| Big data (1070) |  |
| Big data analytics (28) | |
| Hadoop (28) | |
| Massive data (8) | |
| Big data (1070) | |
| Big data analytics (28) | |
| Hadoop (28) | |
| Massive data (8) | |
mobile, cloud and big data DIG 12. The topics of Digiworld 2013 were connected objects, video as a service, digital malls and digital money, smart city and digital living, future Internet and games.
Big data offers unified access to information. It allows the large-scale dissemination, analysis and use of data for the benefit of consumers and citizens.
Social networks, especially Facebook, are another contributor to big data. All these data are stored in data centers that must be powered and cooled.
Another ethic of publishing on Facebook may considerably decrease the need for big data. Google are said to use 50%less energy than the typical data center.
Bull offer includes big data, the cloud, green IT and digital simulation. The latter is very useful for creating a â€oegreener†innovation †it allows us to simulate the potential impact before transformation
such as high-performance computing (HPC), big data, M2m, cloud, security and mobility, dovetail neatly with the most crucial health issues:
Smart city is an intensive user of ICT, intelligent technology, big data, connected objects and others.
Big data will be used to give organizations a competitive advantage in terms of marketing and customer relationships; lawyers will be displaced by e-discovery software that can rapidly determine which electronic documents are relevant to court cases;
†the valuation of big data. These challenges â€oecan be seen as seven critical pillars to initiate in France the process of long-term prosperity and employmentâ€.
15 are concerned with exploring big data, 13 others address the personalized medicine and 10 are concerned with energy storing.
energy storage, recycling of rare metals, exploration of sea resources, vegetable proteins and plant chemistry, personalized medicine, silver economy and longevity and valorization of Big data.
two involving cross-border and cross-sector collaboration, two on user-centered design, one on big data,
The other cases described in the Open Innovation Yearbook 2014 are about innovation networks such as Oulu Innovation Alliance, Big data exploration, smart urban lighting and innovative services for lawyers.
Big data is collected from devices; we will be able to create our own apps and smart analytics to use them
†big data versus world knowledge base. It is time to switch from â€oequick business, having more and to show what we have†to an awareness about the beauty
-and-its-impact-society video http://www. itif. org/media/big data-cloud-computing-how-it-creating-new-era-disruptive-innovation#video,
such as mobile and real-time technologies, the Internet of things, big data analytics, and social media, have come to the fore in recent years,
New technologies including mobile and real-time technologies the Internet of things, big data analytics, and social media clearly illustrate the enormous impact of IT on society in terms of enabling competitiveness and welfare (vom Brocke, Debortoli, Muâ ller, & Reuter, 2014).
and big data analytics that allow for real-time process decisions based on data available from products in use. Overall, we can observe distinct ways in which BPM can serve as a source of innovation.
Existing big data technology can make information available on a real-time basis and at the same time enable prediction of future events,
Big data and Analytics Information-filled events are generated by a wide variety devices and systems: computers, mobile phones, vehicles, industrial equipment, sensors, security systems, building automation systems,
including complex event processing, pattern analysis and detection, big data processing, predictive analytics and automated decisioning.
The pilot generated in total big data containing 4, 500 driving hours and 250,000 km road vehicle traffic data (Haâ ndel et al.
, cyber physical systems, big data analysis, business intelligence approaches, or process mining provide more and more results in real-time.
100 Automatic layout, 183 B Big data, 3, 7, 10,22, 53,95, 106,250 Bottom up approach, 61 BPM.
and Distributed Co-creation 2. 3 Events, Big data and Analytics 3 The Changing Nature of Work 3. 1 Social BPM 3. 2 Dynamic Processes
including actuaries and statisticians for the purposes of developments in Big data and occupations in engineering, financial services,
for Big data 53 3. 4 Winning Abroad 55 3. 5 Integrated Licensing Application Service 56 3. 6 Local Enterprise Offices 57 3
and our recognised success in Big data and data analytics; ï§Grid integration of renewables, with associated †smart grid†components.
Manufacturing Step Change, National Health Innovation Hub, Competitive Ecosystem for Big data, Winning Abroad, Integrated Licensing Application Service, Local Enterprise Offices, Trading Online,
DJEI, D/Health, EI, Joint Agency Project Team, Oversight Group) 2015 ACTION PLAN FOR JOBS 53 3. 3 Competitive Ecosystem for Big data
The overall ambition of the Disruptive Reform is to build on existing enterprise strengths to make Ireland a leading country in Europe in the area of Big data and Data Analytics.
On behalf of the Task force on Big data, DJEI commissioned a review of Ireland†s progress towards achieving this goal
However in the face of strong European and international competition in this area the Task force has identified a number of new actions that will harness Big data for employment growth.
and develop a specific Big data agenda clarifying its leadership goals; 2. Building on our research strengths consolidate Ireland†s leadership position in Big data/Data Analytics within Horizon 2020
and continue to promote engagement by enterprise in Ireland; 3. Continue to implement the recommendations of the EGFSN€ s report â€oeassessing the demand for Big data and Analytics Skillsâ€;
4. Develop a coherent ecosystem to bridge the gap between R&d and innovation and take-up;
and focus of the Big data Task force will be renewed to provide effective overarching coordination and monitoring to ensure that the strategic goals are achieved.
The Big data market is in an emerging phase of development and in order to achieve the benefits of data-driven innovation,
and will progress a range of actions in 2015 (as set out below) in this regard. 54 2015 Actions Big data 86 Renew the mission
and focus of the Big data Taskforce with the goal to oversee progress towards the strategic goals of the Disruptive Reform.
Task force on Big data, DJEI) 88 Monitor progress annually, based on the KPIS, and produce a report updating/revising the main actions.
Task force on Big data, DJEI) 89 Oversee the implementation of the actions arising from the IDC review
Task force on Big data, DJEI) 90 The Task force on Big data will review the opportunities for Ireland arising from the Internet of things
Task force on Big data, DJEI, IDA) 91 Establish interdepartmental committee on data protection issues and related structures.
In 2015 the Taskforce on Big data will assess the most appropriate policy response to this new and emerging opportunity
and job creation. 2015 Actions Internet of things 352 The Task force on Big data will review the opportunities for Ireland arising from the Internet of things
Task force on Big data, DJEI, IDA) 11.5 Innovative/Advanced Manufacturing By 2020 manufacturing will be different from
and ï§Big data in manufacturing: better use if data is derived from process and product analytics.
-Intellectual Property in Enterprise 3. 3-Competitive Ecosystem for Big data 10-RD&I 11-New Sources of Growth 7. 2 Increase researchers employed in industry Foreign
crowdsourcing and crowdfunding, big data visualisation and analytics, P2p production and consumption, edemocracy and eparticiaption. Crowdsourcing refers to a platform for on-line distributed problems and a network of coordinated human †problem solversâ€.
-Challenges & Opportunities of Big data for the Digital Society. pdf. txt'¥ Perceivced Barriers to Innovtion in SMES. pdf. txt>Policies in support of high growth innovative smes
ê Using big data for the future of personal transportation DATASIM Digital Agenda for Europe Euro. pdf. txt n?
G#2v 7766 Big data G#3v 7767 Big data 0#4#big data Big data G#3v 7768 Big data analytics
0#4#big data analytics Big data analytics G#3v 7769 Hadoop 0#4#hadoop Hadoop G#3v 7770 Massive data
0#4#massive data Massive data G#2v 7771 Computer data storage G#3v 7772 Computer data storage 0#4#computer data storage Computer data storage
G#3v 7773 Data storage 0#4#data storage Data storage G#3v 7774 Memory G#4v 7775 Memory chip
0#5#memory chip Memory chip G#4v 7776 Memory device 0#5#memory device Memory device G#4v 7777 Memory-access
0#5#memory access Memory-access G#4v 7778 Quantum memory 0#5#quantum memory Quantum memory G#2v 7779 Data
0#3#data Data G#2v 7780 Data center 0#3#data center Data center G#2v 7781 Data compression
0#3#data compression Data compression G#2v 7782 Data format 0#3#data format Data format G#2v 7783 Data mining
G#3v 7784 Clustering G#4v 7785 Cluster analysis 0#5#cluster analysis Cluster analysis G#4v 7786 Clustering
x How to guard against decisions being taken about peoples†lives based purely on big data, data
benefits of big data People live their private and public lives in physical localities, although most are today more or less
small to big data depending on case -Civil, voluntary finance & operation -Flat, informal, open bottom-up, self-regulate
x How to address the big data challenges, knowledge generation and use x How to ensure access to relevant data for the use of social enterprises and the potential for Social
x Big data and healthcare-Health communication and health information technology (IT) are central to health care, public health,
the apps only indirectly create network effects by providing big data of interest across all users
by a high intensity of utilisation of novel digital technologies (particularly social, big data, mobile and cloud solutions
Industry 4. 0, 3d, smart services, big data and cloud com -puting. Innovations and new business models are emerging
Ever greater volumes of data (so-called big data) are being interconnected to build smart data, resulting in new
3d, big data, cloud computing and microelectronics zz the initiation of new business models and innovative services by fostering the development and distribution
of big data and cloud applications that offer greater security and data privacy zz reinforcing security and confidence in relation to the
Two centres of excellence for big data are to be established in Berlin and Dresden zz The Federal government is boosting high-performance
types of data processing such as big data, profiling, web tracking or cloud computing to protect privacy 32 VI.
-etary social networks, big data providers implementations of the Internet of things is convenient for users but also â€oelocks us
while the value of big data is often only associated with efficiency and profitability big data can also be used for social good
to improve public services and stimulate inclusive innovation 1. 3 DIGITAL SOCIAL INNOVATION IN THE CONTEXT OF FUTURE
Big data can also be used for social good, to improve public services and stimulate inclusive innovation 18 Growing a Digital Social Innovation Ecosystem for Europe
big data, machine learning, 3d print -ing, online learning and e-petitions The main technological trends in DSI
personal and social data in massive data centres. This can also mean increased surveil -lance, prediction and control of people and the environment.
Big data and cloud computing Collective awareness platforms collective intelligence CENTRALISED TOP-DOWN GRASSROOTS DISTRIBUTED COMPETITION ECONOMIC ENTERESTS
A EU Big data strategy is becoming a pri -ority for the competitiveness of European industries.
Public Private Partnership on big data with industry. The focus is driven business with little attention to societal challenges
Big data and cloud computing Collective awareness platforms collective intelligence CENTRALISED TOP-DOWN GRASSROOTS DISTRIBUTED COMPETITION ECONOMIC ENTERESTS
Big data and cloud companies but also States have a lot of control over an individual†s online identity
3. New governance modalities for big data (main question around collective ownership of data, data portability
an ocean of commercially valuable big data. Citizens should be aware that technical solutions do not work by
Defining sensible governance modalities for big data will require substantial collaboration between the public and private sectors, based on a multi-stakeholder model, in order to define
the minimum level of sensible regulation allowing fair competition in the emerging areas of big data
mobility, big data, cloud computing packaged in new digital government offerings y Adopting an attitude of experimentation and entrepreneurship (government itself
(PPE, by defining sensible governance modalities for big data thorugh a large collaboration between public and private actors;
developers†community, the innovation labs community, the open/big data community, the smart citizen /civic society community,
a EU Big data strategy is becoming a priority for the competitiveness of European industries, and it
In this report, the open/big data community refers to the set of governments, usually at the local level, that
Open/big data (Local) govern -ments Competition organ -izers Networks of develop -ers Open data evange
The open/big data community It has already been stated that the open/big data community includes a set of governments, usually at the local
level, that decide to open their data. Governments are, therefore, the focal actors of this community.
The open/big data community†s enablers connect (local governments with those who are potential users and who will boost innovation.
Open data evangelists are also enablers within the open/big data community. There are organisations that encourage the use of open data.
PWFSOBODF of the open/big data community is top down, that is, governments decide what, when and
This does not mean the open/big data community does not have references. There are outstanding good practices,
Lastly, a lot has been written on open/big data failures. Huijboom & Van den Broek (2012) identified several barriers for open/big data initiatives to progress.
After reviewing open data strategies in several European countries, they describe a closed government culture, privacy legislation, limited quality of data, lack of
Open/big data Organization of competi -tions Support for networking Knowledge sharing and dissemination New services
the open/big data community†s instruments are very similar to the so-called enablers in section X. In particular, in this section we will refer to the organization of
Incentives for the open/big data community should take into account the instruments†flaws and the needs of the community in terms of motivations.
governance modalities for Big data, collective ownership of data, data portability, and how to valorize data as knowledge commons.
an ocean of commercially valuable Big data. Technical Solutions do not work by themselves, therefore legal and commercial solutions have to be based in technology and integrated with the appropriate
Defining sensible governance modalities for big data will requires a large collaboration between public and private actors
big data with collective awareness, while taking into account privacy concerns. The objective would be to
The emerging cloud model,(proprietary social networks, big data providers, the Internet of things im -plementation), are currently following a different model that allows us convenience but at the expense of
crowdfunding, big data, machine learning, 3d printing, online learning, e-petitions and so on Open networks The ability to build bottom-up networking capabilities in every corner or the world and in people†s everyday
data in massive data centres with little privacy and security. The hypothesis of this model is that people
top-down technology-push approaches (e g. supply-side approach to Big data & Big brother. Â Unlike traditional innovation actions, DSI and Collective Awareness Platforms are motivated by the vision
abundance of †big data. †Volunteers are presented with a series of image or †slidesâ€.
up data analysis to the public) to process big data sets quicker, while simultaneously advancing scientific research. As mentioned above, the
big data with collective awareness, while taking into account privacy concerns. The objective would be to
The emerging cloud model,(proprietary social networks, big data providers, the Internet of things im -plementation), are currently following a different model that allows us convenience but at the expense of
crowdfunding, big data, machine learning, 3d printing, online learning, e-petitions and so on Open networks The ability to build bottom-up networking capabilities in every corner or the world and in people†s everyday
data in massive data centres with little privacy and security. The hypothesis of this model is that people
top-down technology-push approaches (e g. supply-side approach to Big data & Big brother. Â Unlike traditional innovation actions, DSI and Collective Awareness Platforms are motivated by the vision
abundance of †big data. †Volunteers are presented with a series of image or †slidesâ€.
up data analysis to the public) to process big data sets quicker, while simultaneously advancing scientific research. As mentioned above, the
possible by Internet of things technologies and Big data analytics. Therefore, it may be possible that as an effect of technological change the significance of mental categories,
Big data analytics and network-based open policymaking †in decisionmaking and providing services to the citizen.
organisations to leverage Big data analytics (from location-based health data to engaging with public opinion) is likely to have an impact on the evidence base that informs policymaking in governments that
and A. Hung Byers, Big data: The next frontier for innovation, competition, and productivity, Mckinsey Global Institute, 2011
and Jules Polonetsky,"Privacy in the Age of Big data: A Time for Big Decisions,"Stanford
social and big data are already central to business thinking, and the next set of digital technologies, trends, opportunities and threats is creating yet another competitive frontier
sciences, Gfk turns big data into smart data, enabling its clients to improve their competitive edge
through aggressive fraud management and the application of big data analytics Mobile first strategy In 2014, mobile commerce well and truly arrived in Europe.
Big data to monitor risks and identify opportunities Another big trend that is further maturing in 2014 is the application of big data
analytics and visualization to the domain of online payments. E-commerce leaders such as Amazon have been applying big data for years now with the objective of
building sophisticated profiles of their consumers for Conversion Rate Optimization (CRO. And with good reason
the cloud and big data †are transforming the way companies and their customers interact. At
technologies †such as big data, mobile social media and cloud †that marketing and IT have no choice
technologies †such as big data, mobile social media and cloud †that marketing and IT have no choice
analytics, big data, mobility and the cloud or whichever area has the highest benefit for your company, †he says
3d printing, augmented reality and an early government big data initiative to build a digital 117 surveillance system today called PRISM.
into their curricula through the fusion of augmented reality, big data and social media Augmented reality (AR) is a new medium of communication (Craig, 2013.
Research shows big data analytics are an effective tool to enable the professor to make instant adjustments to optimize learning and further diagnostics
-etary social networks, big data providers implementations of the Internet of things is convenient for users but also â€oelocks us
while the value of big data is often only associated with efficiency and profitability big data can also be used for social good
to improve public services and stimulate inclusive innovation 1. 3 DIGITAL SOCIAL INNOVATION IN THE CONTEXT OF FUTURE
Big data can also be used for social good, to improve public services and stimulate inclusive innovation 18 Growing a Digital Social Innovation Ecosystem for Europe
big data, machine learning, 3d print -ing, online learning and e-petitions The main technological trends in DSI
personal and social data in massive data centres. This can also mean increased surveil -lance, prediction and control of people and the environment.
Big data and cloud computing Collective awareness platforms collective intelligence CENTRALISED TOP-DOWN GRASSROOTS DISTRIBUTED COMPETITION ECONOMIC ENTERESTS
A EU Big data strategy is becoming a pri -ority for the competitiveness of European industries.
Public Private Partnership on big data with industry. The focus is driven business with little attention to societal challenges
Big data and cloud computing Collective awareness platforms collective intelligence CENTRALISED TOP-DOWN GRASSROOTS DISTRIBUTED COMPETITION ECONOMIC ENTERESTS
Big data and cloud companies but also States have a lot of control over an individual†s online identity
-mation and allow for harnessing big data to improve healthcare Clinical decision systems assist healthcare providers with decision making task
â€oeinternet of Things, †data analytics and big data, IT-powered robotics, intelligent agents mobile commerce, improved self-serve kiosks, 3d printing, location awareness, and
area of big data analytics Regulations don†t just increase costs†poorly-designed or unresponsive regulations can
learning analytics, big data research, etc. to study in depth the complex'ecosystems'of ICT-ELI 61.8
learning analytics, big data research, etc. to the study of complex'ecosystems'of ICT-ELI 131 61.8
learning analytics, big data research, etc. to the study of complex'ecosystems'of ICT-ELI Supporting research on the perspectives of various actors and stakeholders such as
learning analytics, big data research, etc. to the study of complex'ecosystems'of ICT -ELI 8. 8 4. 6 8. 4 23.7 29.8 32.1 61.8
associated with big data †to better provide timely and relevant evidence for policy-making. Calls for
as well as being a major source of big data in their own right. Big data from mobile operators, for example, are real-time and low-cost
and have one of the greatest development potentials in view of the widespread use and availability of mobile networks and services.
provides the reader with a comprehensive and critical overview of the role of big data from the
Chapter 5. The role of big data for ICT monitoring and for development...173 5. 1 Introduction...
5. 2 Big data sources, trends and analytics...175 5. 3 Telecommunication data and their potential for big data analytics...
181 5. 4 Big data from mobile telecommunications for development and for better monitoring...185 5. 5 Challenges and the way forward...
195 Chapter 5 Annex...207 List of references...213 Annex 1. CT Development Index (IDI) methodology...
5. 1 The five Vs of big data...176 5. 2 An overview of telecom network data...
5. 3 Customer profiling using telecom big data...184 xii List of boxes 1. 1 Final review of the WSIS targets:
5. 1 How big data saves energy †Vestas Wind Systems improves turbine performance...177 5. 2 How Twitter helps understand key post-2015 development concerns...
5. 9 Using mobile big data and mobile networks for implementing surveys...193 List of tables 1. 1 Rural population covered by a mobile-cellular signal, 2012.4
5. 1 Sources of big data...175 1 Measuring the Information Society Report 2014 Chapter 1. Recent information society
and the role of big data for ICT monitoring 1. 2 The voice market In line with developments in recent years, fixed
New data sources could include big data mostly provided by private-sector companies which could help â€oeimprove the timeliness and
The topic of big data is gaining momentum in the statistical community. Chief statisticians gathering at the UNSC meetings in 2013
surveys in a number of countries, big data could provide important sources of more timely and
potentially becoming big data sources as well At the UNSC meeting in 2014, the commission reiterated its call for the global statistical
the use of big data for official statistics. The commission requested the group to include the
of big data for official statistics at regional subregional and national levels •To address the concerns of methodology
data and legislation related to big data •To address the issue of obtaining â€oeaccess at no cost†to big data from the private sector
for official statistical purposes, as well as the issue of access to transborder data or access to data on transboundary phenomena
various types of big data sources and approaches •To develop methodological guidelines related to big data, including guidelines
for all the legal aspects •To formulate an adequate communication strategy for data providers and users on the
issue of use of big data for official statistics •To reach out to other communities especially those more experienced
The UN Global Working group on Big data for Official Statistics was launched formally in June 2014, under the auspices of the UN Statistics
on big data for official statistics; promote practical use of sources of big data for official statistics
provide solutions for methodological, legal and privacy issues; promote capacity building; foster communication and advocacy of the use of big
in the use of private-sector big data for official statistics ICTS are part of the debate on the data revolution
big data and, more broadly, emerging data issues in the post-2015 development debate. First the ICT sector in itself represents a new source
In view of the link between big data and ICTS, work is under way in ITU with a view to contributing to
of exploiting the potential of big data. The focus is primarily on the telecommunication/ICT sector
as a source of big data, including players such as operators and service providers, in the fixed
of big data, in particular data coming from ICT companies; that regulatory authorities should explore the development of guidelines on how
big data could be produced, exploited and stored and that national statistical offices, in cooperation with other relevant agencies, should look into
the opportunities for big data and address current challenges in terms of big data quality Chapter 1. Recent information society developments
30 veracity and privacy within the framework of the fundamental principles of official statistics. 33 The big data approach taken by ITU so far
focuses on the following areas and questions Standardization: 34 •Which standards are required to facilitate
integration in the big data value chain •Which definitions, taxonomies, secure architectures and technology roadmaps
need to be developed for big data analytics and technology infrastructures •What is the relationship between cloud
computing and big data in view of security frameworks •Which techniques are needed for data anonymization for aggregated datasets
•How is exploited big data in different industries; what are the specific challenges faced; and how can these
and should big data be regulated •How does big data impact on the regulation of privacy, copyright and
intellectual property rights (IPR transparency and digital security issues Box 1. 4: What is a data revolution
as big data, geospatial information and geographical information systems •Open data policies should be envisaged to ensure
•What is the link between big data and open data (crowdsourcing, cloud computing, etc •is there need a to regulate data
of big data be prevented and the rights of the data owners protected ICT data collection and analysis
•How can big data complement existing ICT statistics to better monitor information-society developments •Which type of data from ICT companies
produced from big data sources •What are the key issues that need to be addressed, and by whom, in terms of
collecting and disseminating big data in telecommunications •What is the role of national statistical offices and how can big data
complement official ICT data •How can big data from telecommunications inform not only ICT but broader development policy in
real time, leading to prompt and more effective action Chapter 5 of this report addresses some of
34 For further information on the work on big data carried out by the ITU Telecommunication Standardization Bureau (TSB),
http://www. itu. int/en/ITU-T/techwatch/Pages/big data-standards. aspx 35 A background document on big data that was prepared for GSR-14 is available at
http://www. itu. int/en/ITU-D/Conferences/GSR/Pages/gsr2014/default. aspx 35 Measuring the Information Society Report 2014
Chapter 5. The role of big data for ICT monitoring and for development 5. 1 Introduction
In this context, the emergence of big data holds great promise, and there is an opportunity to explore their use in order to complement the
new phenomenon known as big data. At the most basic level it is understood as being data sets whose volume, velocity or variety is very
big data is linked closely to advances in ICTS. In today†s hyper-connected digital world, people
Big data have great potential to help produce new and insightful information, and there is a growing debate on how businesses
benefits of big data. Although it was the private sector that first used big data to enhance
efficiency and increase revenues, the practice has expanded to the global statistical community The United nations Statistical commission
NSOS) are looking into ways of using big data sources to complement official statistics and better meet their objectives for providing timely
value added by big data in the context of monitoring of the information society, and Chapter 5. The role of big data for ICT monitoring and for development
174 there is a need to explore its potential as a new data source. While existing data can provide
society, and that big data have the potential to help realize those efforts In addition to the data produced
on the role and potential of big data when it comes to providing new insights for broader
Big data are already being leveraged to understand socio -economic well-being, forecast unemployment and analyse societal ties.
Big data from the ICT industry play a particularly important role because they are the only stream of big
big data hold great promise for development However, while there are a growing number of research collaborations and promising proof
sharing of big data for development The chapter will first (in Section 5. 2) describe some of the current big data trends and
definitions, highlight the technological developments that have facilitated the emergence of big data, and identify the main
sources and uses of big data, including the use of big data for development and ICT monitoring.
Section 5. 3 will examine the range and type of data that telecommunication companies, in particular mobile-cellular
operators, produce, and how those data are 175 Measuring the Information Society Report 2014 currently being used to track ICT developments
leveraging big data for ICT monitoring and broader development, including in terms of standardization and privacy. It will also make
fully exploiting telecom big data for monitoring and for social and economic development in particular with regard to the different
stakeholders involved in the area of big data from the ICT industry 5. 2 Big data sources, trends
and analytics With the origins of the term â€oebig data†being shared between academic circles, industry and
Sources of big data Sources Some examples Administrative data •Electronic medical records •Insurance records •Tax records
the emergence of big data is the massive â€oedatafication†and digitization, including of human activity, into digital â€oebreadcrumbs†or
big data are generated in digital form from a number of sources. They include administrative records
Big data is not just about the volume of the data. One of the earliest definitions, introduced
big data characteristics such as velocity and variety, in addition to volume (Laney, 2001 â€oevelocity†refers to the speed at
Chapter 5. The role of big data for ICT monitoring and for development 176 term â€oevariety†encompasses the fact that data
Included within the scope of big data is the category of transaction-generated data (TGD), 2
big data is that it is connected directly to human behaviour and its accuracy is generally high
The five Vs of big data Source: ITU at the forefront of extracting value from this
turning towards big data to improve its service delivery and increase operational efficiency. In addition, there are uses for big data in broader
development and monitoring, and there is an increasing focus on big data†s role in producing timely (even real-time) information, as well as
Big data uses by the private and pub -lic sectors Marketing professionals, whose constant aim is to understand their customers,
preferences from the analysis of big data Walmart, the world†s biggest retailer, has been one of the largest and earliest users of big data
In 2004, it discovered that the snack food known as Pop Tarts was purchased heavily by United
value of big data Nor is the private sector†s use of big data techniques restricted solely to market research
Companies and whole industries (healthcare energy and utilities, transport, etc. are using such techniques to optimize supply chains and
How big data saves energy †Vestas Wind Systems improves turbine performance Vestas, a global energy company dedicated to wind energy, with
installations in over 70 countries, has used big data platforms to improve the modelling of wind energy production and identify
By using big data techniques based on a large set of factors and an extended set of structured and
Big data have enabled the creation of a new information environment and allowed the company to manage and analyse
big data techniques to understand and control churn, optimize their management of customer relations and manage their network quality and
Encouraged by the potential of big data to produce new insights and slimmer budgets governments (at all levels) are now looking to
exploit big data and increase the application of data analytics to a range of activities, including
Chapter 5. The role of big data for ICT monitoring and for development 178 complement their official statistics by
Big data for development and ICT monitoring One of the richest sources of big data is the data captured by the use of ICTS.
This broadly includes data captured directly by telecommunication operators as well as by Internet companies and by content
Big data from the ICT services industry are already helping to produce large-scale development insights of relevance to public
data, including big data from the ICT industry is subject to national regulation. In the EU, for
UN Global Pulse, a UN initiative to use big data for sustainable development and humanitarian action, has been mining Twitter data from
Campaign are using big data and visual analytics to identify the most pressing development topics that people around the world
as a source of big data for monitoring purposes Regulators and others are now using the
Chapter 5. The role of big data for ICT monitoring and for development 180 Mobile data
-network big data seems to have the widest socioeconomic coverage in the near term and the greatest potential to produce
Mobile network big data have been utilized to great effect in the area of transportation helping to measure and model people†s
big data with a view to understanding its potential for producing additional information and statistics on the information society
telecommunication big data (either volume velocity or variety) are being considered. Most telecommunication data can be considered as
Not surprisingly, the big data for development initiatives (outlined in Section 2. 2) have mainly drawn on mobile network big data rather than
on those from fixed-telephone operators or ISPS. Figure 5. 2 illustrates some of the similarities
Chapter 5. The role of big data for ICT monitoring and for development 182 Figure 5. 2:
The telecom industry†s use of big data Telecommunication companies are actively seeking to intensify their use of big data analytics
in order to improve existing services and create new ones. For operators, big data open up opportunities for better understanding of their
customers, which in turn leads to improved sales and marketing opportunities. At the same time big data can help optimize network operations
and create new revenue streams and business lines, for example when selling data Customer profiling Telecom operators capture a range of
Chapter 5. The role of big data for ICT monitoring and for development 184 Customer profiles include details about
Big data, on the other hand can help to enhance that classification by enabling analysis of the levels of
Big data techniques can help operators understand churn better by enabling them to model the likelihood
Customer profiling using telecom big data Source: ITU CUSTOMER INTERESTS SOCIO -ECONOMIC CLASS LEVEL OF INFLUENCE OF
subsidiary of SK TELECOM, uses big data to help its parent company to cut churn and generate new
One example is based the US big data startup Cignifi, 19 which obtains data from mobile operators and financial institutions to build credit
5. 4 Big data from mobile telecommunications for development and for better monitoring In 2013, the United nations High-level Panel of
Chapter 5. The role of big data for ICT monitoring and for development 186 statistical systemsâ€,
of a big data working group at the global level UNSC, 2013). 20 Current uses of big data to
complement official statistics are still exploratory but there is a growing interest in this topic, as evidenced by the numerous initiatives being
There are many big data sources that can be used to monitor and assess development results. In a world where mobile telephony is
mobile telecommunication big data have unique potential as a new data source, with high mobile -cellular penetration levels and the increasing
telecommunication big data have potential as a source to enable monitoring of the information society, although they have yet to assume a critical
potential of big data to complement its existing and often limited, set of ICT statistics. This section
big data could complement existing ICT indicators to provide a more complete comprehensive and up-to-date picture of the
Mobile phone big data for develop -ment Mobile data offer a view of an individual†s behaviour in a low-cost, high-resolution, real
Big data for disaster management and syndromic surveillance21 Mobility data collected immediately after a disaster can in many cases help emergency
Chapter 5. The role of big data for ICT monitoring and for development 188 Big data for better transportation planning
A data-centric approach to transportation management is already a reality in many developed economies.
potential of big data in tracking population movements Source: Bengtsson et al. 2011 Figure Box 5. 5:
results of a big data analysis using mobile-phone data (left-hand map) underscore the merits of big data.
The image on the left based on mobile-phone data, depicts the relative population density in Colombo city and its surrounding regions at 1300
Mobile big data (left) versus official survey data (right Both passive and active positioning data are used to
Chapter 5. The role of big data for ICT monitoring and for development 190 Big data for socioeconomic analysis
Data from mobile operators can provide insights in the areas of economic development and socioeconomic status, often in near real time
Big data techniques can therefore complement official statistics in the intervals between official surveys, which are usually relatively expensive
from big data sources may help to fill in the gaps rather than replace official surveys. It should
also be noted that mobile network big data are one of the few big data sources (and often the only one) in developing economies that
contain behavioural information on low-income population groups Frias-Martinez et al. 2012) developed a mathematical model to map human mobility
example of how mobile big data can be used for the unbanked is Cignifi, a big data startup that uses
the mobile phone records of poor people to assess their creditworthiness when they apply for a loan
Big data for understanding societal structures Social-network studies relying on self-reporting relational data typically involve both a limited
Cignifi, a big data startup, has developed an analytic platform to provide credit and marketing scores for consumers, based on
Chapter 5. The role of big data for ICT monitoring and for development 192 have been used to study the geographic
Big data to monitor the information society There is a case to be made for analysing data captured by telecommunication operators in
big data analytics. 25 The core indicators on ICT infrastructure and access include indicators on mobile-cellular and
networks and mobile big data could be used to identify alternative, less costly and faster ways of
which big data can be used to overcome the shortcomings of existing key ICT indicators and to provide additional insights into ICT access
Big data could help in obtaining more granular information in several areas, and big data techniques could be applied to existing
data to produce new insights. In particular operators†big data could produce information in the following areas
Individual subscriber characteristics: Additional categorization across both time and space are possible for subscription indicators, and big
Using mobile big data and mobile networks for implementing surveys An important measurement for assessing the development
using big data analytics. In 2011, for example, UN Global Pulse partnered with Jana, 27 a mobile-technology company,
Chapter 5. The role of big data for ICT monitoring and for development 194 create new information.
By applying big data techniques to survey data and administrative data from operators, new insights could be derived, in particular, in respect
Big data techniques could help extrapolate the actual number of unique mobile subscribers or users rather than just subscriptions, by comparing
In sum, relatively simple big data techniques can help analyse and provide complementary information on existing ICT data, and provide
combining data from surveys with big data to build new correlation and predictive analytic techniques Finally, it should be noted that the methods that
and for other big data for development projects, big data analysis cannot replace survey data, which is needed to build and
test correlations and to validate big data results While the opportunities discussed above present what is analytically possible, data access
and privacy considerations are complex and nuanced, and therefore place constraints on what is practically feasible or advisable.
and interoperability of big data analytics, as well as with privacy and security. Addressing such privacy and other concerns with respect
important for big data producers and users to collaborate closely in that regard. This includes raising awareness about the importance and
to exploit fully the potential of big data for development Data curation, standardization and continuity Data curation and data preparation help to
85 per cent of big data are estimated to be unstructured (Techamerica Foundation, 2012 Dealing with large heterogeneous data sets calls
big data for development and monitoring and to guarantee its continuity, the creation of a semantic framework would require greater
Chapter 5. The role of big data for ICT monitoring and for development 196 Accessing and storing data, and data
Big data for development is still in its nascent stages and, as such, comes with its share of
Until holders of big data become more comfortable about their release it is going to be difficult for third-party research
big data, but it has taken them considerable time to build and leverage the necessary relationships with operators.
when it comes to telecom network big data Some have argued that NSOS are placed well to ensure that best practices are followed in
the collection and representation of big data and to provide a stamp of trust for potential
big data is linked closely to advances in the ICT sphere, including the falling cost of data storage
Chapter 5. The role of big data for ICT monitoring and for development 198 being collected and how they will be used.
however, that in a big data world the â€oeinform and consent†approach is woefully inadequate and impractical,
big data for the greater social good. Encryption virtual private networks (VPNS), firewalls, threat monitoring and auditing are some potential
of big data, the World Economic Forum (WEF initiated a global multi-stakeholder dialogue on personal data that advocated a principle-based
privacy and data protection in a big data world the danger is that these questions may take too
use of big data for broader development. Hence a balanced risk-based approach may be required in the context of what is under discussion here
i e. the use of telecom big data for monitoring and development. This does still require the
of big data for development can be â€oesandboxed†with appropriate privacy protections imposed on researchers, while still ensuring that the broader
In the big data paradigm, it is easy to overlook that concept, given the expectation that when dealing with vast volumes of (often
generalizability of the big data findings Data provenance and data cleaning Understanding data provenance involves tracing the pathways taken by data from the
data that feed into the big data analyses. This is no simple feat. Nor, given the varied sources of
Chapter 5. The role of big data for ICT monitoring and for development 200 population whose behaviour has been captured
big data, which mostly fall under this category may be less susceptible to self-censorship and persona development,
In a way, big data analyses of behavioural data are subject to a form of the Heisenberg uncertainty principle, whereby
considering big data analyses for monitoring purposes. Dr Nathan Eagle, a pioneer in the use of cellphone records to understand phenomena
-data analyses based on big data. For example prior research had established a power-law distribution between the frequency of airtime
the big data paradigm, leading to the discovery of misleading patterns. As Google†s Chief Economist, Hal Varian, notes, â€oethere are often
) Big data draws many of its techniques from machine learning, which is primarily about correlation and predictions. 40 Big data are by
their very nature observational and can measure only correlation and not causality. Supporters of big data have predicted the end of theory and
hypothesis-testing, with correlation trumping causality as the most relevant method (Anderson 2008; Mayer-Schã nberger and Cukier, 2013
mining of big data sources) will not drown out traditional deductive science (i e. hypothesis testing), even in a big data paradigm.
Among the three Vs in the traditional big data definition volume and variety produce countervailing forces.
More volume makes big data induction techniques easier and more effective, while more variety makes them harder and less effective
It is this variety issue that will ensure the need for explaining behaviour (i e. deductive science
Chapter 5. The role of big data for ICT monitoring and for development 202 rather than merely predicting it (Mullainathan
Causal modelling is possible in a big data paradigm by conducting experiments. Telecom network operators themselves use such
2012a), big data are most useful as a basis for encouraging timely investigation, rather than as a replacement
to be important to building the big data models and for periodic benchmarking so that the models can be tuned fine to reflect ground
and replicability problems with big data research The fact that the original private data may in many cases not be available to everyone
volumes of big data calling for computer science and decision-analysis skills that are not emphasized in traditional statistical courses
to leverage big data for development will face competition from the private sector when seeking to attract the right talent.
big data to complement official statistics, have a shortage of advanced analytical skills by comparison with developed economies.
Current research suggests that new big data sources have great potential to complement official statistics and produce insightful
of big data will have to overcome a number of barriers. This includes the development of models which protect user privacy while still
for using big data to complement official ICT statistics. Although this report highlights some of the big data sources and techniques that
could be used, further research is needed to understand and confirm the usefulness of big data sources for monitoring the information
for big data producers and big data users to collaborate and to initiate a dialogue to identify opportunities and understand needs
Since many of the big data sources lie within the private sector, close cooperation between NSOS, on the one hand
talk to commercial vendors of big data analytics In addition, operators and Internet companies can benefit greatly from engagement with
to leverage big data for different purposes Such engagement will also broaden their understanding of the limitations and assist them
Chapter 5. The role of big data for ICT monitoring and for development 204 privacy framework, in consultation with other
big data sources has great potential to increase added value and produce new insights. There is scope for exploring established models for such
can use big data to identify areas where rapid intervention may be necessary, to track progress and make sure their decisions are based evidence
big data and have set up communities of practice and working groups to study their use and potential impact (UNSC 2013
setting big data standards. To this end, national regulatory authorities (NRAS) and NSOS, in consultation with other national stakeholders
big data clearing houses that promote analytical best practices in relation to the use of big data for complementing official statistics and for
development. Those standards, which NSOS are in the best position to enforce, would also have
handle big data, while at the same time investing in the necessary computational infrastructure As the main regulatory interface to the telecom
big data may be leveraged for social good Regulators have a role to play in facilitating the introduction of legislation that addresses privacy
While the use of big data can help better decision-making through probabilistic predictions, this information should not be used against citizens
can utilize big data predictions •Fostering big data competition and openness: Regulators could foster big data competition in increasingly
concentrated big data markets, including by ensuring that data holders allow others to access their data under fair and
reasonable terms 205 Measuring the Information Society Report 2014 International stakeholders International stakeholders †including UN
agencies and initiatives (such as ITU and UN Global Pulse), the Partnership on Measuring ICT for Development, ICT industry associations
and producers of big data (Google, Facebook etc.)) †have an important role globally. More work is needed to understand fully the potential
of big data and examine the challenges and opportunities related to big data in the ICT sector. To this end, the key international
stakeholders have to work together to facilitate the global discussion on the use of big data
UN Global Pulse, as one of the main UN initiatives exploring the use of big data,
can do much to inform and motivate the discussion on global best practices and the use of big data for
development Where using big data for monitoring the information society is concerned, new partnerships, including public-private
partnerships between data providers and the ICT statistical community, including ITU, could be formed to explore new opportunities and
global discussion on the use of telecom big data for monitoring the information society Together, ITU and UN Global Pulse could
big data, for example by facilitating the standards-setting process. Standardized contracts for obtaining data access as well as
telecommunication big data for social good Academia, research institutes and develop -ment practitioners The research into how telecom data may be
respect to leveraging big data for development They, more than others, have been the first to engage with telecommunication operators
Chapter 5. The role of big data for ICT monitoring and for development 208 Location and movement data
Chapter 5. The role of big data for ICT monitoring and for development 210 1 The report of the UN Secretary-General†s High-level Panel of Eminent Persons on the post-2015 Development Agenda
big data. In addition, the European Statistical System Committee (ESSC) in 2013 adopted the Scheveningen Memorandum on  Big data and Official Statisticsâ,
which acknowledges that Big data represents new opportunities and challenges for Official Statistics, and which encourages the European Statistical System
and its partners to effectively examine the potential of Big data sources, see: http://epp. eurostat. ec. europa. eu/portal/page/portal/pgp ess/0 docs/estat/SCHEVENINGEN
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http://policyreview. info/articles/analysis/big data-big-responsibilities 6 See https://www. google. org/denguetrends
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Chapter 5. The role of big data for ICT monitoring and for development 5. 1 Introduction
5. 2 Big data sources, trends and analytics 5. 3 Telecommunication data and their potential for big data analytics
5. 4 Big data from mobile telecommunications for development and for better monitoring 5. 5 Challenges and the way forward
List of references Annex 1. ICT Development Index (IDI) methodology Annex 2. ICT price data methodology
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