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 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
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.
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
Moreover, marketing and service may exploit Big data for increasing customer experience, through the adoption of social media analytics
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
Big data ask for a clear understanding of both IT Portfolio and data asset, for identifying relevant data from all 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
-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:
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.
1 See also Chap. 1 of this book for details on Mapreduce and Big data 26 2 Cloud computing
Big data •Amazon†s Dynamo, HBASE, Google†s Bigtable Cassandra, Hadoop, etc Ultra-fast, low latency switches •Cisco Networks, etc
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
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:
and J. Maniyka. 2010. â€oeclouds, Big data, and Smart Assets: Ten Tech-Enabled Business Trends to Watch. â€
Rewards and Risks of Big data Beã at Bilbao-Osorio, Soumitra Dutta, and Bruno Lanvin, Editors
Rewards and Risks of Big data Beã at Bilbao-Osorio, World Economic Forum Soumitra Dutta, Cornell University
and Risks of Big data 1. 1 The Networked Readiness Index 2014: 3 Benchmarking ICT Uptake in a World
of Big data Beã at Bilbao-Osorio and Roberto Crotti (World Economic Forum Soumitra Dutta (Cornell University),
of Big data Robert Pepper and John Garrity (Cisco systems 1. 3 Big data Maturity: An Action Plan 43
for Policymakers and Executives Bahjat El-Darwiche, Volkmar Koch, David Meer, Ramez T. Shehadi and Walid Tohme (Booz & Company
1. 4 Big data: Balancing the Risks and 53 Rewards of Data-Driven Public Policy Alex Pentland (MIT
of Big data Matt Quinn and Chris Taylor (TIBCO 1. 6 Rebalancing Socioeconomic 67 Asymmetry in a Data-Driven Economy
of Big data Scott Beardsley, Luã s Enrã quez, Ferry Grijpink, Sergio Sandoval Steven Spittaels, and Malin Strandell-Jansson (Mckinsey &
1. 8 From Big data to Big Social 81 and Economic Opportunities: Which Policies Will Lead to Leveraging Data
1. 9 Making Big data Something 87 More than the â€oenext Big Thing†Anant Gupta (HCL Technologies
of big data and analytics, and the explosive development of the Internet of things (Iot. These transitions are
In capable companies, big data is aligned with their strategies. They invest only in the data gathering that
can be overwhelmed by big data. They can collect a huge volume of information without any predetermined
EXTRACTING VALUE FROM BIG DATA Data have had always strategic value, but with the magnitude of data available today†and our capability to
This new asset class of big data is commonly described by what we call the â€oethree Vs. †Big data is
high volume, high velocity, and includes a high variety of sources of information. Next to those traditional three Vs
and this is why big data today gets so much attention. In the quest for value, the challenge facing us
Big data can take the form of structured data such as financial transactions or unstructured data such as
Big data has been fueled by both technological advances (such as the spread of radio-frequency identification, or RFID, chips
of big data Big data has arrived. It is changing our lives and changing the way we do business.
But succeeding with big data requires more than just data. Data-based value creation requires the identification of patterns from
which predictions can be inferred and decisions made Businesses need to decide which data to use.
This world of big data has also become a source of concern. The consequences of big data for issues
of privacy and other areas of society are not yet fully understood. Some prominent critics, such as Jaron
Moreover, applications of big data in military intelligence have created a growing concern for privacy around the world
into the role of big data and how to extract value from it are included also. These contributions relate to (1) how
the network unleashes the benefits of big data;(2) how and why policymakers and business executives need
to develop action plans to extract value from big data 3) balancing the risks and rewards of big data from a
public policy perspective;(4) managing these risks and rewards;( (5) rebalancing socioeconomic asymmetry in a
building in unlocking the value of big data;(7) turning the potential of big data into socioeconomic results;
and (8 defining organizational change to take full advantage of big data Insights from the NRI 2014 on the world†s
networked readiness Chapter 1. 1 provides an overview of the networked readiness landscape of the world as assessed by the
Unleashes the Benefits of Big data Chapter 1. 2, contributed by Robert Pepper and John Garrity from Cisco systems, details how Internet protocol
facilitating the growth of big data, and networks are fast becoming the key link among data generation, analysis
or encumber, the full impact of big data and the Ioe including standards and interoperability, privacy and
Big data Maturity: An Action Plan for Policymakers and Executives In Chapter 1. 3, Bahjat El-Darwiche, Volkmar Koch
Booz & Company argue that big data has the potential to improve or transform existing business operations
Big data can pave the way for disruptive, entrepreneurial companies and allow new industries to emerge.
to allow big data to show its full potential and to prevent companies from feeling swamped by this information
terms of big data maturity, an approach that allows them to assess progress and identify necessary initiatives
complicated methods for using big data, which can mean simple efficiency gains or revamping a business
with a compelling case for the benefits of big data This means addressing privacy concerns and seeking
facilitates the business viability of the big data sector such as data, service, or IT system providers), and
shortage of big data specialists. As big data becomes ubiquitous in public and private organizations, its use will
become a source of national and corporate competitive advantage Balancing the Risks and Rewards of Data-Driven
are entering a big data world, where governance is far more driven by data than it has been in the past
big data. The key policy recommendations for all large organizations, commercial or government, are that 1. Large data systems should store data in a
using big data to help set and monitor public policy Managing the Risks and Rewards of Big data
In Chapter 1. 5.,Matt Quinn and Chris Taylor from TIBCO argue that expert handling of big data brings the reward
of being able to react to world-changing events, both big and small, at an unprecedented rate and scope
for example, but at the same time, big data brings risks that require balancing those benefits against privacy concerns raised by the potentially unsettling correlation
•Big data leverages previously untapped data sources to liberate information from places where it was hidden previously
•Big data management requires automation wherever possible, because volume and complexity eliminate the ability of humans to intervene and reprogram
•Big data forces us to create adaptable, less fragile data systems because the sheer variety of
•Big data holds unseen patterns, which need to be visualized using analytics tools and techniques
making use of big data in increasingly sophisticated ways. The chapter cites examples in healthcare logistics, and retail where big data is being tackled
with a systems approach that takes into consideration information streaming constantly as well as what is found
big data and get to the core of understanding big data†s risks and rewards Rebalancing Socioeconomic Asymmetry in a
the Value of Big data In Chapter 1. 7, Scott Beardsley, Luã s Enrã quez, Ferry
the expectation that big data will create great benefit for society, companies, and individuals in the coming
might hamper public trust in big data applications and companies and hinder the development of big data to
its full potential. The issues of concern include how to define personal data, how to treat anonymous data
From Big data to Big Social and Economic Opportunities: Which Policies Will Lead to Leveraging Data-Driven Innovation†s Potential
been driven to call this revolution the â€oeage of big data. †However, what is commonly known as â€oebig data†is
Moreover, the term big data is ambiguous: the main features of big data (quantity speed, variety) are technical properties that depend
not on the data themselves but on the evolution of computing, storage, and processing technologies.
is important about big data is not its volume but how it may contribute to innovation
Making Big data Something More than the â€oenext Big Thing†In Chapter 1. 9.,Anant Gupta, Chief executive officer at
of Big data  2014 World Economic Forum  2014 World Economic Forum The Global Information technology Report 2014 3
Big data BEÑAT BILBAO-OSORIO, World Economic Forum ROBERTO CROTTI, World Economic Forum SOUMITRA DUTTA, Cornell University
EXTRACTING VALUE FROM BIG DATA Data have had always strategic value, but with the magnitude of data available today†and our capability to
new asset class of big data is described commonly by what we call the â€oethree Vs. †Big data is high volume
high velocity, and includes a high variety of sources of information. Next to those three Vs we could add a
this is why big data today gets so much attention. In the quest for value, the challenge facing us is how to reduce
the complexity and unwieldiness of big data so that it becomes truly valuable Big data can take the form of structured data such
as financial transactions or unstructured data such as photographs or blog posts. It can be crowd-sourced or
Big data has been fueled by both technological advances (such as the spread of radio-frequency identification, or RFID, chips
of big data Big data has arrived. It is changing our lives and changing the way we do business.
Some examples include the following •Google uses big data to predict the next wave of influenza. 2
•IBM uses data to optimize traffic flow in the city of Stockholm, 3 and to get the best possible air quality
But succeeding with big data requires more than just data. Data-based value creation requires the identification of patterns from which predictions can be
large extent, mastering big data can also be compared to irrigation. It is not enough to â€oebring water†to where it
applied to big data, but this is a resource that could benefit the entire planet instead of just one country
two-thirds of executives feel that big data will help find new market opportunities and make better decisions. 6
Nearly half of the surveyed respondents feel big data will increase competitiveness, and more than a third believe
This world of big data has also become a source of concern. The consequences of big data for issues
of privacy and other areas of society are not yet fully understood. Some prominent critics, such as Jaron
Moreover, applications of big data in military intelligence have created a growing concern for privacy around the world
of data, often referred to as big data, are constantly generated both in a structured and non-structured
more than just the generation of or access to big data Organizations, both public and private, need to decide
same time, the potential of big data to be misused is also increasingly becoming a source of concern.
big data can yield in generating growth and high-quality employment in a rapidly changing context. Designed
Getting More out of Big data. †White paper, sponsored by Oracle and Intel. London, New york, Hong kong, and Geneva
Big data ROBERT PEPPER JOHN GARRITY Cisco systems Exabytes (1018) of new data are created every single day.
big data, and fast becoming the key link among data generation, processing, analysis, and utilization How can we effectively maximize value from this
of big data and the Ioe ACCELERATING DATA PRODUCTION AND DATA TRAFFIC Data growth is skyrocketing.
For governments, Ioe and big data applications are helping to monitor pandemics and environmental conditions, improve public safety and security, and
Ioe will not only fuel the expansion of big data and data transmission, but can also provide targeted, automatic
Big data: Huge and growing data volume from industrial applications Industrial applications of the Internet of Everything (Ioe
At an industrial level, big data analysis can yield very large benefits. For example, the value of modernizing
EQUIPPING IP NETWORKS TO DELIVER BIG DATA INSIGHTS Moving up the knowledge pyramid from data to insights
expansion of big data and the Ioe, technical and policy challenges exist in the ability of current IP networks to
fully exploit big data expansion (Figureâ 3). An approach that tackles these issues concurrently will help to
Policy and technical issues facing big data and the Ioe Standards & interoperability Privacy & security
issues for big data include the reliable prevention of hacking and access by unauthorized and unwanted
and businesses feel safe in engaging in big data activities, network security is essential Over the next five years, the growth of mobile data
and the Ioe and the era of big data are transforming our lives Data flows and the ability to capture value from data
big data and generate added positive impact for society NOTES 1 Palmer 2006 2 The Economist 2010
Beals, B. 2013. â€oethe Big Deal about Big data in Oil and Gas. †Hitachi Available at www. lnm. com. br/bah/downloads/Hitachi bert-Beals
Defining Big data report. September 27. Palo alto Shanghai, Singapore, and Reading, UK: Canalys Cisco. 2012. Cisco Global Cloud Index:
-schoenberger/the-rise-of-big data Danahy, J. 2009. â€oethe Coming Smart Grid Data Surge. †October 5
Big data Bigger Digital Shadows, and Biggest Growth in the Far east. †IDC iview, sponsored by EMC.
/IBM Software. 2012. â€oemanaging Big data for Smart Grids and Smart Meters. †IBM White paper. Somers, NY:
Leber, J. 2012. â€oebig Oil Goes Mining for Big data. †MIT Technology Review, May 8. Available at http://www. technologyreview. com
/news/427876/big-oil-goes-mining-for-big data /Lopez, M. 2013. â€oege Speaks on the Business Value of the Internet of
An Overview. †Going deep on Big data ZDNET special feature, October 1. Available at http://www. zdnet
com/big data-an-overview-7000020785 /Palmer, M. 2006. â€oedata Is the New Oil. †Blog Post, November 3
Big data Maturity An Action Plan for Policymakers and Executives BAHJAT EL-DARWICHE VOLKMAR KOCH DAVID MEER
old. 1 So-called big data has the potential to improve or transform existing business operations and reshape
THE BIG DATA IMPERATIVE If they are to capitalize on this potential, organizations should avoid a common misapprehension.
necessary, it is not sufficient to enable big data to be exploited fully Organizations must instead remold their decision
We propose a Big data Maturity Framework that is based on the experiences of organizations that have undergone a big data transformation.
This framework will allow organizations to assess their progress in this arena and determine what they need
sophisticated, ways to use big data that range from increased efficiency in existing operations to a complete
WHAT IS BIG DATA Big data represents the newest and most comprehensive version of organizations†long-term aspiration to establish
and improve their data-driven decision-making. It is characterized by what are known as the â€oethree Vs††large data volumes, from a variety
in organizations†data warehouses, big data builds on unstructured data from sources such as social media text and video messages,
Big data has the potential to infuse executive decisions with an unprecedented level of data-driven
of big data. For example, in 2012 the Aberdeen Group found that the proportion of executives who reported
Despite the rapid growth of big data, organizations should keep its influence in perspective. Although remarkable, the big data phenomenon is merely
the continuation of a journey in which evermore -elaborate data have influenced decision-making. From organizations†first attempts at data analytics in the
The latest development, big data, may appear all-enveloping and revolutionary. However, the essential principles for exploiting its commercial benefit remain
BIG DATA Chapter 1. 3: Big data Maturity 44 The Global Information technology Report 2014 Â 2014 World Economic Forum
performance or allow them to gain access to new revenue pools This continuation of a trusted managerial approach
culture to exploit the opportunities presented by big data and prepare their own internal capabilities to handle
exploitation of big data THE BUSINESS IMPACT OF BIG DATA Many organizations are still in the early stages of reaping
the benefits of big data. Writing in the Harvard Business Review, Andrew Mcafee and Erik Brynjolfsson explored
the impact of big data on corporate performance. The authors interviewed executives in 330 publicly traded
companies in the United states. They then examined relevant performance data, enabling them to measure the extent to which corporate attitudes toward big data
correlated with how the respective companies were faring Mcafee and Brynjolfsson†s conclusions were remarkable for establishing a connection between
big data and performance: â€oethe more companies characterized themselves as data-driven, the better they performed on objective measures of financial
big data practices has materialized not yet. A 2013 Gartner survey found that less than 8 percent of companies surveyed have deployed actually big data
technology. 5 Investment in forthcoming projects is much more widespread; the research firm IDC has forecasted that
the market for big data technology and services will reach US$16. 9 billion by 2015, up from US$3. 2 billion
according to IDC, are considering big data technology investment in 2013. Although few have actually undertaken large-scale big data or analytics programs
to date, IDC forecasts investment in this area to grow at a compound annual growth rate of over 20 percent over
substantially more on big data projects than the energy sector, and there is far more implementation of big data
initiatives in the United states than in the Asia Pacific region. Meanwhile, the Economist Intelligence Unit found
that big data is enlisted most frequently to assist financial management and marketing/sales, and deemed least
How big data is used The big data maturity stages (Figureâ 2) depict the various ways in which data can be used, from selective
adoption to large-scale implementation. Depending on the maturity of an organization†s big data capabilities big data can significantly increase top-line revenues
and markedly reduce operational expenses. The path to business model transformation, the highest level of maturity, promises potential high returns but often
involves major investment over many years The first maturity stage, performance management enables executives to view their own business more
Advances in operational efficiency through big data such as the efficient deployment of staff resources and the optimization of the supply chain, also reside within
3), organizations start to monetize big data, positioning it as a value driver of the business that offers a new source
Big data Maturity  2014 World Economic Forum deriving insights from it. This may include innovations
Big data maturity stages and related use cases Source: Booz & Company Maturity stages Typical use cases/applications
BIG DATA Chapter 1. 3: Big data Maturity 46 The Global Information technology Report 2014 Â 2014 World Economic Forum
of parking space at all times. The initial results are impressive. Although city parking revenues increased by 2. 4 percent due to higher utilization, 60 percent of
transformation, big data permeates the whole organization. It becomes deeply embedded within the operation, determining the nature of the business and
of a product organization placing great faith in big data GE expects that machinery and equipment will soon
of big data comes from the public sector. Regional and national-level policymakers around the world are
progress through all the big data maturity stages A data-driven business model has been integral to companies such as Google, Facebook, and Twitter
benefit from big data usage. Some of these relate to their own internal systems and culture;
potential of big data as a concept will take organizations only so far. First and foremost, they must get the basics
As big data extends its reach, executive instinct is challenged by the facts of hard data. However, while data can be of great
to significant public reservations about big data. Such concerns about privacy will strengthen demands for tighter regulatory control, potentially limiting companiesâ€
ability to exploit big data opportunities or exposing them to threats of legal and regulatory intervention
HOW TO REACH BIG DATA MATURITY Our big data maturity framework (Figureâ 3) comprises three elements: the enablers of environment readiness
the organization†s internal capabilities, and the different stages of maturity and sophistication in which big data
can be used. The framework enables organizations to view the extent of their success in overcoming obstacles
Big data Maturity  2014 World Economic Forum The environment readiness dimension considers how far the relevant governments have enabled
organizations in their jurisdiction to use big data freely and productively. This is achieved through appropriate regulations and a supportive infrastructure
themselves to extract greater benefits from big data While environment readiness serves as an enabler for big data usage, internal capabilities act as critical
success factors for organizations seeking to progress through the maturity stages The following two sections explain the full range
Big data will soon become ubiquitous practice in both the public and private worlds. Policymakers therefore need to act in a timely manner to promote an
•enable a big data ecosystem by establishing policies to facilitate valid business models for third-party data, service, and information technology system
Big data maturity framework Source: Booz & Company Enablers of environment readiness Success factors for internal capabilities
of big data Traditional applications getting more out of data you already have New horizons of big data
Technical capabilities/infra -structure Regulatory framework for data privacy Dataâ availability andâ governance ICT infrastructure Sponsorship
Big data ecosystem Organizational capabilities and resources Public perception and awareness Data-driven decision-making culture Education/training
Big data Maturity 48 The Global Information technology Report 2014 Â 2014 World Economic Forum Priorities for policymakers will vary in different
big data. In more developed countries, however, the government†s primary concerns should be ensuring transparent regulation and promoting a public-interest
argument for big data Policymakers must make the case for big data In particular, policymakers should set clear rules
regarding data privacy so that organizations know which personal data they can store and for how long, and which data are forbidden explicitly by privacy regulations
skeptical citizens must first be persuaded that big data will work in their favor by paving the way for better
benefits of big data. Indeed, Jules Polonetsky and Omer Tene, in their Stanford Law Review article (2013
offered by big data practices may be the greatest contemporary public policy challenge. 13 The outcome of this debate will vary depending on
harmonization threatens the adoption of big data on an international scale The prevailing patchwork situation accentuates
should navigate the stages of big data maturity. They must each decide for themselves, based on their
•develop a clear (big data strategy •prove the value of data in pilot schemes
•position big data as an integral element of the operating model; and •establish a data-driven decision culture and launch
infrastructure investments for overly ambitious big data projects. Instead, they should select opportunities for high business impact
Big data Maturity  2014 World Economic Forum one such quick win. For example, a mobile phone operator can collect anonymized real-time travel
in establishing big data maturity quickly, while potentially training employees to take on these tasks themselves CONCLUSION
We currently see big data as poised to have significant impact in public and business spaces alike.
-scale investment is flowing into establishing big data capabilities in many organizations, despite the limited number of cases in which it has been used successfully
in their ability to utilize big data to good effect, as seen in their stages of big data maturity.
These differences range from adopting big data practices for operational improvement in selected functional areas or building
or revamping an organization†s value proposition to completely transforming their business model based on big data. At the more advanced stages, organizations
learn to monetize big data far beyond simply getting better at what they are currently doing;
learning this lesson is an accomplishment that can mean a fundamental shift for them. Environment readiness
full potential of big data. For their part, governments throughout the world need to create a supportive
environment for the usage of big data to attract business to their region. Meanwhile, organizations must act
effectively deploy big data. They will have to predict what the world of data-driven insights will look like in the
Within the next five years, big data will become the norm, enabling a new horizon of personalization for both
the game-changing opportunities that big data affords for their societies and organizations, and will provide the
1 IBM, no date. â€oewhat Is Big data? †2 Constine 2012 3 Aberdeen Group 2013
at http://www. aberdeen. com/Aberdeen-Library/8244/RA-big data -trends. aspx Catts, T. 2012. â€oege†s Billion-Dollar Bet on Big data. †Bloomberg
Businessweek, April 26. Available at http://www. businessweek com/articles/2012-04-26/ges-billion-dollar-bet on-big data
Constine, J. 2012. â€oehow Big Is Facebook†s Data? 2. 5 Billion Pieces of Content and 500+Terabytes Ingested Every day. †Tech Crunch
Big data Adoption in 2013 Shows Substance Behind the Hype. †Available at http://www. gartner. com
No date. â€oewhat Is Big data? †Available at http://www. ibm. com /big data ITP. net. 2013. â€oedwtc to Highlight Big data at GITEX:
Event Organiser to Host First Ever Big data Conference on October 22, †September 29. Available at http://www. itp. net/595102-dwtc-to-highlight-big
-data-at-gitex#.#Ukrz9oasiso Mcafee, A. and E. Brynjolfsson. 2012. â€oebig Data: The Management Revolution. †Harvard Business Review, October.
Available at http://hbr. org/2012/10/big data-the-management-revolution Munford, M. 2013. â€oedon†t Follow the Leaders, Watch the Parking
Meters. †The Daily telegraph, September 15. Available at http://www. telegraph. co. uk/technology/news/10307926/Dont
Big data Maturity 50 The Global Information technology Report 2014  2014 World Economic Forum The New york times. 2012. â€oeidc Sizes Up the Big data Market, †March
7. Available at http://bits. blogs. nytimes. com/2012/03/07/idc -sizes-up-the-big data-market/?
/r=0 OECD (Organisation for Economic Co-operation and Development 2013. OECD Guidelines on the Protection of Privacy and
Polonetsky, J. and O. Tene. 2013. â€oeprivacy and Big data: Making Ends Meet. †66 Stanford Law Review 25 september 3. Available at
Big data Maturity  2014 World Economic Forum  2014 World Economic Forum CHAPTER 1. 4
Big data: Balancing the Risks and Rewards of Data-Driven Public Policy ALEX PENTLAND MIT In June 2013, massive US surveillance of phone
the risks and the rewards of this new age of big data address policy issues in this area,
A BIG DATA TAXONOMY It is probably hopeless to try to provide a detailed taxonomy of data types and uses because the
weaknesses of big data analysis are very different from those obtained through standard information sources. To begin to manage our society in a data
With the advent of big data, we can potentially develop many more types of data commons; these commons
To explore the viability of a big data commons, what is perhaps the world†s first true big data commons was
unveiled on May 1, 2013. In this Data for Development D4d) initiative, 90 research organizations from around
Big data: Balancing the Risks and Rewards of Data-Driven Public Policy 54 The Global Information technology Report 2014
The future of big data and governance The Data for Development (D4d) data commons is only a
Big data gives us†for the first time†a chance to view society in all its
to discover how to leverage big data and have been using datasets from companies such as cell phone carriers and
Big data: Balancing the Risks and Rewards of Data-Driven Public Policy  2014 World Economic Forum
the great potential of a big data commons for improving people†s living conditions. From the point of view of
Big data: Balancing the Risks and Rewards of Data-Driven Public Policy 56 The Global Information technology Report 2014
That is, the government must organize big data resources in a distributed manner, with each different type of data separated
a big data government will trample individual freedoms The key insight is that for these types of data systems
Big data: Balancing the Risks and Rewards of Data-Driven Public Policy  2014 World Economic Forum
greatest dangers that companies face in the big data era. A distributed architecture of databases joined with
We are entering a big data world, where governance is driven far more by data than it has been in the past
for organizations that use big data. The key policy recommendations for all large organizations, commercial or government, are that
Big data: Balancing the Risks and Rewards of Data-Driven Public Policy 58 The Global Information technology Report 2014
using big data to help set and monitor public policy NOTES 1 See the D4d challenge, available at http://www. d4d. orange. com
Big data: Balancing the Risks and Rewards of Data-Driven Public Policy  2014 World Economic Forum
and Rewards of Big data MATT QUINN CHRIS TAYLOR TIBCO One of the biggest challenges of the term big data is
deciding on a standard definition of what those words really mean. For many companies that have worked in an
environment of big data is just business as usual. In this chapter we will discuss how managing the growing
companies, big data represents a significant challenge to growth and competitive positioning. In some cases, it
BIG DATA: RISKS AND REWARDS Digitization itself is not new, but the maturation and availability of the Internet;
Big data, in its outsized properties, amplifies those effects. It is in those extremes that the risks and
rewards of big data are decided THREE KEY BIG DATA TRENDS As the world becomes more familiar with big data
three key trends that have a significant impact on those risks and rewards are emerging.
First and foremost big data leverages previously untapped data sources Those sources are of several types. The first includes
wearable devices that stream data about an individual and his or her surrounding environment on a moment
is connected to the Internet, making big data even bigger than human-generated information The third type of sensor provides constant
second trend of big data: the need for automation technologies. Richard Hackathorn wrote about the value
big data. Today, the volume, velocity, and variety of data continue to push the curve down and to the right as
The value-time curve challenge makes big data management a function of creating automation wherever possible.
and big data follows the same path. Big data†s growth in each of its dimensions
The big data conversation often centers on the use of machines as the best resource for the storage and
The third trend being driven by big data is the necessity for adaptable, less fragile systems. For
big data to leverage previously untapped sources of information, organizations need to quickly adapt to the opportunities and risks represented by these new
Automated systems that manage big data ecosystems cannot be developed around rigid schemas that require redevelopment for each new stream of
Managing the Risks and Rewards of Big data 62 The Global Information technology Report 2014 Â 2014 World Economic Forum
BIG DATA Most organizations need to overcome two primary challenges before becoming productive with big data The first is need the for powerful visualization that
allows the business to explore data to find questions worth answering. This stands the traditional business
as the pre†big data model began with the business asking a question and ended with information technology structuring data
Thinking of big data in those terms fails to take into account all of the data being created everywhere, every day.
Managing the Risks and Rewards of Big data  2014 World Economic Forum data†not with the goal of having a larger dataset, but
approach to how big data is being used and apply the right level of oversight. There are two specific reasons for
Many of the risks and rewards of big data are coupled tightly to the use of all of those data.
opportunities for big data to reward us. At the same time, big data comes with privacy concerns that are
not simply related to technology but are also about very human things such as privacy, all-knowing â€oecreepiness, â€
Managing the Risks and Rewards of Big data 64 The Global Information technology Report 2014 Â 2014 World Economic Forum
Throughout the evolution of big data, the capability to govern data appropriately has existed, but unless organizations make the choice themselves
Gaining benefits from big data while mitigating risks is entirely a matter of data systems sophistication. This
successful use of big data The first example of that sophistication is on display at a major network of hospitals in the Midwest to
big data transformation. It has become far more complicated in recent years because of the explosion of data that connect the customer†s customer and the
the management of big data supports a brand†s ability to predict the best product offering
service execution. 4 Big data offers an enormous reward to retail because successful selling is ultimately about
Managing the Risks and Rewards of Big data  2014 World Economic Forum ideal way to gain that access
crucial parts of ensuring the benefits of big data while mitigating its risks. Accomplishing these three objectives
Big data has a remarkable ability to change the world. Its benefits need to be considered as a function
of big data brings the reward of being able to react to world-changing events, both big and small, at an
control to humans†after all, big data should be working for the benefit of humans, not the other way around
Organizations that manage big data have an obligation to monitor security device, server, and application logs, all of which generate machine data
big data moves from low impact â€oeexperiments†to driving real-time operations and decision-making. Although social acceptance of what data can
Big data is a fast-moving technology space that will affect all aspects of our lives
their use of big data NOTES 1 Hackathorne 2004 2 The website for the service is http://mercytelehealth. com/services
Managing the Risks and Rewards of Big data 66 The Global Information technology Report 2014 Â 2014 World Economic Forum
The recent emergence of big data, along with what is being called the â€oedata-driven economy, †may finally
rate of 89 percent. 10 Indeed, the majority of big data will be collected passively and automatically, via machine
Big data then, truly does promise to create new knowledge†and indeed new kinds of knowledge†on which an entirely
Moreover, in the world of big data, it would be impractical if not impossible, for individuals to give express consent
and analytics of big data is how little we actually know about it†its potential risks
concept of fair value exchange in the world of big data The importance to our economic future†to the entire
Big data: The next Frontier for Innovation Competition and Productivity. Mckinsey Global Institute Report May. Available at http://www. mckinsey. com/insights/business
Value of Big data SCOTT BEARDSLEY LUIS ENRIQUEZ FERRY GRIJPINK SERGIO SANDOVAL STEVEN SPITTAELS MALIN STRANDELL-JANSSON
speaking about the value of big data earlier in 2013. As Kroes noted, data comprise a fuel we have only just
Definitions of big data vary greatly. Rather than put a number on what qualifies as â€oebig,
Whatever the precise definition, big data is widely acknowledged to create value in four ways. It creates
Big data can create significant value for the whole economy. Mckinsey research shows that companies that use big data can deliver productivity and profit gains
that are 5 to 6 percent higher than those of competitors The private sector is not the only beneficiary
Big data can also enhance productivity and effectiveness of the public sector and create economic surplus for consumers.
could be reduced by 8 percent by using big data to drive efficiency and quality No wonder, then, that governments and political
institutions are promoting big data on their agendas and adopting initiatives such as the European Union†s
The Role of Regulation in Unlocking the Value of Big data 74 The Global Information technology Report 2014
uptake of big data will depend on the adoption of next -generation telecommunications infrastructure, which is still in its early development in many parts of the world
big data uptake will hinge on whether ways can be found to protect information technology infrastructures and the data they carry from cyberattacks.
potential of big data, and it outlines some actions companies can take themselves to promote consumer
CONSUMER TRUST AS AN ENABLER OF BIG DATA Research reveals that consumers are increasingly concerned about how their personal data are used
If big data is to deliver on its promise, companies will need both to create customer trust in big data
applications and their use and to help customers feel safe about the protection of their personal data and
policies must not stifle the innovation that big data can deliver, or its attendant economic and social benefits
The Role of Regulation in Unlocking the Value of Big data used. They have a right to be informed if those data
KEY REGULATORY AREAS FOR BIG DATA UPTAKE Whatever approach any single government or regulator chooses to adopt, all will need to pay particular
The Role of Regulation in Unlocking the Value of Big data 76 The Global Information technology Report 2014
The Role of Regulation in Unlocking the Value of Big data data can drive, while maintaining customer trust and
However, in a big data world where anonymized data can easily be linked up, it is not very
The Role of Regulation in Unlocking the Value of Big data 78 The Global Information technology Report 2014
often cooperate to produce big data applications and solutions. One company orders software from another which in turn uses a third company as a contractor
using big data clearly know what the rules are in order to ensure a certain environment that is conducive to
make the big data environment more certain IMPLICATIONS FOR REGULATORS AND POLICYMAKERS Regulators will need to address all the above issues
the use of personal data protection in big data would certainly be beneficial to establish a higher level of trust
maximize the benefits of big data and to build trust, a number of actions could be considered
to leverage those strengths to develop their big data strategy. For example, a company may wish to build
The Role of Regulation in Unlocking the Value of Big data Companies should strive to make data protection part of
business issues at hand and the benefits of big data for society Furthermore, companies need to cooperate with
Big data offers a wide range of opportunities†not just for individual companies, but also for nations and society
to regulatory and policy concerns regarding big data development. They must enable fast network build-out
different levels within the industry that the big data industry can eventually evolve to its full potential
The Role of Regulation in Unlocking the Value of Big data 80 The Global Information technology Report 2014
Kroes, N. 2013a. â€oethe Big data Revolution. †Speech given by Neelie Kroes, Vice president of the European commission responsible
††â€. 2013b. â€oethe Economic and Social Benefits of Big data. †Speech given by Neelie Kroes, Vice president of the European
From Big data to Big Social and Economic Opportunities: Which Policies Will Lead to Leveraging Data-Driven
INSTEAD OF BIG DATA It has become axiomatic that more data are produced every year, and somehow this phenomenon has
of big data. †However, what is commonly known as big data is not a new concept, as the use of data to
build successful products and services, optimize business processes, or make more efficient data-based decisions already has established an history.
Moreover, the term big data is ambiguous, and it sets up data as a negative because of the implication
big data usually focus not on size but instead on various characteristics, including the frequency of production
example, describes big data as â€oedatasets whose size is beyond the ability of typical database tools to capture
definition are that the main features of big data (quantity speed, variety) are technical properties that depend
What may look like big data today will not likely be as â€oebig†in the near future Thus, what is important about data is not their
From Big data to Big Social and Economic Opportunities 82 The Global Information technology Report 2014 Â 2014 World Economic Forum
From Big data to Big Social and Economic Opportunities  2014 World Economic Forum SETTING THE STAGE FOR A DATA-DRIVEN
From Big data to Big Social and Economic Opportunities 84 The Global Information technology Report 2014 Â 2014 World Economic Forum
big data that we can create the right environment for data-driven innovation, and that the individuals
From Big data to Big Social and Economic Opportunities  2014 World Economic Forum REFERENCES Brynjolfsson, E.,L. M. Hitt,
Big data and Digitizing the Farm. †Canadian Startup News, August 7. Available at http://www. betakit. com
/semios-big data-and-digitizing-the-farm /Hemerly, J. 2013. â€oepublic Policy Considerations for Data-driven
IBM. 2013. â€oethe IBM Big data Platform. †New york: IBM Corporation Available at http://public. dhe. ibm. com/common/ssi/ecm/en
Big data: A Revolution That Will Transform How We Live, Work, and Think. New york Houghton Mifflin Harcourt
-using-big data. html OECD (Organisation for Economic Co-operation and Development 2013. â€oeexploring Data-Driven Innovation as a New Source of
8. Available at http://strata. oreilly. com/2011/02/big data-fraud -protection-payment. html Talbot, D. 2013. â€oebig Data from Cheap Phones. †MIT Technology
/featuredstory/513721/big data-from-cheap-phones /Tapscott, D. and A. Williams. 2007. Wikinomics: How Mass
From Big data to Big Social and Economic Opportunities 86 The Global Information technology Report 2014 Â 2014 World Economic Forum
Making Big data Something More than the â€oenext Big Thing†ANANT GUPTA HCL Technologies Big data is the business buzzword du jour.
But how can you turn this hot topic into a real source of business value
technologies to extract significant value from big data Visa recently announced that increasing from 40 to 200
But for most businesses, the promise of big data is nowhere close to being fulfilled. For one thing, spending on it is polarized.
making significant strides in big data technologies, other industries, such as manufacturing and government, 5 are in a wait-and-watch mode
The lack of major big data initiatives across industries can be seen in the numbers from service
In 2012, the global top 20 big data players made less than 1 percent of their total revenues from big
The total market for big data hardware, software and services in 2012 was US$11. 5 billion, whereas the
combined overall revenue of those 20 big data players was more than US$1. 2 trillion The disparity between a few success stories and
big data. But it is important that they not rush thoughtlessly into the fray. An organization should make
a big data investment only if it has well-defined and realizable business objectives We offer here nine steps that companies can take
to begin turning big data talk into action, buzz into business benefits WHY IS EXTRACTING VALUE FROM BIG DATA
SO HARD First, though, we examine some of the barriers to realizing big data†s promise
Big data is said often to be characterized by 3 Vs: its tremendous volume, the velocity at which it needs
But mining the value of big data also is difficult because it requires simultaneously analyzing various
However, much of the value in big data exists in unstructured information†for example, the transcript of a
and managers to analyze big data and make decisions based on those findings. 6 Another report predicts that
only one-third of 4. 4 million big data jobs created by 2015 will be filled. 7 Unlike traditional analytics,
Big data is not a substitute for†much less a solution for†flawed information management practices If anything, it requires much more rigorous data
are excited often more about the potential of big data Box 1: A user†s glossary of key big data terms
As an organization plans its big data strategy, the following terms are likely to be used with increasing frequency
•Hadoop: A batch-oriented programming framework that supports the processing of large data sets in a
Making Big data Something More than the â€oenext Big Thing†88 The Global Information technology Report 2014  2014 World Economic Forum
Big data represents a convergence of IT and data science Technologies include Hadoop (which enables large -scale processing of diverse datasets), R (a programming
Big data professionals are expected to be familiar with both disciplines, but this combination is rare despite the training courses that are sprouting up globally
the analysis of big data, see Boxâ 1 NINE STEPS TO BIG DATA VALUE CREATION The barriers to extracting business value from big data
can seem daunting. But they can be overcome through a systematic plan, one that breaks down the challenge
into a series of nine sequential steps that will enable organizations to take advantage of this valuable and
big data could drive value. However, getting this level of support from functional leaders is not easy, especially
if the team†IT and analytics or a dedicated big data center of excellence†reside outside of the business
In order to drive the big data program, the team may want to appoint a big data program sponsor for each function
and work closely with him or her to discover and locate the types of information that would
and identify big data opportunities within the function Step 2: Get the business functions to ask the right
big data might be valuable to them. Simple questions such as â€oewhat would you really like to know about
questions is key to succeeding with big data. It also pays to keep in mind that big data is not about data
themselves; it is about using data to discover insights that can lead to valuable outcomes
Making Big data Something More than the â€oenext Big Thing† 2014 World Economic Forum Step 4:
It is smart to launch big data initiatives in business functions that are most ready to collect and analyze
Match big data initiatives with compatible business functions Some big data programs can be implemented in a variety of settings,
but most are suited to specific functions. For example •Customer functions (such as marketing e-commerce, and customer service) can use
big data for targeted advertising that provides personalized offers to consumers based on their socio-demographic characteristics, and for loyalty
treasury) can use big data for intraday liquidity management, providing real-time monitoring of price movements in relation to positions, to make
•Supply chain and procurement can use big data for dynamic route optimization because big data technologies that are faster than conventional
systems allow more iterations and faster route planning in real-time Step 6: Determine whether big data will yield
valuable information unavailable through traditional business analytics Making the business case for a big data initiative
clearly will be easier if it can be shown that it creates new value. For instance, if a marketing department is
business intelligence perspective versus a big data one, consider the following the questions: What data are we capturing today?
Potential payback of big data initiatives Source: Gartner, 2013 Da ta s ys te m s
Making Big data Something More than the â€oenext Big Thing†90 The Global Information technology Report 2014  2014 World Economic Forum
big data experimentation with an initiative that is not too demanding. In assessing possibilities, it is helpful to keep
in anticipation of big data In fact, the idea that big data involves negligible cost because it is analyzed using open-source tools and
platforms is a myth. â€oefree†open-source technologies such as Hadoop (which enables large-scale processing of diverse datasets) are typically not immediately
If the outcome of big data analysis is mission-critical for your business, it probably makes sense to use only
Big data initiatives require multidisciplinary teams of business and technology experts. Every team member†business analyst, programmer, data scientist, and data
owning the big data portfolio to succeed. Without clear line responsibilities, a CDO (whichever flavor, Data or
Instead, big data and business analytics expertise should fall within existing functions†for example finance, human resources, and marketing†with the aim
The efforts of the big data teams in these areas could be overseen and coordinated by a big data manager
reporting to the Chief Information Officer, who would The Global Information technology Report 2014 91 1. 9:
Making Big data Something More than the â€oenext Big Thing† 2014 World Economic Forum ensure that best practices were adopted and that
embarking on deriving value out of big data initiatives Almost all of them have defined step-by-step frameworks somewhat similar to the one outlined above.
In order to take full advantage of the potential of big data in both the public and private sectors, we recommend
Organizations already using big data initiatives A few organizations that have followed frameworks for using big data include •A US-based mid-to upscale chain of department stores
is gaining new insights from analyzing and combining data on Hadoop with data from traditional databases to turn its
is using big data to develop an integrated approach to optimizing how clinical trials are conducted and eliminate
research services uses a big data technology platform it has developed in house both for its risk management
now also sells this big data platform through its newly established subsidiary •A US-based multinational consumer goods company has
using big data to transform the audit function. It runs audit tests on all of its accounts payable transactions instead
Making Big data Something More than the â€oenext Big Thing†92 The Global Information technology Report 2014  2014 World Economic Forum
big data plan for all government services and activities The plan should identify all government data worth
becoming competent in the realm of big data. A step -by-step approach can make the transition seem less
What†s the Big Deal with Big data for Customer service Webinar with Gareth Herschel, Research director, Gartner and
Big data Strategy Components: IT Essentials. October 15, ID G00238944. Chicago: Gartner Manyika, J.,M. Chui, B. Brown, J. Bughin, R, Dobbs, C. Roxburgh, and
Making Big data Something More than the â€oenext Big Thing† 2014 World Economic Forum  2014 World Economic Forum
big data, the Internet of things, and the economic impact of digital technologies. Previously Mr Haynes was New york
He leads the Big data/CRM Center of Excellence for Europe and the Middle east within Booz & Company
embrace opportunities from big data/advanced analytics Bruno Lanvin Bruno Lanvin is the Executive director of INSEAD€ s
technologies, such as big data and the Internet of things on existing social, economic, and policy frameworks Prior to joining Microsoft, Dr Nguyen held positions with
Program, co-leads the World Economic Forum†s Big data and Personal data initiatives, and is a board member for
rewarding global big data integration challenges Walid Tohme Dr Walid Tohme is a Senior Principal with Booz & Company
Dr Tohme leads the big data efforts for Booz & Company in the middle East  2014 World Economic Forum
the role that big data may play in this process and the conditions that leading organizations will need to
advent of big data. In addition, the Report includes detailed profiles for the 148 economies covered this year together with data tables for each of the 54 indicators used in the computation of the NRI
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