2. 6 Big data...64 2. 6. 1 Introduction...64 2. 6. 2 European context...65
Big data and Social media †increase efficiency and reduce the public sector costs in Romania by having a modern administration.
Support for use of Big data in public administration Ministry for Information Society (responsible All Ministries offering
Big data #of applications developed using Big data databases Target: To be defined based on Appendix 5 Methodology Collection of data from multiple
sources ï Paper document (physical environment ï Digital documents ï Points of access to governmental
using Big data databases Target: To be defined based on Appendix 5 Methodology Educate teachers on ICT technologies Ministry of Education
using Big data databases Target: To be defined based on Appendix 5 Methodology #of digitized units of Achieve the minimum contribution to
using Big data databases Target: To be defined based on Appendix 5 Methodology Field of action 3 â€
Big data and Social media Field of Action II ICT in Education, Health Culture and einclusion Field of Action III
SECURITY, CLOUD COMPUTING, OPEN DATA, BIG DATA AND SOCIAL MEDIA 2. 1 EGOVERNMENT AND INTEROPERABILITY 2. 1. 1 Introduction
Support for use of Big data in public administration Operational Indicators Number of public initiatives promoted through social media
2. 6 BIG DATA 2. 6. 1 Introduction Preamble Page 65 of 170 Big data is a concept
which refers to an informational initiative which solves the issue related to processing high amounts of data within a limited interval
The Big data systems may provide information both to governmental organisations and to citizens from different sources which may be identified as follows
The information provided by Big data systems does not include personal information or information restricted by mechanisms of control and confidentiality
Big data Definition Big data is the term for a collection of data sets so large and complex that it becomes difficult to process
using on hand database management tools or traditional data processing applications. There's nothing new about the notion of big data,
which has been around since at least 2001. Big data is related with the information owned by your private organization
or public institution obtained and processed through new techniques to produce value in the best way possible
organizations that are best able to make real time business decisions using Big data solutions will thrive
Big data, a general term for the massive amount of digital data being collected from all sorts of sources,
and business reports have proposed ways governments can use big data to help them serve their citizens
At the European level, the improvement of the analytics and data processing, especially Big data, will allow to
has drafted a Strategic Research and Innovation Agenda (SRIA) on Big data Value for Europe. The objective of the SRIA is to describe the main research challenges
Big data Value in Europe in the next 5 to 10 years 2. 6. 3 National context
Big data analytics can improve efficiency and effectiveness across the broad range of government responsibilities, by improving existing processes and operations and enabling completely new ones.
Big data Approach in Romania Concepts Lines of Action Comments Big data-refers to an informational initiative which
solves the issue related to processing high amounts of data an interval included between dozens of Terabytes and several
Use the Big data concepts in order to optimize, reduce costs or bring value added services Example of fields where Big data
project have proven feasible -Health (statistical analysis of cases The Government is increasingly dependant on large variety of
The benefits of leveraging Big data concepts include -Reduced overpayments -Better fraud and abuse -Improve efficiency
Using Big data to manage the information generated by the IT system will help increase transparency and flexibility of the
ï Utilisation of certain Big data technologies for the review of data generated by healthcare informatics system and reporting of these data so that they will stand for management and
on Big data direct direct direct indirect indirect indirect indirect indirect indirect direct direct #of public initiatives promoted by
on Big data direct direct direct indirect indirect indirect indirect indirect indirect indirect indirect Page 168 of 170
-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
150 Mckinsey Global Institute, Big data: The next frontier for innovation, competition and productivity, May 2011; available at:
http://www. mckinsey. com/mgi/publications/big data/index. asp 151 Behavioural targeting or behavioural advertising is used a technique by online publishers and advertisers to
Smart Fabric to Big data: from One Innovation to Two Promising Businesses...111 The Open European Youth Innovation Framework (Openeyifâ¢..
talk about Open Ecosystems, Big data, Youth Innov -ation, Smart Cities and two very special, but inter
especially big data) as driver for future growth The new educational challenges together with the stated incentives can be very impactful when it
extension of many big data projects to get more out of the datasets governed by financials.
-care were to use big data creatively and effectively to drive efficiency and quality, the sector could cre
big data, not including using big data to reduce fraud and errors and boost the collection of tax revenue
Big data, Bigger Digital Shadows, and Biggest Growth in the Far east; IDC; December 2012; Available from:
8) Big data Big Impact: New Possibilities for International Development. World Economic Forum Switzerland: The World Economic Forum;
http://www. weforum. org/reports/big data-big -impact-new-possibilities-international-development http://www3. weforum. org/docs/WEF TC MFS
9) Talbot D. Big data from Cheap Phones (Internet 2013; Available from: http://www. technologyreview. com
R.,Roxburgh C.,Hung Byers A. Big data: The next frontier for innovation, competition, and productivity Mckinsey Global Institute;
Smart Fabric to Big data: from One Innovation to Two Promising Businesses Introduction The Internet of things is now a reality.
usage of Big data but the way we manage the data itself. It is not necessarily the most sexy even we
3) platform, architecture, big data analysis and visualisation solutions for novel sport and health solutions, 4) produce a variety of validated digital
are part of the big data movement, you would say that brainstorming is unreliable. With data-driven
big data game •Thanks to the crisis and existing management techniques, many organisations suffer from
Smart Fabric to Big data: from One Innovation to Two Promising Businesses The Open European Youth Innovation Framework (Openeyifâ
big data, mobile commerce, cost of energy, technology pace and globalisation/localisation Companies must continually innovate
big data, mobile commerce, cost of energy, technology pace and globalisation/localisation Companies must continually innovate
medical data, telemedicine, nano-electronics, opto-electronics, industrial software, Big data GPS, ERP data systems, cloud computing, intelligent wireless networks, cybernetic security
•An R&i program fiche (ICT/Big data) in the online real-time Delphi consultations •Exploration and discovery
•Analysis, management and security of big data •Future internet •Software development technologies, instruments, and methods
Big data, future internet etc. Advanced and nano-materials The R&i program fiches in the smart specializations fields provide †where pertinent â€
areas, such as mobile applications and technology, cyber security, Big data, Internet of the Future, Cloud computing, all of which are crosscutting technologies for any economic
months, to harvest the potential of Big data To meet these challenges, innovation is no longer a simple strategic option
Big data is a goldmine for companies...p. 6 Boosting e-skills in European higher education requires political will at national level...
believe a new wave of big data and smartphone applications has the highest potential in terms of job creation Filling the gaps
Euractiv, and big data can predict crimes before they are committed and earn businesses money Kenneth Cukier is data editor at The
-Schã nberger of Big data: A Revolution That Will Transform How We Live Work and Think.
Euractiv†s James Crisp about what big data can teach us What is big data Well there†s no single definition, which
is probably a good thing, because to define it is to constrain it. Broadly speaking though mankind has more information
great example of how big data can be commodified So big data can be sold Absolutely. In fact big data is a potential
gold mine. There are a few forward-thinking companies who have realised they can sell the data they collect as they go about their
everyday work. It will be a revenue generator In the future I expect to see companies employing data or chief information
realise the enormous potential of big data Will there be an impact on how people work
replaced by big data, but that destruction will also create jobs It†s a demonstrable fact that a computer
is hard to predict the impact of the big data revolution What can policymakers do to ensure
that the power of big data can be exploited The issue of data privacy and protection has been deservedly getting a lot of attention
That isn†t really feasible with big data Continued on Page 7 Euractiv ESKILLS FOR GROWTH SPECIAL REPORT 5-9 may 2014 7
Big data is like a mosh pit or jazz-improv. No one knows what†s coming next
potential of big data We need to move from a notice and consent to a system of consent which allows
What are the dangers of big data Of course there are risks, and there will be challenging questions for us to answer
Big data could be used to predict which people are most likely to commit murder That throws up interesting questions
in a way a crisis of big data. Decisions were made on economic models that turned out to be false
Using big data for the future of personal transportation: DATASIM Published by Newsroom Editor(/digital-agenda/en/users/Newsroom) on 26/11/2014
massive amounts of Big data of various types and from various sources, like GPS, mobile phones and social networking sites.
systems†trends (Part I), trying to examine technological issues such as Big data Cloud computing, Mobile services, etc.
one hand, of Big data, Cloud computing, and Mobile Services for business; on the other hand, it discusses the drivers and challenges of Social Listening and
1 Big data...3 1. 1 Introduction...3 1. 1. 1 Big data Drivers and Characteristics...5
1. 1. 2 Management Challenges and Opportunities...9 1. 2 Case studies...15 1. 3 Summary...
Big data Abstract The role of this Chapter is to introduce the reader to the area of Big
characteristics of Big data, both at technical and managerial level. Furthermore the Chapter aims at investigating management challenges and opportunities
identifying the main phases and actions of a Big data lifecycle. Finally, the discussion of case studies concludes the Chapter,
on factors and strategic points of attention, suitable to support Big data-driven decision making and operational performance
information flows in social networks and potentially see the world as a big data repository to be exploited,
4 1 Big data 1. 1. 1 Big data Drivers and Characteristics The spread of social media as a main driver for innovation of products and services
and the increasing availability of unstructured data (images, video, audio, etc from sensors, cameras, digital devices for monitoring supply chains and stocking
Furthermore, Big data require new capabilities 5 to control external and internal information flows, transforming them in strategic resources to define
Thus, Big data call for a radical change to business models and human resources in terms of information orientation and a unique valorization of a
Nevertheless, as usual with new concepts, also Big data ask for a clarification of their characteristics and drivers.
-acterizing Big data 6†8 Volume: the first dimension concerns the unmatched quantity of data actually
BIG DATA Cloud computing Social networks Internet of things Mobile 80%of the world's data is unstructured
Fig. 1. 1 Big data drivers and characteristics 1. 1 Introduction 5 Velocity: the second dimension concerns the dynamics of the volume of data
namely the time-sensitive nature of Big data, as the speed of their creation and use
considered relevant to Big data characterization: Veracity concerns quality of data and trust of the data actually available at an incomparable degree of volume
Thus, this dimension is relevant to a strategic use of Big data by businesses, extending in terms of scale
on one of the Big data characteristics. As pointed out by Pospiech and Felden 7 at the state of the art, cloud computing is considered a key driver of Big data, for
the growing size of available data requires scalable database management systems DBMS). ) However, cloud computing faces IT managers and architects the choice
6 1 Big data the open source computing framework Hadoop have received a growing interest and adoption in both industry and academia. 5
Considering velocity, there is a debate in academia about considering Big data as encompassing both data ††stocks††and ††flows††14.
For example, at the state of the art Piccoli and Pigni 15 propose to distinguish the elements of digital data
Davenport et al. 14 have pointed out for Big data applications to information flows •Support customer-facing processes:
As a consequence, we believe that the distinction between DDSS and Big data is useful to point out a difference in scope and target of decision making, and
focus on Big data applications As shown in Fig. 1. 2 they cover many industries, spanning from finance (banks
5 See for example how IBM has exploited/integrated Hadoop 42 1. 1 Introduction 7 management. Moreover, marketing and service may exploit Big data for
increasing customer experience, through the adoption of social media analytics focused on sentiment analysis, opinion mining,
As for public sector, Big data represent an opportunity, on the one hand, e g for improving fraud detection as tax evasion control through the integration of a
Thus, Big data seem to have a strategic value for organizations in many industries, confirming the claim by Andrew Mcafee and Erik Brynjolfsson 8 that
believe that management challenges and opportunities of Big data need for further discussion and analyses, the state of the art currently privileging their technical
understanding Big data value from a business and management point of view BIG DATA Applications Public sector Banks /Insurances Marketing
/Services Utilities /Manufacturing Sentiment Analysis Opinion Mining Social media Analytics Recommender systems †Riskanalysis Fraud detection
Fig. 1. 2 Big data applications 8 1 Big data 1. 1. 2 Management Challenges and Opportunities
In the Sect. 1. 1. 1 we have provided a set of drivers and characteristics actually
identifying Big data and their target applications. However, they do not allow yet a clear understanding of the specific actions required for exploiting their research and
Big data seems to be yet another brick in the wall in the long discussion in the information systems field on
hand, Big data change the rules of the game, asking to change the overall infor -mation orientation 22 of an organization (from the separation of stocks and flows
Thus, Big data are different because they actually prompt a rethinking of assumptions about relationships and roles of business and IT, moving information
Therefore, Big data challenges can actually be addressed by actions asking a technological/functional or else a business perspective, depending
fields, contributions to Big data research. In Table 1. 1 we classify these per -spectives with regard to their type and we associate actions they may be suitable to
support in Big data value exploitation Considering, the technological type of perspective, the Technical-Data-Provi
Table 1. 1 Big data perspectives and related actions Perspectives Types Actions Technical-data-provisioning Technological Storage
governing the overall lifecycle from Big data storage to use. Nevertheless, the latter is suitable to be addressed with a Functional-Data-Utilization perspective
and experience in the usage of Big data from state of the art in various disciplines such as, e g.,
Considering now the actions required for exploiting Big data value, Fig. 1. 3 provides a summary of the priority ones together with the related perspective
need actions for Big data management for (i) valuing information asset,(ii understanding costs,(iii) improving data governance practices to extract the right
Fig. 1. 3 Big data management challenges. Adapted from 7 10 1 Big data As for data governance, several approaches have been proposed in the literature
for Data Quality Management (DQM) to face strategic and operational challenges with quality of corporate data 25.
support valuable Big data management The factors considered in Table 1. 2 act at organization, industry, and tech
nature of Big data and the unpredictable dynamics of the digital environment producing them. Furthermore, they often require business process management
business process optimization issues, organization may fail to exploit Big data Indeed, optimization often leads to rigidity and inflexibility of business processes
rely and exploit Big data to develop flexible strategy and business models, thus anticipating and responding to volatility of market and customer needs, while
Furthermore, companies aiming to exploit the opportunities offered by Big data have to connect business agility to information value (axes in Fig. 1. 4), through
The above arguments and cases lead us to the third Big data lifecycle chal -lenge. As for their use,
12 1 Big data management, and workforce planning and allocation. Furthermore, Lavalle et al 28 pointed out that among the impediments to becoming data driven, companies
among the main impediments to a full exploitation of Big data opportunities to business value. However, managers considered as a priority or mandatory premise
-marize the main challenges and IT actions of Big data for business value as follows •Convergence of information sources:
and evolution of Big data as key part of the digital asset of today†s organizations To this end, Fig. 1. 5 shows how digital asset components, i e.,
represent the main Big data sources, alimenting in a volatile and dynamic way the digital asset of an organization,
management of Big data. Indeed, the greater the integration of a company†s information system, the faster the overall planning and control cycles 29
Applying to Big data issues the SIGMA model, that we have proposed in a previous work to improve strategic information governance modeling and
-plete a learning process as coping with IT complexity or in our case with Big data management and use by businesses.
which aim to exploit the opportunities of Big data for business performance and value Decisions Actions
14 1 Big data Taking all the contributions discussed in this section into account, Table 1. 3 summarizes a set of strategy points and recommendations for managerial actions in
building what we call a Big data intelligence agenda. It is worth noting that a relevant factor and challenge has to be considered as the background to the agenda
how strategy points for Big data lifecycle phases in Table 1. 3 have been addressed in practice, emphasizing point of attention and insights for managers
and strategy points for big data lifecycle phases Lifecycle phase Factors Recommendations Strategy points Storage Technology Consolidate corporate databases (internal
aligned with IS strategy for Big data exploitation from social media. The case has been discussed by Moses et al. 31
Using Big data should be enhanced and sup -ported by a business strategy focused and shared by the overall company
16 1 Big data As a consequence, Baharti Airtel, to manage the evolution of the market,
from Big data, with a specific attention to data base technologies. The case analyzes how Nokia, the Finland based global telecommunications company, has
and Hadoop at the core of Nokia†s infrastructure POINT OF ATTENTION: Big data ask for a clear understanding of both
IT Portfolio and data asset, for identifying relevant data from all information sources (internal/external) to be stored,
As reported by Cloudera 33 the centralized Hadoop cluster actually contains 0. 5 PB of data.
servers in Singapore to a Hadoop cluster in the UK data center Nevertheless, Nokia faced also the problem of fitting unstructured data into a
includes Apache Hadoop (CDH), bundling the most popular open source projects 1. 2 Case studies 17 in the Apache Hadoop stack into a single, integrated package.
In 2011, Nokia put its central CDH cluster into production to serve as the company†s information
Finally, we present a case study that shows how a Big data strategy can be implemented in a specific industry.
corporation, is building Big data and analytics capabilities for an ††Industrial Internet††In 2011, GE announced $1 billion investment to build software and expertise
on Big data analytics, launching a global software center in San ramon, Cali -fornia. GE charged William Ruh from Cisco systems to lead the center, devel
-oping software and data science capabilities for GE€ s Big data domain of interest †the industrial Internetâ€
Big data require top management commitment and investments, in particular, on human resources to be focused on data scientist capabilities.
-tion have to be considered as a core target for the success of a Big data strategy
GE envisions Big data as a $30 trillion opportunity by 2030, using a conservative 1%savings in five sectors that buy its
In particular, Big data is strategic for a growing percentage of GE€ s business related to services, such as, e g.,
electric utilities, hospitals to exploit GE€ s Big data expertise, generating big savings, likewise. Thus, human resources and talent management are key issues to
GE Big data strategy The center has a staff of about 300 employees (most of them, characterized as
18 1 Big data 1. 3 Summary In this Chapter, we have discussed the business challenges of Big data as a core
component of the information infrastructure upon which our society is building its own open environment.
main drivers and characteristics of Big data, both at technical and managerial level, emphasizing their differences with regards to, e g.,
Big data lifecycle. As for these issues, the Chapter has pointed out the relevance of ††softscaling††approaches, balancing optimization issues, such as, e g.,
experience and contextual needs for an empathic exploitation of Big data as a digital asset Finally, the Chapter has discussed a set of case studies,
importance of a clear and shared Big data strategy together with investments and focus on human resources for capabilities,
suitable to support Big data-driven decision making and operational performance References 1. Ahituv N (2001) The open information society.
6. IBM (2013) What is big data? http://www-01. ibm. com/software/data/bigdata/./Accessed 9 jul
8. Mcafee A, Brynjolfsson E (2012) Big data: the management revolution. Harv Bus Rev 90 (10:
Agrawal D, Das S, El Abbadi A (2010) Big data and cloud computing: new wine or just new
Agrawal D, Das S, Abbadi A (2011) Big data and cloud computing: current state and future opportunities.
Tallon PP (2013) Corporate governance of big data: perspectives on value, risk, and cost IEEE Comput 46:
Lavalle S, Lesser E, Shockley R, Hopkins MS, Kruschwitz N (2011) Big data analytics and the path from insights to value.
using big data to bridge the virtual & physical worlds 34. Consultancy T (2013) Big data case study:
how GE is building big data, software and analytics capabilities for an ††Industrial Internet. ††http://sites. tcs. com/big data-study
/ge-big data-case-study/./Accessed 20 jul 2013 35. Floridi L (2010) Information: a very short introduction.
Oxford university Press, Oxford pp 1†43 36. Avison DE, Fitzgerald G (1999) Information systems development. In:
Currie WL, Galliers RD (eds) Rethinking management information systems: an interdisciplinary perspective Oxford university Press, Oxford, pp 250†278
20 1 Big data 38. Kharif O (2013) ATMS that look like ipads. Bloom Businessweek, pp 38†39
IBM, Zikopoulos P, Eaton C (2011) Understanding big data: analytics for enterprise class hadoop and streaming data, 1st edn.
Mcgraw-hill Osborne Media, New york References 21 Chapter 2 Cloud computing Abstract During the last decade, the Information and Communication Technol
1 See also Chap. 1 of this book for details on Mapreduce and Big data 26 2 Cloud computing
applications use the Mapreduce frameworks such as Hadoop for scalable and fault-tolerant data processing. However, modeling the performance of Hadoop
tasks (either online or offline) and the adaptive scheduling in dynamic condi -tions form an important challenge in cloud computing
Big data •Amazon†s Dynamo, HBASE, Google†s Bigtable Cassandra, Hadoop, etc Ultra-fast, low latency switches •Cisco Networks, etc
High density, low cost chips •IBM, Intel, AMD chips 50 3 Mobile Services As pointed out by Bagozzi 20,
Increased storage Hyperscale storagea for big data Fast stream processing platforms Platforms such as, e g.,, IBM System S, storing and processing
see also Chap. 1 of this book for storage issues for Big data 4. 5 Social Sensing 79
data or Big data •data quality techniques, enabling, e g.,, the trustworthiness, accuracy, and completeness of data collected through sensors
emphasized in Chap. 1 on Big data References 1. Weill P, Vitale M (2002) What IT infrastructure capabilities are needed to implement
Baeza-Yates R (2013) Big data or Right Data? In: Proceedings 7th Alberto Mendelzon International Work Foundation Data Management Puebla/Cholula, Mex May 21†23
-ioral analysis from intensive data streams as well as from Big data References 1. Christensen CM (1997) The innovator†s dilemma:
trends we have considered the business challenges of Big data as a core com -ponent of the information infrastructure upon which our society is building its own
Big data, 5 C Called technology steward (TS), 118 Campus connect Initiative, 129 Capabilities, 5 Chronological age, 55
Hadoop, 7, 28 Hybrid cloud, 34 Hyperscale storage, 80 I Information, 4 Information aggregation markets (IAMS), 146
1 Big data Abstract 1. 1†Introduction 1. 1. 1 Big data Drivers and Characteristics 1. 1. 2 Management Challenges and Opportunities
1. 2†Case studies 1. 3†Summary References 2 Cloud computing Abstract 2. 1†Introduction 2. 1. 1 Cloud computing:
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