 Summary
References
6 Microeconomic Aspects of Data Value
6.1 Introduction
6.2 Stakeholders
6.2.1 Open Data Ecosystem
6.2.2 Demand and Supply
6.2.3 Joint Production
6.3 Mutual Benefits
6.3.1 Community Involvement
6.3.2 Value Networks
6.3.3 Data-Sharing Economy
6.4 Data Ownership
6.4.1 Access to Data
6.4.2 Ownership Roles
6.4.3 Open Algorithms
6.5 Economics of Personal Data and Privacy
6.5.1 Role of Regulations
6.5.2 Secure Sharing of Information
6.5.3 Value of Customer Data
6.5.4 Benefits, Costs, and Externalities of Disclosed Data
6.6 Innovation as Value
6.6.1 Analytics as a Product
6.6.2 Data-Driven Innovation
6.6.3 Open Innovation
6.7 Summary
References
7 Business Models for Data
7.1 Introduction
7.2 Digital Disruption and Social Business Transformation
7.3 Business Models Research
7.3.1 Definition
7.3.2 Frameworks
7.3.3 Business Model Innovation and Evolution
7.4 Literature Review Methodology
7.4.1 Research Objectives
7.4.2 Data Collection and Search Process
7.4.3 Results Overview
7.5 Analysis of Business Model Components
7.5.1 Value Creation
7.5.2 Value Transfer
7.5.3 Value Capture
7.6 Analysis of Relevant Business Models
7.6.1 Business Models for Data Assets
7.6.2 Business Models and the Web
7.6.3 Business Models for Linked Data
7.7 Discussion
7.7.1 Study of Business Models
7.7.2 Real-World Applications
7.7.3 Intellectual Property Issues
7.7.4 Markets and Ecosystems
7.8 Summary
References
8 Geographical Profiling with Linked Data
8.1 Introduction
8.2 Spatial Information
8.3 Mobile Data
8.4 Geographical Linked Data-Based Profiling
8.4.1 Characteristics of Base Transceiver Stations
8.4.2 TF-IDF Weighting Schema
8.4.3 Characteristics of Users
8.5 Advanced Geographical Profiling with Latent Variables
8.5.1 Data Flows
8.5.2 Tools
8.5.3 Latent Dirichlet Allocation
8.5.4 BTS Profiling Results
8.5.5 User Profiling Results
8.6 Summary
References
9 Conclusions