The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences

✍ Scribed by Rob Kitchin

• Publisher: SAGE Publications
• Year: 2014
• Tongue: English
• Leaves: 285
• Category: Library

✦ Synopsis

Traditionally, data has been a scarce commodity which, given its value, has been either jealously guarded or expensively traded. In recent years, technological developments and political lobbying have turned this position on its head. Data now flow as a deep and wide torrent, are low in cost and supported by robust infrastructures, and are increasingly open and accessible.

A data revolution is underway, one that is already reshaping how knowledge is produced, business conducted, and governance enacted, as well as raising many questions concerning surveillance, privacy, security, profiling, social sorting, and intellectual property rights.

In contrast to the hype and hubris of much media and business coverage, The Data Revolution provides a synoptic and critical analysis of the emerging data landscape. Accessible in style, the book provides:

A synoptic overview of big data, open data and data infrastructures
An introduction to thinking conceptually about data, data infrastructures, data analytics and data markets
Acritical discussion of the technical shortcomings and the social, political and ethical consequences of the data revolution
An analysis of the implications of the data revolution to academic, business and government practices

✦ Table of Contents

Chapter 1: Conceptualising Data
What are data?
Kinds of data
Data, information, knowledge, wisdom
Framing data
Thinking critically about databases and data infrastructures
Data assemblages and the data revolution
Chapter 2: Small Data, Data Infrastructures and Data Brokers
Data holdings, data archives and data infrastructures
Rationale for research data infrastructures
The challenges of building data infrastructures
The challenges of building data infrastructuresData brokers and markets
Chapter 3: Open and Linked Data
Open data
Linked data
The case for open data
The economics of open data
Concerns with respect to opening data
Chapter 4: Big Data
Volume
Exhaustive
Resolution and indexicality
Relationality
Velocity
Variety
Flexibility
Chapter 5: Enablers and Sources of Big Data
The enablers of big data
Sources of big data
Directed Data
Automated data
Volunteered data
Chapter 6: Data Analytics
Pre-analytics
Machine learning
Data mining and pattern recognition
Data visualisation and visual analytics
Statistical analysis
Prediction, simulation and optimization
Chapter 7: The Governmental and Business Rationale for Big Data
Governing people
Managing organisations
Leveraging value and producing capital
Creating better places
Chapter 8: The Reframing of Science, Social Science and Humanities Research
The fourth paradigm in science?
The re-emergence of empiricism
The fallacies of empiricism
Data-driven science
Computational social sciences and digital humanities
Chapter 9: Technical and Organisational Issues
Deserts and deluges
Access
Data quality, veracity and lineage
Data integration and interoperability
Poor analysis and ecological fallacies
Skills and human resourcing
Chapter 10: Ethical, Political, Social and Legal Concerns
Data shadows and dataveillance
Privacy
Data security
Profiling, social sorting and redlining
Secondary uses, control creep and anticipatory governance
Modes of governance and technological lock-ins
Chapter 11: Making Sense of the Data Revolution
Understanding data and the data revolution
Researching data assemblages
Final thoughts

✦ Subjects

Библиотека;Компьютерная литература;Алгоритмы и структуры данных;