Synopsis: Ict: Data: Data:

The Global Information Technology Report 2014 Rewards and Risks of Big data Beã at Bilbao-Osorio, Soumitra Dutta,

Data Initiative Dimitri Kaskoutas, Senior Community Associate Telecommunication Industry Danil Kerimi, Director, Government affairs, Information and Communication Technology Industries

statistical data are maintained on a separate and independent basis  2014 World Economic Forum The Global Information technology Report 2014 iii

Rewards of Data-Driven Public Policy Alex Pentland (MIT 1. 5 Managing the Risks and Rewards 61

Asymmetry in a Data-Driven Economy Peter Haynes (Atlantic Council) and M-H. Carolyn Nguyen (Microsoft

Policies Will Lead to Leveraging Data -Driven Innovation†s Potential Pedro Less Andrade, Jess Hemerly, Gabriel Recalde, and

Data Tables 249 How to Read the Data Tables...251 Index of Data Tables...253

Data Tables...255 Technical Notes and Sources 323 About the Authors 329 Partner Institutes 335

Acknowledgments 343  2014 World Economic Forum  2014 World Economic Forum The Global Information technology Report 2014 v

The 13th edition of The Global Information technology Report is released at a time when economies need to solidify the recovery of the past year and leave the

data, an unprecedented phenomenon in terms of the volume, velocity, and variety of sources of the creation

of new data. These essays also advise on the changes that organizations, both public and private, will need to

newly generated data. In addition, the Report presents a wealth of data, including detailed profiles for each

economy covered and data tables with global rankings for the NRI€ s 54 indicators We would like to convey our sincere gratitude to

the industry and academic organizations†experts who contributed outstanding chapters. We also wish to thank

data, and things become increasingly connected We call this the Internet of Everything (Ioe), and it is

data analytics The explosive expansion of Iot, or connections between context-aware machines and other physical

And the shift in data and analytics†from being centralized, structured, and static to being distributed, mixed structured and unstructured, and real

data traffic. The migration to IP networks and the ability to turn â€oebig data†into valuable, actionable information

data traffic to voice and video traffic, PC connections to any-device connections, and physical data centers to the

Data centers have evolved as more intelligence has been built into the network†from networking virtual machines and developing a platform optimizing computing to scaling

data. This is comprised of large datasets often gathered in unstructured forms from the behavior of people and

data in unprecedented amounts and interpret them in novel ways. Insights from old forms of market research

They invest only in the data gathering that gives them privileged access to the customers they care

Data have had always strategic value, but with the magnitude of data available today†and our capability to

process them†they have become a new form of asset class. In a very real sense, data are now the equivalent

of oil or gold. And today we are seeing a data boom rivaling the Texas oil boom of the 20th century and the

San francisco gold rush of the 1800s. It has spawned an entire support industry and has attracted a great deal of business press in recent years

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

obtained from proprietary data sources. Big data has been fueled by both technological advances (such as the spread of radio-frequency identification, or RFID, chips

and social trends (such as the widespread adoption of social media. Our collective discussions, comments likes, dislikes,

now all data, and their scale is massive. What did we search for? What did we read?

with big data requires more than just data. Data-based value creation requires the identification of patterns from

Businesses need to decide which data to use. The data each business owns might be as different as the

businesses themselves; these data range from log files and GPS data to customer-or machine to machine-machine

data. Each business will need to select the data source it will use to create value.

Moreover, creating this value will require the right way of dissecting and then analyzing those data with the right analytics.

It will require knowing how to separate valuable information from hype 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 Lanier, 2 call on us to be cautious about readily believing

data-driven economy;(6) the role of regulation and trust building in unlocking the value of big data;(

addition, the Country/Economy Profile and Data Tables sections at the end of the Report present the detailed

As exabytes of new data are created daily, a rising share of this data growth is flowing over IP networks

as more people, places, and things connect to the Ioe Proprietary networks are increasingly migrating to IP

becoming the key link among data generation, analysis processing, and utilization The authors highlight four major trends driving

data growth over IP networks and detail how networks are central to maximizing analytical value from the

data deluge. The chapter identifies critical technology and public policy challenges that could accelerate or encumber, the full impact of big data and the Ioe

-border data traffic, legacy regulatory models, reliability scaling, and electrical power Big data Maturity: An Action Plan for Policymakers

culture so that executives base their judgments on data rather than hunches. Research already indicates that

data initiatives; and looking also at the many and more complicated methods for using big data,

the business model to become data-driven, which requires significant investment over many years Policymakers should pay particular attention to

such as data, service, or IT system providers), and they should take educational measures to address the

Balancing the Risks and Rewards of Data-Driven Public Policy Alex â€oesandy†Pentland from the Massachusetts Institute

far more driven by data than it has been in the past Basic to the success of a data-driven society is the

protection of personal privacy and freedom. Discussions at the World Economic Forum have made substantial contributions to altering the privacy and data ownership

standards around the world in order to give individuals unprecedented control over data that are about them, while at the same time providing for increased

transparency and engagement in both the public and private spheres We still face the challenge that large organizations

the power of the data that they hold. To address this concern we need to establish best practices that are in

1. Large data systems should store data in a distributed manner, separated by type (e g financial vs. health) and real-world categories (e g

whose function is focused on those data, and with sharing permissions set and monitored by personnel from that department.

would have the custodians of data be regional and use heterogeneous computer systems. With such safeguards in place, it is difficult to attack

many different types of data at once, and it is more difficult to combine data types without authentic

and permissions associated with data and support automatic, tamper-proof auditing. Best practice would share only answers to questions about the

data (e g.,, by use of preprogrammed SQL queries known as â€oedatabase Viewsâ€) rather than the data themselves, whenever possible.

This allows improved internal compliance and auditing, and helps minimize the risk of unauthorized information

Otherwise data can be siphoned off at either the data source or the end consumer, without

the need for attacking central system directly 4. The need for a secure data ecosystem extends to

the private data of individuals and the proprietary data of partner companies. As a consequence, best

practice for data flows to and from individual citizens and businesses is to require them to have secure

personal data stores and be enrolled in a trust network data sharing agreement 5. All entities should employ secure identity credentials

at all times. Best practice is to base these credentials on biometric signatures 6. Create an â€oeopen†data commons that is available

to partners under a lightweight legal agreement such as the trust network agreements. Open data can generate great value by allowing third parties to

improve services Although these recommendations might at first glance seem cumbersome, they are for the most part easily implemented with the standard protocols found

many cases, the use of distributed data stores and management are already part of current practice, and

will result in a data ecosystem that is more secure and resilient, allowing us to safely reap the advantages of

high-velocity data creates three key trends •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

structured and unstructured data breaks the old computational and transactional ways of writing logic These trends create two main challenges

amounts of data is complicated much more than the relatively simple problem of marshaling storage and

Data-Driven Economy Chapter 1. 6, contributed by Peter Haynes of the Atlantic Council and M-H. Carolyn Nguyen at Microsoft

explains that an increasing amount of data is being generated by individuals who are handing potentially

asymmetries in the broad data ecosystem are a potential threat to the emerging data-driven economy, since

they may reduce overall output as more and more economic activity is predicated on the use, exchange and analytics of data.

The authors argue the need for a data ecosystem based on fair value exchange and the

ability of users to control the use of data related to them The chapter also considers potential technology and

policy approaches by which this might be achieved, and present the need for significant additional research and

sustainable data-driven economy Building Trust: The Role of Regulation in Unlocking the Value of Big data

data and privacy is being recognized by countries and organizations across the world. There are, however, a

how to treat anonymous data whether to allow the right to be forgotten, and the need

Leveraging Data-Driven Innovation†s Potential Chapter 1. 8, contributed by Pedro Less Andrade, Jess

focuses on the social and economic value of data but from the point of view of use and purpose rather

data are produced every year, commentators have been driven to call this revolution the â€oeage of big data. â€

the use of data to build successful products and services, optimize business processes and make more efficient data-based decisions already

not on the data themselves but on the evolution of computing, storage, and processing technologies.

This is why this chapter uses data-driven innovation to frame the discussion High-value solutions that may not have quantifiable

economic value are being developed using data, and many sectors, from businesses to governments, benefit from data-driven innovation.

Apart from producing and using data for better policymaking processes, the public sector can also play its part in promoting and

fostering data-driven innovation and growth throughout economies by (1) making public data accessible through

open data formats,(2) promoting balanced legislation and (3) supporting education that focuses on data science skills

Making Big data Something More than the â€oenext Big Thing†In Chapter 1. 9.,Anant Gupta, Chief executive officer at

HCL Technologies Ltd, argues that big data analytics is not a passing fad. It will be a central means of creating

-data competent. A step-by-step approach can make the transition seem less daunting and minimize the stumbles

AND DATA PRESENTATION Parts 2 and 3 of the Report feature comprehensive profiles for each of the 148 economies covered this

year as well as data tables for each of the 54 variables composing the NRI, with global rankings.

begins with a description of how to interpret the data provided Technical notes and sources, included at the end

-Survey data variables included in the NRI computation this year NOTES 1 Alexander 1983 2 See Lanier 2010;

Data have had always strategic value, but with the magnitude of data available today†and our capability to

process them†they have become a new form of asset class. In a very real sense, data are now the equivalent

of oil or gold. And today we are seeing a data boom rivaling the Texas oil boom of the 20th century and the

San francisco gold rush of the 1800s. It has spawned an entire support industry and has attracted a great deal of business press in recent years

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 obtained from proprietary data sources.

Big data has been fueled by both technological advances (such as the spread of radio-frequency identification, or RFID, chips

now all data, and their scale is massive. What did we search for? What did we read?

•IBM uses data to optimize traffic flow in the city of Stockholm, 3 and to get the best possible air quality

medical billing data to map out hot spots where you can find his city†s most complex and costly

just data. Data-based value creation requires the identification of patterns from which predictions can be

which data to use. The data each business owns might be as different as the businesses themselves;

these data range from log files and GPS data to customer-or machine to machine-machine data.

Each business will need to select the data source it will use to create value Moreover, creating this value will require the right way

of dissecting and then analyzing those data with the right analytics. It will require knowing how to separate

valuable information from hype. Chapter 1. 7 provides guidelines for businesses to make this transition.

To a large extent, mastering big data can also be compared to irrigation. It is not enough to â€oebring water†to where it

Unit released survey data showing that approximately two-thirds of executives feel that big data will help find

Although data are still scarce in terms of ICT impacts, policy interest in measuring ICTS has shifted from measuring ICT access to measuring ICT impacts

At the moment, because of data limitations, this pillar focuses on measuring the extent to which governments are becoming more efficient in

quantitative data to do so is still in its infancy. As a result many of the dimensions where ICTS are producing

new data on many of these dimensions as they become available COMPUTATION METHODOLOGY AND DATA

In order to capture as comprehensively as possible all relevant dimensions of societies†networked readiness the NRI 2014 is composed of a mixture of quantitative

and survey data, as shown in Figureâ 3 Of the 54 variables composing the NRI this year

27†or 50 percent†are collected quantitative data primarily by international organizations such as International Telecommunication Union (ITU), the World

ensure the validation and comparability of data across countries The remaining 27 variables capture aspects that

comparable quantitative data are not available for a large enough number of countries, but that nonetheless are

These data come from the Executive Opinion Survey (the Survey), which the Forum administers annually to over

by the Survey coverage and data availability for indicators obtained from other sources, mostly international organizations.

in the 2014 Report because Survey data could not be collected this year More details on variables included in the Index and

Breakdown of indicators used in the Networked Readiness Index 2014 by data source TOTAL: 54 INDICATORS

addition, the Country/Economy Profiles and Data Tables sections at the end of the Report present the detailed

handset data download because owners of smartphones are more likely to purchase goods, access video and

data. Large amounts of data, often referred to as big data, are constantly generated both in a structured and non-structured

manner. Thanks to advances in ICTS, the volume and velocity of generation of these data are unprecedented

as is the capacity of organizations to capture and treat them, potentially generating great economic and social

and interpret these data. This will frequently require new management philosophies and organizational structures capable of adapting and

matches the numbering of the data tables at the end of the Report The computation of the NRI is based on successive

Where data are missing for indicator 4. 03 (i e.,, Puerto Rico and Timor-Leste), the score on the affordability pillar, which is

Exabytes (1018) of new data are created every single day. Much of this information is transported over Internet

as the â€oenew oil, †1 this data growth is fueling knowledge economies, sparking innovation,

But most of these data are unstructured and underutilized, flowing at a volume and velocity that is often too large and too fast to analyze

If data do in fact, comprise the new raw material of business, on par with economic inputs such as capital

A rising share of this data growth is flowing over IP networks as more people, places, and things connect

big data, and fast becoming the key link among data generation, processing, analysis, and utilization How can we effectively maximize value from this

data explosion and avoid the pitfall of diminishing marginal data value? This chapter details how IP

networks underpin the Ioe and can accelerate big data†s transformational impact on individuals, businesses, and

four major trends driving data growth over IP networks and detailing how networks are central to maximizing

analytical value from the data deluge, the chapter identifies critical technology and public policy challenges

ACCELERATING DATA PRODUCTION AND DATA TRAFFIC Data growth is skyrocketing. Over 2. 5 quintillion bytes

of data are created each day; 90 percent of the world†s stored data was created in the last two years alone. 3 To

put this into context, one hour of customer transaction data at Wal-mart (2. 5 petabytes) provides 167 times

the amount of data housed by the Library of Congress The research consultancy IDC estimates that the

digital universe†all digital data created, replicated, or consumed†is growing by a factor of 30 from 2005 to

2020, doubling every two years. By 2020, there will be over 40 trillion gigabytes (or 40 yottabytes) of digital

data†or 5, 200 gigabytes for every person on earth. 4 Much of this data growth is traversing IP networks

Cisco†s Visual Networking Index estimates that, from 2012 to 2017, total traffic over IP networks will grow

Mobile data traffic, however, is growing at an even faster pace: over the same period

mobile data will grow 13-fold, with a CAGR of 66 percent, capturing a greater share of all data created and

transmitted (Figureâ 1). 5 The Global Information technology Report 2014 35 Â 2014 World Economic Forum

Despite the rapid growth in data production and transmission, however, only a small fraction of all

Internet-enabled alarm clocks gather data on weather and traffic, combining that information with a user†s schedule, determining the optimal time to wake

technologies are capturing vast amounts of data to improve decision-making. Sensors embedded in agricultural fields monitor temperature and moisture

Ioe, the data universe will continue to grow rapidly. The Ioe will not only fuel the expansion of big data and data

transmission, but can also provide targeted, automatic data-driven analysis for our day-to-day lives CRITICAL DRIVERS OF DATA GROWTH

In 1944, the first digital computer, the Colossus was deployed in the United kingdom to decipher codes during WORLD WAR II.

The Colossus was able to process data at 5, 000 characters per second (25 Kb/s). 7 Currently the world†s fastest supercomputer

the Milkyway-2, can process 54,902 Ã 1012 operations per second (54,902 TFLOP/s). 8 This intensive growth in

extensive growth in data production. This data growth also supports four major trends that lead to a rising

share of data transmission over IP networks in the world of the Ioe, as described below

bringing previously isolated data onto public and managed IP networks. The Internet†s history is

Proprietary data networks such as Appletalk and IBM Systems Network architecture (SNA) have migrated to IP over time,

Growth rates and rising share of mobile data Sources: Cisco 2013b; EMC 2013; authors†calculations In

†Mobile data traffic †Total data universe †Total IP traffic 1. 2: The Internet of Everything

36 The Global Information technology Report 2014 Â 2014 World Economic Forum Voice over internet Protocol (Voip. Today electricity

large amount of data production and transmission onto IP networks (see Boxâ 1 •Previously unconnected places, people, things

to the endpoints collecting data and to the devices consuming information. Cisco†s Visual Networking

the data itself (e g.,, descriptive statistics, frequency distribution, dispersion, etc..This digitization of information is leading to greater exchange of stored

media and data over the Internet •The introduction of Internet protocol version 6 (IPV6) allows for trillions of trillions (1038) of

Huge and growing data volume from industrial applications Industrial applications of the Internet of Everything (Ioe

generate immense data flows, which are increasingly shifting over to Internet protocol (IP) networks. One reason

and the data flows that come with them In the oil and gas industry, for example, data are

utilized across the entire value chain, from exploration production, refining, and distribution to marketing and

monitor seismic data, borehole activity, environmental readings, weather, production utilization, storage capacity spot pricing (trading), transportation, inventory levels

and location data. In seismic exploration, the cost, size, and speed of data are all

rising as exploration moves to 3d imaging. Data capture amounted to around 300 megabytes per square kilometer

in the 1990s. By 2006, data per square kilometer amounted to 25 gigabytes, while today the amount per

square kilometer is in the petabytes. 1 According to Chevron and industry-wide estimates, a â€oefully optimized†digital oil

field based on data utilization results in 8 percent higher production rates and 6 percent higher overall recovery. 2

In electric utility grids, data utilization also improves efficiency. Current grids monitor data to control electricity

flows (both to and from the grid) based on real-time demand, thus improving generator efficiency and ensuring

increases the amount of data captured. While traditional meters are read once a month, some smart meters can

an estimated 3, 000-fold increase in data collection. 3 Conservative estimates of the total amount of data that will

be generated by smart meters by 2019 in the United states alone (assuming only two readings per day, and below full

GE) †s jet plane turbines illustrate the vast amount of data generated daily. GE estimates that each sensor on a GE

turbine generates approximately 500 gigabytes of data every day. Each turbine has 20 sensors, and globally GE

petabytes of data daily. 5 Notes 1 Beals 2013; see also note 4 at the end of this chapter

THE GAP BETWEEN DATA GROWTH AND DATA VALUE Current estimates suggest that only half a percent of

all data is being analyzed for insights; 13 furthermore the vast majority of existing data are unstructured and

machine-generated. 14 Applying analytics to a greater share of all data can lead to productivity increases

economic growth, and societal development through the creation of actionable insights Data alone are not very interesting or useful.

It is when data can be used and become actionable that they can change processes and have direct positive

impact on people†s lives. The Ioe generates data, and adding analysis and analytics turns those data into

actionable information. Building on the framework of the knowledge hierarchy, 15 aggregated data become information that,

when analyzed, become knowledge Knowledge can lead to insights and informed decision -making, which at the highest level is wisdom (Figureâ 2

For example, society at large can benefit from tracking trends observed from metadata such as anonymized mobile phone data used to track population

migration after the earthquake and cholera outbreaks in Port-au-prince, Haiti. 16 Likewise, analyzing social media discussions can identify crises or flu outbreaks

the US electricity grid to be driven data is estimated at US$210 billion. A reconstituted electricity grid would

selections to fully harness the convergence of data controls and transactions. †17 According to Bradley et al. in a recent Cisco White

Paper, harvesting data for critical decision-making though the Ioe can create approximately US$14. 4 trillion

to manage the rise in data. It is forecasted that by 2020 an average business will have to manage 50 times

Turning data into insight Sources: Ackoff 1989; authors†interpretation Insight wisdom Process optimization Knowledgedecision-making

Data Individual data points 1. 2: The Internet of Everything 38 The Global Information technology Report 2014

 2014 World Economic Forum EQUIPPING IP NETWORKS TO DELIVER BIG DATA INSIGHTS Moving up the knowledge pyramid from data to insights

and informed decisions is a critical challenge facing businesses and governments. Equipping IP networks to better transmit data to processing centers as well as

enabling the network to create, analyze, and act on data insights is one comprehensive approach. Building this

capability will require improving network infrastructure building analytical capabilities and â€oeintelligence†into the network, and distributing computing and analytical

only of transmitting data, not receiving them securing infrastructure; improving inter and intra -data center traffic flows;

require building in the ability to compute data in motion and host partner applications in an

analyze data inflow, particularly enabling machine -to-machine (M2m) services •Distributing computing and storage.

data only in the data center to add processing at the edge (or near the edge) of the network, to prevent

data requires powerful and seamless interactions among sensors, devices, computing, storage, analytics, and control systems But although IP networks are primed to support the

Privacy issues arise with the growth of data particularly with regard to data generated by or about

individuals. Policymakers must identify the appropriate balance between protecting the privacy of individuals†data and allowing for innovation in service delivery and

product development. New technologies and services Source: Authors POLICYTECHNICAL Figure 3: Policy and technical issues facing big data and the Ioe

Cross-border data traffic Legacy regulatory models The Global Information technology Report 2014 39 1. 2:

data and effective mechanisms for consumer control of personal data†can help in this regard. The key security

users to large databases and data flows. In order to ensure a healthy ecosystem where users, consumers

Over the next five years, the growth of mobile data traffic will require greater radio spectrum to enable

short haul and long haul spectrum, continuous data transmission and short bursts of data transmission and licensed spectrum in addition to license-exempt

obstacle in transmitting data over existing networks The examples cited in Boxâ 1 reflect the volume of data

being generated by proprietary networks, resulting in the need to move computing close to the network edge

Data loads will be lumpy across various applications of the Ioe, and matching bandwidth needs to bandwidth availability will

transmission of data over networks negatively impacts these processes Constraints on the technological limits of electrical

Data centers, for example, exemplify the boundaries where electrical power, cooling resources, and space design are redesigned constantly

Ioe applications that collect and handle data across sovereign jurisdictions could be affected negatively by policies restricting cross-border data traffic and global

trade in Ioe-related services. Emerging cross-border issues include national data protection rules and data transfers, data portability and interoperability standards

and liability costs for cloud service providers. Furthermore trade in some Ioe services may fall under existing

international trade agreements, while others do not As the Ioe permeates across business sectors, the application of Ioe technology in traditional industries

processing data. We have been our own primary data machines. But today, with the advent of vast arrays

of computing power, we increasingly rely on data processed by others, and the Ioe and the era of big data

are transforming our lives Data flows and the ability to capture value from data are changing industries,

creating new opportunities while impacting others. For example, the â€oeapp economy††the business created by software applications running on

impact of data utilization in the Ioe could raise US gross domestic product by 2 percent to 2. 5 percent by 2025.22

The Ioe†where more data are being captured by more devices, interacting with more people and

Ackoff, R. 1989. â€oefrom Data to Wisdom. †Journal of Applied Systems Analysis 16: 3†9

Population Movements with Mobile phone Network Data: A Post -Earthquake Geospatial Study in Haiti. †PLOS Med 8 (8:

Data: How It†s Changing the Way We Think about the World. †Foreign affairs May/June.

Danahy, J. 2009. â€oethe Coming Smart Grid Data Surge. †October 5 Available at http://www. smartgridnews. com/artman/publish

/News blogs news/The-Coming-Smart-Grid-Data-Surge-1247 html De Martini, P. and L. von Prellwitz. 2011. â€oegridonomics:

The Economist. 2010. â€oedata, Data Everywhere. †Managing Information Special report, February 25. Available at http://www. economist

Data and Decision making, June 12. Report commissioned by Capgemini. Available at http://www. managementthinking. eiu. com

Fehrenbacher, K. 2009. â€oesmart Grid Data About to Swamp Utilities. †October 12. Gigaom. Available at http://gigaom. com/2009/10/12

/smart-grid-data-about-to-swamp-utilities /Gantz, J. and D. Reinsel. 2012. â€oethe Digital Universe in 2020:

Website. â€oebig Data. †Available at http://www. ibm. com/big -data/us/en /IBM Software. 2012. â€oemanaging Big data for Smart Grids and Smart

Meters. †IBM White paper. Somers, NY: IBM Corporation Available at ftp://public. dhe. ibm. com/software/pdf/industry

Mclellan, C. 2013. â€oebig Data: An Overview. †Going deep on Big data ZDNET special feature, October 1.

Utility Data Management and Intelligence. Cisco. http://www. cisco. com/web /strategy/docs/energy/managing utility data intelligence. pdf

The total volume of structured and unstructured data generated by individuals, enterprises, and public organizations is multiplying exponentially;

of the total data stored today is less than two years old. 1 So-called big data has the potential to improve

store and analyze the deluge of data that threatens to drown companies. Although this technology is indeed

judgments based on clear data insights rather than on intuition. They must build the necessary internal

resources to interpret data in an astute manner Moreover, because they rely on governments to provide

benefits from the vast volume of data. The framework incorporates three elements:(1) environment readiness

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

of sources, at high velocity (i e.,, real-time data capture storage, and analysis). Besides structured data (such as

customer or financial records), which are kept typically in organizations†data warehouses, big data builds on unstructured data from sources such as social media

text and video messages, and technical sensors (such The authors wish to thank Dr. Andreas Deckert for his contribution to this

chapter The Global Information technology Report 2014 43 Â 2014 World Economic Forum as global positioning system,

The magnitude and complexity of data being produced far exceed the typical capacities of traditional

illustrate the sheer volume of unstructured data. For example, in 2012 Facebook reported that it was

decisions with an unprecedented level of data-driven insights. However, research indicates that many organizations are struggling to cope with the challenges

data, and who complained that the volume of data was growing too rapidly to manage,

-elaborate data have influenced decision-making. From organizations†first attempts at data analytics in the 1960s and 1970s, this journey has proceeded through

various stages, described by buzz words such as data mining and business intelligence, all of which sought to transform raw data into meaningful information for

business purposes (Figureâ 1 The latest development, big data, may appear all-enveloping and revolutionary. However, the essential

increased data-driven decision-making. Executives must harness this recent data explosion by focusing on carefully formulating the business questions that enable

the swift and accurate identification of those nuggets of data that they believe can improve their organization†s

1970 1980 1990 2000 2010 Now and future Figure 1: Evolution of data-driven decision-making

Source: Booz & Company Linearâ programming Managementâ information systems/dashboards Dataâ marts Dataâ warehouses Dataâ clusters

Data visualization Monteâ Carlo simulations Standardâ reporting Knowledgeâ discovery Operational intelligence Heuristic problem -solving Riskâ modeling

Volume/complexity of data BIG DATA Chapter 1. 3: Big data Maturity 44 The Global Information technology Report 2014

relevant performance data, enabling them to measure the extent to which corporate attitudes toward big data

characterized themselves as data-driven, the better they performed on objective measures of financial and operational results. †The advantage gained by

in the use of data-driven decision-making were, on average, 5 percent more productive and 6 percent more

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

internal data, with an organization establishing key performance indicators (KPIS) to evaluate its success at

external data to improve selected facets of their business. This may involve sales and marketing

data recounting the past purchasing behavior of individual customers in conjunction with the company†s

data from monitoring of newborns, enabling detection of dangerous infections 24 hours before symptoms appeared. 9

involves obtaining data from external sources and The Global Information technology Report 2014 45 Chapter 1. 3:

Data-rich organizations, such as retailers or telecommunications companies, are equipped better than others to utilize their internally generated data in

this way. For instance, a global mass merchant was able to increase its profit per customer by 37 percent

modeling, fed with data from parking sensors, surveys weather forecasts, information about holidays, local business activities, and other information.

•Data monetization •Online telematics services •Personalization of customer experience/products Stage 4

•Selling of data to open new revenue pools •Data-centric business models e g.,, web search, web adver

-tising •Quantitative management of investment funds •Crowdsourcing to augment internal data Large-scale implementation Experimenting

/selective adoption BIG DATA Chapter 1. 3: Big data Maturity 46 The Global Information technology Report 2014 Â 2014 World Economic Forum

toward a more science-based, data-driven business that aims to personalize ads. The ultimate goal is to

comprehensive data about consumers and are thus able to understand them better†who they are, where

will produce the desired volume of data General electric (GE) provides a prominent example of a product organization placing great faith in big data

be loaded with sensors, making in depth status data available both in real time and across longer time spans

capabilities to provide data and analytics services across business functions and geographies. 11 Another showcase for the transformative potential

launching â€oeopen data†initiatives, making data available to the public via integrated web portals and automated

decisions, the release of public data is an important environmental factor enabling organizations to use big

data, creating novel applications and services However, some organizations do not have to progress through all the big data maturity stages

A data-driven business model has been integral to companies such as Google, Facebook, and Twitter which have burst on to the scene in recent years and

Despite widespread interest in data-driven decision -making in one form or another, companies face many

available talent specializing in data analytics†data scientists with an advanced education in mathematics or statistics who are also able to translate raw data material

data that are fragmented across various systems geographies, and functional silos. Embracing the potential of big data as a concept will take organizations

Internal data has to be of high quality†consistent accurate, and complete†and available across the

facts of hard data. However, while data can be of great assistance in solving an actual problem,

it still holds true that senior management has to ask the right questions Many of the external challenges that companies

data maturity and reach the desired destination The Global Information technology Report 2014 47 Chapter 1. 3:

data. By building up these capabilities and integrating them effectively, organizations move further along the path of data-driven decision-making and position

themselves to extract greater benefits from big data While environment readiness serves as an enabler for big data usage, internal capabilities act as critical

•formulate a vision for the usage of data consistent with the public interest, fostering a common

data, service, and information technology system providers; and •speed and scale up the education of talent to

getting more out of data you already have New horizons of big data Technical capabilities/infra

Data-driven decision-making culture Education/training Stage 1 Performance management Stage 2 Functional area excellence

read from the data What can we learn from the data to become better How can we make

data a value driver of our business How can we use data to fundamentally reinvent our business

Chapter 1. 3: Big data Maturity 48 The Global Information technology Report 2014 Â 2014 World Economic Forum

Priorities for policymakers will vary in different parts of the world. Developing countries, for example will concentrate on building up the required ICT

which data are forbidden explicitly by privacy regulations If the scope of permissible data is to expand

skeptical citizens must first be persuaded that big data will work in their favor by paving the way for better

the lack of clarity on lawful data usage†especially the question of which jurisdiction holds sway for certain

For example, if data are owned by a company in the European union, but hosted on servers in the United states,

example, when an organization plans to outsource data operations to a foreign provider, yet some personal data

specification of the purpose of data collection, the protection of collected data, the prevention of data loss

or unauthorized access, and the right of individuals to obtain information about collected data. The guidelines

have influenced in the past national legislation, including privacy acts in Australia, Japan, Mexico, and New Zealand. We encourage both OECD members and non

executives still grappling with existing data, making intelligent use of what they already possess may have a

•prove the value of data in pilot schemes •identify the owner for â€oebig data†in the organization

and formally establish a â€oechief Data Scientist†position (where applicable •recruit/train talent to ask the right questions and

to allow data scientists to answer those questions •position big data as an integral element of the

•establish a data-driven decision culture and launch a communication campaign around it Quick wins

Seeking out proprietary data that can be immediately exploited for commercial gain may provide The Global Information technology Report 2014 49

External data providers can offer all types of data to organizations and can therefore complement existing

data-gathering efforts. Typical datasets offered by external providers include contact, lifestyle, and demographic information on (market segments

In addition to sourcing data from outside the organization, the selective use of external analytics service providers can also prove instrumental

already acknowledge the future influence of data-driven decision-making However, organizations confront vast differences in their ability to utilize big data to good effect, as seen

data Nonetheless, policymakers and organizations in general still have much to do if they want to realize the

what the world of data-driven insights will look like in the medium term, anticipate which trends will lead there, and

12 In the UK, the initiative is available at http://data. gov. uk/;/in New york city it is available at https://data. cityofnewyork. us

/13 Polonetsky and Tene 2013 14 OECD 2013 REFERENCES Aberdeen Group. 2013. â€oebig Data Trends in 2013, †February 1.

Available 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

Constine, J. 2012. â€oehow Big Is Facebook†s Data? 2. 5 Billion Pieces of Content and 500+Terabytes Ingested Every day. †Tech Crunch

com/2012/08/22/how-big-is-facebooks-data-2-5-billion-pieces-of -content-and-500-terabytes-ingested-everyday

/The Economist Intelligence Unit. 2013. â€oethe Evolving Role of Data in Decision-making. †Available at http://www. economistinsights. com

/analysis/evolving-role-data-decision-making Gartner. 2013. â€oesurvey Analysis: Big data Adoption in 2013 Shows Substance Behind the Hype. †Available at http://www. gartner. com

-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

Data-Driven Public Policy ALEX PENTLAND MIT In June 2013, massive US surveillance of phone

records and Internet data was revealed by former National security agency (NSA) contractor Edward Snowden, who called these activities the â€oearchitecture

Data about human behavior, such as census data have always been essential for both government and

methodology for collecting data about human behavior has emerged. By analyzing patterns within the â€oedigital breadcrumbs†that we all leave behind us as we move

The risk of deploying this sort of data-driven policy and regulation comes from the danger of putting so

The three main divisions within the spectrum of data control are:(1) data commons, which are available to

all, with at most minor limitations on use;(2) personal or proprietary data, which are controlled typically by

individuals or companies, and for which legal and technology infrastructure must provide strict control and

and (3) the secret data of governments The Global Information technology Report 2014 53 Â 2014 World Economic Forum

The issues of data commons will be addressed first, followed by concerns about personal and proprietary data,

and, finally, issues of secret government data The preferred lens for examining these issues is

experimentation in the real world rather than arguments from theory or first principles, because using massive live data to design institutions

and policies is outside of our traditional way of managing things. In this new digital era we cannot rely only on existing policy, tradition, or

To begin to manage our society in a data -driven manner requires us to move beyond academic

Data commons The first entry in the data taxonomy is the data commons. A key insight is that our data are worth more

when shared because they can inform improvements in systems such as public health, transportation, and government. Using a â€oedigital data commons†can

potentially give us unprecedented ability to measure how our policies are performing so we can know when to act

quickly and effectively to address a situation We already have many data commons available maps, census data,

and financial indices, for example With the advent of big data, we can potentially develop many more types of data commons;

these commons can be both accessible in real time and far more detailed than previous, hand-built data commons (e g.,

, census data, etc..This is because the new digital commons depend mostly on data that are produced already as a

side effect of ongoing daily life (e g.,, digital transaction records, cell phone location fixes, road toll records, etc

and because they can be produced automatically by computers without human intervention One major concern with these new data commons

is that they can endanger personal privacy. Another secondary, concern involves the tension between proprietary interests, both commercial and personal

and the goal of putting data in the commons. Acceding to these proprietary interests might tend to reduce the

richness of such a commons, which would diminish the ability of such a data commons to enable significant

public goods To explore the viability of a big data commons, what is perhaps the world†s first true big data commons was

In this Data for Development D4d) initiative, 90 research organizations from around the world reported hundreds of results from their analysis

of data describing the mobility and call patterns of the citizens of the entire African country CÃ'te d†Ivoire. 1 The

data were donated by the mobile carrier Orange, with help from the University of Louvain (Belgium) and the

participating organizations explored the use of this data commons, covering many different aspects of better

An example of using the D4d data to improve social equality was highlighted by work done

D4d data to enhance social equality is the mapping of ethnic boundaries by researchers from the University

The D4d data were utilized also to understand and promote operational efficiency through an analysis of

Finally, examples of using D4d data to improve social resiliency include analysis of disease spread

Balancing the Risks and Rewards of Data-Driven Public Policy 54 The Global Information technology Report 2014

The Data for Development (D4d) data commons is only a small first step toward improving governance by using big

data. Much more can be accomplished because our current understanding of policy and human society is based on

very limited data resources. Currently, most social science is based either on analysis of laboratory experiments or on

survey data. These approaches miss the critical fact that it is the details of which people you interact with, and how

duration of the data collection; the vertical axis shows the richness of the information collected

Unfortunately, as illustrated in Figure A, almost all data from traditional social science (labeled â€oe1†in the figure) are near

Recently data scientists have developed living lab technologies for harvesting digital breadcrumbs, and are now obtaining much richer descriptions of human behavior.

to collect data. 2 The point labeled â€oe9†is the D4d dataset that covers the entire country of CÃ'te d†Ivoire. 3

continuous, dense data that allow us to build quantitative predictive models of human behavior in complex, everyday

available incredibly rich data about the behavior of virtually all of humanity on a continuous basis. The data mostly

already exist in cell phone networks, credit card databases and elsewhere, but currently only technical gurus have

Balancing the Risks and Rewards of Data-Driven Public Policy  2014 World Economic Forum

and unique data commons. These results and others like them are available at http://www. d4d. orange. com/home

lines of business that combine this data commons with customers†personal data: imagine phone applications that advise commuters about which bus will get them to

with the release of data about human behavior may be generally misunderstood. In this data commons, the

data were processed by advanced computer algorithms e g.,, sophisticated sampling and the use of aggregated indicators) so that it was unlikely that any individual could

be identified re. In fact, no path to re-identification was discovered even though several of the research groups

In addition, although the data were freely available for any legitimate research in which a group was

interested, the data were distributed under a legal contract that specified that they could be used only

Personal and proprietary data The second category in the data taxonomy is personal and proprietary data,

which are controlled typically by individuals or companies, and for which legal and technology infrastructure that provides strict control and

auditing of use is needed. The current best practice is a system of data sharing called trust networks. 2 Trust

what can and cannot be done with the data and what happens if there is a violation of the permissions.

labels specifying what the data can, and cannot, be used for. These labels are matched exactly by terms

giving the right to audit the use of the data. Having permissions, including the provenance of the data

allows automatic auditing of data use and allows individuals to change their permissions and withdraw

their individual data Today, longstanding versions of trust networks have proven to be both secure and robust.

The best known example is the SWIFT network for inter-bank money transfer; its most distinguishing feature is that it has

with the Institute for Data Driven Design (http://idcubed org), have helped build openpds (open Personal data

is the extent to which incidental data about human behavior must be included in the permissions and

Such data are collected typically in the course of normal operations in order to support those operations (e g.,

the Institute for Data Driven Design, and local companies within Trento. Importantly, this living lab has the approval

new ways of sharing data to promote greater civic engagement and information diffusion. One specific goal

data services†designed to enable users to collect store, manage, disclose, share, and use data about

Chapter 1. 4: Big data: Balancing the Risks and Rewards of Data-Driven Public Policy 56 The Global Information technology Report 2014

 2014 World Economic Forum themselves. For example, the openpds system lets the community of young families learn from each other

without the work of entering data by hand or the risks associated with sharing through current social media

These data can then be used for the personal self -empowerment of each member, or (when aggregated

for the creation of a data commons that supports improvement of the community†for example, a map

The ability to share data safely should enable better idea flow among individuals, companies, and government

financial, and medical records†to generate a useful data commons. It will also explore different user interfaces

for privacy settings, for configuring the data collected for the data disclosed to applications, and for those

data shared with other users, all in the context of a trust framework. Although the Trento experiment is still in its

early days, the initial reaction from participating families is that these sorts of data sharing capabilities are

valuable, and they feel safe sharing their data using the openpds system Government data The third category in the taxonomy is secret government

data. A major risk of deploying data-driven policies and regulations comes from the danger of putting so much

personal data into the hands of governments. But how can it happen that governments, especially authoritarian

governments, choose to limit their reach? The answer is that unlimited access to data about the citizen behavior

is a great danger to the government as well as to its citizenry. Consider the NSA€ s response to the recent

type of data separated and dispersed among many locations, using many different types of computer

that have different types of data that are physically and logically distributed, and that also have heterogeneous

access to data about citizen behavior can be a major aid to organizing a successful coup to overthrow the

Governments that structure their data resources in this manner can more easily monitor attacks and misuse of

Balancing the Risks and Rewards of Data-Driven Public Policy  2014 World Economic Forum

distributed data stores with permissions, provenance, and auditing for sharing among data stores. In this case, however, the data

stores are segmented by their referent†for example tax records for individuals, tax records for companies import records from country X to port Y, and so

on†rather than having one data store per person Because the architecture is so similar to the citizen

-centric personal data stores, it enables easier and safer sharing of data between citizens and government.

For this reason, several states within the United states are beginning to test this architecture for both internal and

is driven far more by data than it has been in the past Basic to the success of a data-driven society is the

protection of personal privacy and freedom. Discussions at the World Economic Forum have made substantial contributions to altering the privacy and data ownership

standards around the world in order to give individuals unprecedented control over data that are about them, while at the same time providing for increased

transparency and engagement in both the public and private spheres We still face the challenge that large organizations

tempted to abuse the power of the data that they hold. To address this concern,

1. Large data systems should store data in a distributed manner, separated by type (e g.,, financial

those data, with sharing permissions set and monitored by personnel from that department Best practice would have the custodians of data be

regional and use heterogeneous computer systems With such safeguards in place, it is difficult to attack many different types of data at once,

and it is more difficult to combine data types without authentic authorization 2. Data sharing should always maintain provenance

and permissions associated with data, and should support automatic, tamper-proof auditing. Best practice would share answers only to questions

about the data (e g.,, by using the preprogrammed structured query language, or SQL, queries known as â€oedatabase Viewsâ€) rather than sharing

the data themselves, whenever possible. This allows improved internal compliance and auditing and helps to minimize the risk of unauthorized

safeguards, data can be siphoned off at either the data source or at the end consumer, without even

attacking central system directly 4. The need for a secure data ecosystem extends to the private data of individuals and the proprietary

data of partner companies. As a consequence, best practice for data flows to and from individual citizens

and businesses is to require them to have secure personal data stores and be enrolled in a trust

network data sharing agreement. 6 5. All entities should employ secure identity credentials at all times. Best practice is to base these

credentials on biometric signatures. 7 6. Create an â€oeopen†data commons that is available to partners under a lightweight legal agreement

such as the trust network agreements. Open data can generate great value by allowing third parties to

improve services Although these recommendations might seem cumbersome at first glance, they are for the most part easily implemented with the standard protocols already

Balancing the Risks and Rewards of Data-Driven Public Policy 58 The Global Information technology Report 2014

In many cases, the use of distributed data stores and management are already part of current practice, and

will result in a data ecosystem that is more secure and resilient, allowing us to safely reap the advantages of

and the Institute for Data Driven Design available at http://idcubed. org 3 For details about openpds, see http://idcubed. org/open-platform

ID3 (Institute for Data Driven Design, or idcubed. Available at http://idcubed. org MTL (Mobile Territorial Lab). Available at http://www. mobileterritoriallab. eu

a New deal on Data. †In The Global Information technology Report 2008†2009: Mobility in a Networked World.

Balancing the Risks and Rewards of Data-Driven Public Policy  2014 World Economic Forum

and data that lack traditional structure, working in an environment of big data is just business as usual.

challenge of data is not new for a regional healthcare organization in the Midwestern United states, a global

data nor built a strategy around its use, the term big data itself is a way to express the sudden digitization

of many things that have been with us forever but were not previously captured and stored as data.

For most companies, big data represents a significant challenge to growth and competitive positioning. In some cases, it

data (data derived from devices that sense their environment) to the mix have pushed all the boundaries

of how we think about data and its uses. The term big data represents the need for a new way of thinking but

also implies new tools and new ways of managing data Like many things, data can be used to do positive things

for the world, but it can also be used to manipulate embarrass, or repress. Data can be highly accurate

and efficiently structured or unstructured, fragmented and highly suspect. Data can also be managed well or

carelessly. 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

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

controls mean not only that data are constantly being fed into machines but that they are also coming out of

a data ecosystem that can be modeled in a way that blends historical with in-the-moment information and

right data together in the right moments that allow for the right response and outcome. Whatever we may know

data sources will continue to change and improve our models, allowing us to better anticipate future events

acutely aware of the explosion of data. 1 Hackathorn†s curve describes the decreasing value of data over time

as it passes through stages of use (Figureâ 1 The challenge of the decreasing value of data over

time has become even more meaningful in the age of big data. Today, the volume, velocity, and variety of data

continue to push the curve down and to the right as organizations struggle to capture, analyze,

complexity is the increasing access to real-time data that leaves organizations in some industries attempting

better and better tools that can manage data far more quickly and efficiently than a human can. Data exist in

a moment, ready for decision and action, but there is a higher-level purpose for information.

Data comprise the digital representation of events, or things that happen in patterns that occur over time, in conjunction with other

for data to arrive or change. Automation is especially well suited to the complexity of predicting, and then

analytic processing of vast amounts of data, but this is only a piece of the story.

each piece of data as it flows over the enterprise so that decisions can be made†some through automation

frame possible before the value of data decays further The third trend being driven by big data is the

existing data that have already been collected. Adaptable systems treat new sources of data coming constantly as

the means to improve analytical models, create better decisions, and drive more appropriate actions Chapter 1. 5:

allows the business to explore data to find questions worth answering. This stands the traditional business

ended with information technology structuring data to answer those questions in a very repeatable way typically as dashboards.

capturing all data available so that multi-structured and iterative discovery can take place that reveals information

lets the data speak for themselves Humans are suited extremely well to visual analysis Our brains are wired to very rapidly assimilate what we

data and the human mind make for a highly efficient combination. Most importantly, visualized data have the

effect of engaging the nontechnical but business-savvy human in the iterative process of discovering exploitable

technical resources and, specifically, on data scientists The second hurdle that organizations face is the

when the data are increasing in size, speed, and complexity. Unfortunately, when people talk about â€oebig

to the beginning of computerization when data were processed as batches of transactions that represented a

terms fails to take into account all of the data being created everywhere, every day. This compartmentalized view can also deprecate data that may not appear

useful or valuable or may be difficult to process. At a point in the future, organizations will very likely look back

and wish they had considered all data when deciding what to store. When we consider data without specific

boundaries, we can focus our efforts on linking data together and analyzing them more broadly.

We will probably find the data have value for a wider range of people in the organization than originally anticipated

When we consider all data, we can see the value of discovering the connectivity of data.

This brings into consideration different data types that are used to adorn our original data and make them more valuable as a

source of visual, predictive, and operational analytics Why does that matter? We have grown accustomed to

having instantaneous answers to our questions. As data grow, they have the very real likelihood of slowing down

how decisions are made. Nonlinear growth taxes our systems and creates the scenario that every day we get

bogged down more as untapped data sources become newly available, our clever automations become less

An all-data approach allows the organization to see today†s information as the best we have in the

DATA LATENCY ANALYSIS LATENCY DECISION LATENCY Business event Data stored Information delivered Action taken Figure 1:

The value-time curve Source: Hackathorn 2004 l Process entry and exit l Process intermediate steps

instead with the goal of using all of the data available to gain the best outcome.

results, using all available data takes into account data linkages and permits a broad analysis that allows the

constantly additive benefits of all data allows experts to be able to explore data to find their value.

For a retailer, that means being able to explore diverse data that include historical visits to the website as well as

transactions completed or shopping carts abandoned with the addition of geographical information from a mobile society, the retailer has an ability to understand

a need to ensure that data are not being used in a way that goes against the organization†s best interests.

Data are very powerful, and organizations need to ensure that information is being collected stored, analyzed,

are coupled tightly to the use of all of those data. On the reward side, data can be used to create far better

customer service by knowing the customers†needs and histories. They can be used to create more personalized

From this perspective, data can be used to engage the customer and to create a better relationship

trials of sample patients give way to all data about every patient Personalization and healthcare offer two standout

the need to protect data both at a discrete level and maybe even more importantly, at an aggregate level

movement, and dissemination of data, but in our haste to build out the largest datasets and the maximum

to govern data appropriately has existed, but unless organizations make the choice themselves or are pushed by legal or public pressure, the protection of personal

patient data despite that patient†s location within the hospital and despite the siloed information technology

of all the system needed to bring data together in a way that allows high-speed correlation, based on prior

analysis of sepsis data, so that medical staff can be alerted within lifesaving time frames. This company†s

of data that connect the customer†s customer and the supplier†s supplier. We are able to know significantly

data to monitor not just the arrival and departure of aircraft but also the aircraft altimeter and attitude in

order to provide additional layers of data that provide better insight on the nuanced status of the flight. 3 In a

data. A global logistics company must monitor discrete data such as package temperature, location, and time to

delivery that continually describe a shipment†s ambient conditions; furthermore, these data must be available

alongside expiration data and acceptable data ranges Those aggregate data form the basis for ensuring

non-stop compliance to local and international standards for moving items that require special handling Those same data ensure that contract terms are

being respected and provide the basis for improving profitability while decreasing waste and inefficiency within a contracted service.

It is a gift that keeps on giving, as detailed historical shipment data allow better

pricing of potential new contracts, making the logistics carrier more competitive and reducing the risk of

ability to manage all relevant data, logistics companies and their customers would be unable to effectively move

patterns in data that tell us what happened under a host of variables in the past. Visual analytics tell the retailer

systems, along with data coming from mobile devices That information is vitally important to knowing not only how to provide information

untapped data sources, using automation wherever possible, and creating less fragile data systems are crucial parts of ensuring the benefits of big data while

and secure ever-larger amounts of data Big data has a remarkable ability to change the

application logs, all of which generate machine data that provide insight into how, when, and why machines

data and how these data were used, affording critical oversight into potential illegal or unethical access and

use of data. Machine data are monitored by healthcare organizations to show compliance with Health insurance Portability and Accountability Act (HIPAA) standards

banks to prevent credit card fraud, and governments and corporations to watch for and prevent data loss

Today†s public, legislative, and legal sentiments may not be tomorrow†s; these attitudes will continue to

social acceptance of what data can and will be shared is changing and evolving, its impact on privacy and

and why data will be used will become more important as organizations seek to provide better services and products at both

in a Data-Driven Economy PETER HAYNES, Atlantic Council M-H. CAROLYN NGUYEN, Microsoft It is more than half a century since economist Fritz

effective processing and use of data, resulting in information that was used, for the most part to improve the performance of existing processes, businesses

Internet, the world is awash in data. By one estimate almost 3 zettabytes (3 billion terabytes) of information

2012 to 2017 machine to machine-machine data traffic is set to grow an estimated 24 times, to reach 6 Ã 1017 bytes

data in groundbreaking ways; and individuals will be empowered because they will be able to draw on a

availability of an adequate supply of data to enable the discovery of new knowledge. This requires policy

of notice and consent to restrict the collection of data predesignated as personal may overly restrict the supply

of data available, hampering the foundation for the new economy. Furthermore, what is considered personal and acceptable use are individual decisions, subject to

In reality, it is not the collection of data that is the source of potential harm, but its unconstrained use

for all the data that may be generated about them Together, the above factors necessitate a change in

the use of data related to them What is increasingly clear about an economy based on the collection, use,

fair value exchange in allowing the use of their data. 11 They have some expectation of

challenges of the data-driven economy. Most consumers understand that the discounts they receive via a loyalty

card are provided in exchange for data they supply to the retailer. But very few realize that the primary value

Rebalancing Socioeconomic Asymmetry in a Data-Driven Economy 68 The Global Information technology Report 2014 Â 2014 World Economic Forum

grounded in the exchange of data, the ways in which those data are collected and analyzed will become

even more opaque to the consumer and the value exchange even harder to discern; trust will decrease

idea of what data exist about him or her and what is being done with these data.

Some will have been actively volunteered by the consumer; some will have been obtained passively, with or without his or her

information, the real values of both the data provided and the service returned (in other words, the underlying

value online data. The most comprehensive survey of valuation methodologies was presented in a recent OECD study (on which the authors of this chapter

data might be valued in the market (refer to Boxâ 1). 12 However, each of these methods has significant flaws

sheer scale of its data holdings, has yet to find the Holy Grail of social media data monetization.

Amazon, by contrast, collects far less personal information from individuals, but its business model is predicated on

Distinguishing personally beneficial uses of data from socially beneficial uses is a further challenge because each creates separate and significant value

this benefit directly to data involves some inspired approximation. And even though one estimate puts the

the ways in which data are valued today would consider such benefits an externality to be ignored

which data might be valued are largely irrelevant today because they have given already away their digital crown

of personal and other data to large corporations with little or no thought to its potential monetary value†and

example, provide it with data that have the potential to generate immense long-term value for the company;

•ascertaining the revenues or net income per data record •establishing the market prices at which personal data

•establishing the economic cost of a data breach •determining prices for personal data in illegal markets

pay to protect their data Source: OECD 2013 The Global Information technology Report 2014 69 Chapter 1. 6:

Rebalancing Socioeconomic Asymmetry in a Data-Driven Economy  2014 World Economic Forum In other words, under the current model, the greater

the role that data play in the global economy, the less the majority of individuals will be worth.

that a data-driven economy may become a contracting economy. Like Lanier, we believe that if a truly

sustainable data-driven economy is to be established the way in which data are traded between individuals

and corporations will require a major reset. For a data -driven economy to thrive, individuals would have to

receive fair/appropriate monetary compensation for each specific datum they provide, perhaps with additional payments whenever that datum produces incremental

only when commingled with other data, for example, and any payment/micropayment system would have to be

And a sustainable data-driven economy might also entail individuals paying fees (likely modest) for services they

concept of a data-driven economy†of undergoing this evolution cannot be overstated. Without it, the

Without fair value exchange for data along with inherent trust in the data ecosystem, everyone will

ultimately lose†consumers, corporations, and countries alike. Establishing a system of fair value exchange will

ECONOMICALLY VIABLE€ DATA ECOSYSTEM We believe that an essential element of the foundation that can enable user trust and fair value exchange

such an architecture, data are accompanied logically by a â€oemetadata tag†that contains references to the

permissions and policies associated with the data along with related provenance information, specified in an extensible and interoperable markup language

The metadata is logically bound to the data and cannot legally be modified unbound or for the entire

data lifecycle by any parties other than the user or as specified by, for example, a related policy or rules of a

in a decentralized data ecosystem†and consequently provides a foundation for both trustworthy data and

fair value exchange. Consider: metadata enables individuals to change their personal data preferences and permissions over time, prevent undesirable use of

previously collected data, address unanticipated uses and adjust to changing norms. Thus, if we consider

sustainability) into a data-driven economy, those data must be assigned monetary value, then metadata is the

its existence in the data ecosystem†enabling a more enlightened society in the digital space.

generation and use of data remains unanswered however, and requires considerably more research Such an approach is technologically non-trivial.

logically bound to data, it can also be unbound by bad actors (a situation similar to the vulnerability of today†s

policies that would govern how data can be used within†and shared across†trust boundaries, and how

among the multiple parties with claims on the data or claims to its monetary value. 16 Yet another, highly

as recommender systems or data intermediaries Achieving all this will require the specification of an interoperable metadata-based architecture that can

multiple data stakeholders to ensure its feasibility and inherent security, as well as its ability to enable

stakeholders in the data ecosystem, not only users. Data controllers and processors can more easily understand

and comply with permissions and policies defined for specific data. They can also establish a dynamic

economically viable and sustainable â€oemarketplace†in data that would ideally mirror the way in which fair value

exchange is established in the physical world. Solution providers can create applications and services that Chapter 1. 6:

Rebalancing Socioeconomic Asymmetry in a Data-Driven Economy 70 The Global Information technology Report 2014 Â 2014 World Economic Forum

value chain, yet still use data in privacy-preserving ways. Companies can develop metadata schemas that

fully describe data use, codes of conduct, and relevant policies to meet industry and regulatory requirements

auditability of data, along with a stronger and better -defined connection between the data and those policies

that govern its use Although metadata can help facilitate a data-driven economy, it cannot guarantee that entities handling the

data will honor the permissions and policies associated with them. However, when implemented as part of

a principles-based policy framework that provides guidance on trustworthy data practices†supplemented by voluntary but enforceable codes of conduct and

underpinned by legal redress†this is a flexible approach that holds the promise of satisfying the interests of

economic ecosystem in a data-driven economy, enabling the monetary value generated by data to be tracked

captured, and realized as payments to and from the ecosystem†s participants CONCLUSION AND WAYS FORWARD

in order to create a sustainable data-driven ecosystem technology and policy must work symbiotically. For that to happen, governments and their regulatory

coalition that will be required if the promise of a data -driven knowledge economy is to be realized fully.

Data are for value-added labor productivity 5 Bughin and Manyika 2013 6 Gens 2011 7 Cisco 2013

International Micro Data. Paper presented at the OECD Workshop on ICT and Business Performance, OECD, Paris, December 9

Global Mobile Data Traffic Forecast Update, 2012†2017, February 6. Cisco. Available at http://www. cisco. com/en/US/solutions/collateral/ns341/ns525

â€oea User-Centred Approach to the Data Dilemma: Context Architecture, and Policy. †In Digital Enlightenment Yearbook 2013

Rebalancing Socioeconomic Asymmetry in a Data-Driven Economy  2014 World Economic Forum PCAST (President†s Council of Advisors on Science and Technology

Rebalancing Socioeconomic Asymmetry in a Data-Driven Economy 72 The Global Information technology Report 2014 Â 2014 World Economic Forum

called data the new oil. Because it†s a fuel for innovation powering and energizing our economy. †1 These were the

Kroes noted, data comprise a fuel we have only just begun to tap This â€oenew oil†is certainly plentiful.

of data are generated by companies that capture information about their customers, suppliers, and operations. Networked sensors and software embedded

sources of data do not even include the billions of individuals around the world generating the same fuel

And the volumes of data are exploding. Mckinsey recently estimated that the data collected globally will

grow from some 2, 700 exabytes in 2012 to 40,000 exabytes by 2020.2 To put this into context, a single

exabyte of data equals a hundred thousand times all the printed material of the Library of Congress Definitions of big data vary greatly.

open data directive, which aims to give both citizens and member governments access to a raft of government

data. Governments understand that big data†s economic and social potential can grow only alongside continued

analytic capabilities for handling data, as well as the evolution of behavior among its users. Recent Mckinsey

the advanced analytical skills needed to put the data to good use. This workforce will need to be trained.

and the data they carry from cyberattacks. A further imperative is to build the trust of citizens,

to the type of data being considered. Consumers care more about their financial transactions and health

disclosing US government data collection practices and the extraction of data from a number of large Internet

companies have raised further public awareness about privacy issues and data protection in the online world If big data is to deliver on its promise, companies

control of data about their own person and preventing unnecessary listings and discriminatory behavior Individuals can exercise this control by explicitly giving

*These data are taken from the Special Eurobarometer poll published in 2011. Respondents were asked to select 4 out of 12 possible responses to the question of what should happen to

88%of Europeans believe that their data would be protected better in large companies that are obliged to name

40%banned from using such data in the future 39%compelled to compensate the victims  2014 World Economic Forum

They have a right to be informed if those data are to be used, and for what purpose.

and organizations using their data are required also to protect it from unauthorized use. There are strict

measures in place to protect medical data and credit information But the issue has become more complicated in

personal benefits can arise from sharing data, and many consumers are perfectly happy to give up some of their

follow to ensure a common, minimum level of data protection across member economies. The aim is to

enable the easier transfer of data among economies where the level of data protection regulation varies

or disclosure of their data, and that the data collected should be accurate, complete and up to date. 6

•Strict ex-ante requirements. Ex-ante requirements apply in Europe, where both the Council of europe

frameworks to protect data and privacy in their respective member countries. 7 These frameworks not only define what is regarded as personal data

and how such data can and cannot be used, but they also set organizational and technological

measures to protect the data gathered. Furthermore strict liabilities are in place relating to both companies and cooperation frameworks for

The frameworks stipulate that data from the European union may be transferred only to countries that have an appropriate level of

requirements of today†s data-intensive world. 9 In the United states, the Federal trade commission (FTC) has

Whatever approach is taken, we believe data protection and privacy regulation is becoming more and more important across the world, and countries and

protection and sector-specific data protection legal provisions Existing regulation is already the strictest globally

States enables free data transfer between compliant companies in the two regions RUSSIA AND CENTRAL ASIA MIDDLE EAST AND AFRICA ASIA PACIFIC

United arab emirates) already have data protection laws Morocco signed the Council of europe data protection convention in 2013 *establishing a general data protection

data can drive, while maintaining customer trust and data protection. These areas include: consent before collection, a definition of personal data, anonymization

Consent before data collection. A key principle in the European regulatory framework is need the to

obtain personal consent before data are gathered Anyone wanting to use an individual†s data must

first seek his or her permission. But with so much information now available and being gathered, seeking

data development. Cookies on the Internet are a simple example. Surfing the web would be more convenient

EU framework defines personal data as â€oeany data that can be attributed to an identifiable person either directly

data as â€oeinformation about an identified or identifiable individual. †Both these definitions mean that not only

data clearly identifying a person with information such as a name or address is considered to be personal

data, but also data that can be attributed to a person indirectly through some other measure, such as via

data world where a lot of data are interlinked, it can be difficult to know exactly when data become â€oepersonal. â€

Is it only data that identify a person with certainty, or does it also include data that identify someone with

high probability? How about a person†s actions Performance? Or buying behavior? To give a concrete

example, a US retail chain identified new parents as a very lucrative market segment. The chain analyzed their

of how to define which data are personal is the issue of data anonymization or sanitization.

Traditionally anonymous data have not been subject to data protection laws. However, in a big data world where

anonymized data can easily be linked up, it is not very hard to build a profile of a person without traditional

means of identification such as a name or address For example, a team at Harvard was able to identify

individuals from anonymized data in a genetics database by cross-referencing it with other public databases

therefore be argued that the use of anonymous data can potentially constitute an intrusion of privacy

Another question related to data anonymization is the right of companies to use the personal data already

in their possession and turn them into anonymized data that they sell to others. Some companies are selling their

customer data†such as location and application data of telecommunications companies†to other companies in anonymized and aggregated form for marketing

effectively by using these data to learn about their customers. Internet companies are also matching their

customer data and online habits with data from other companies to better target their online advertising. 15

When can data be considered anonymized? Does using a pseudonym make data anonymous? Are companies allowed to use anonymized data without the customer†s

consent, or must customers give their prior approval Should that consent be granted before use, or is it

enough to allow customers to opt out The right to be forgotten. The new EU data protection framework proposes introducing a right

for users to request that data controllers remove their personal data from their files. Although on paper it

sounds easy to remove personal data relating to an individual upon request, this may not be so easy

a great deal of data are stored in different places in the cloud for security reasons, and these data may

have been aggregated or amended into new forms such as statistical data. Thus removing some specific data from all systems upon request may be entwined

with the aggregated data. Clearly this is not such a straightforward task in a virtual environment, and there is

no single technical method to enable this easily. 16 Relevant jurisdiction. Data are used increasingly and stored across borders,

but regulation is still largely national in its scope and regulators lack jurisdiction in markets outside their own.

The uncertainty about jurisdictions creates problems for companies and  2014 World Economic Forum 1. 7:

Union that are handling data relating to European Union†based individuals Liability issues. In today†s world, companies

which stores its data within a cloud service operated by yet another. If data are leaked,

it can be very difficult to decide which company is liable The above remaining gray areas must be considered

When it comes to data protection, companies and other organizations will need regulatory certainty if innovation is to be encouraged

companies to transfer data between the two regions without further approval from EU-based regulators

data environment would be beneficial for all parties Whatever their approach to regulation, governments should promote industry self-regulation.

decisions about what data they do or do not share Providing transparent privacy policies or simply informing

the customer of the scope of data handling as well as requesting clear consent declarations from customers

data business opportunities. Technological tools help, as they can allow customers to adjust their privacy settings

the Free Movement of such Data. Available at http://eur-lex. europa eu/Lexuriserv/Lexuriserv. do?

and A. Hung Byers. 2011. â€oebig Data: The next Frontier for Innovation, Competition and Productivity. †Mckinsey Global

Doshi. 2013. â€oeopen Data: Unlocking Innovation and Performance with Liquid Information. †Mckinsey Global Institute, Mckinsey

Data Privacy Lab. White paper 1021-1 april 24. Available at http //dataprivacylab. org/projects/pgp /USC Dornsife/Los angeles times. 2012. â€oevoters Across the Political

Leveraging Data-Driven Innovation†s Potential PEDRO LESS ANDRADE JESS HEMERLY GABRIEL RECALDE PATRICK RYAN

models for data-driven innovation. For example businesses are developing ways for real-time weather information to be communicated to devices in the field

more than existing data from cell-tower installations. 2 The next phase of the Internet†s evolution has us on a

clear path toward a â€oerevolution of data. †3 Every year the costs associated with the production, collection

storage, and dissemination of data come down making those data more readily available. This process is fomented by the increasing migration of many social

and economic activities to the web. 4 More data are generated today than ever before; this is a positive

trend that will inevitably continue: 90 percent of the world†s information generated through the history of

while data generated per year is growing at a rate of 40 percent. 6 In this chapter we will focus on the social and

economic value of data, but from the point of view of use and purpose rather than volume. We will therefore

talk about data driven-innovation instead of â€oebig data, †and will provide case studies from different areas, with

a special consideration of how data-driven innovation in the public sector could improve policymaking. We

leverage the potential of data-driven innovation in their communities through forward looking policies WHY SPEAK OF DATA-DRIVEN INNOVATION

INSTEAD OF BIG DATA It has become axiomatic that more data are produced every year, and somehow this phenomenon has

driven commentators to call this revolution â€oethe age of big data. †However, what is commonly known as

as the use of data to build successful products and services, optimize business processes, or make more efficient data-based

uses of data have been key to developing new products and making more efficient decisions for quite a long

Crunching data, statistics, and trends in new ways has helped always change the way that entire

analysis of data: in 1793, the Farmer†s Almanac found a The opinions here are the views of the authors

sets up data as a negative because of the implication that â€oebig†is â€oebad. †Indeed, many common definitions of

not on the data itself, but instead on the evolution of computing, storage, and processing technologies. 11

Thus, what is important about data is not their volume, but how they may contribute to innovation

Data alone do not possess inherent value; instead it is the processing of data in innovative ways that brings new economic

and social benefits, and this value creates a virtuous circle to feed into more use of data-based decision

data that really matters. 13 One way to measure this value is to measure the socioeconomic metrics (or

use of data. The excitement that we are seeing with new deployments of data to fuel innovation is not just

because of the volume of data, nor is it about the data themselves. As pointed out by the Software and

Information Industry Association, â€oetransformative data can be big or small or even the †needle†of data found

in a giant haystack. †14 The truth is that data are data, and that has not

changed for centuries. When â€oebig data†is no longer a trendy concept, data will continue to drive innovation

and solutions for new problems will come from new ways of analyzing and interpreting data, regardless of

volume or our technological capacities to manage it In the next section, we will address what we see in the

future for data-driven innovation THE BENEFITS OF DATA-DRIVEN INNOVATION Many sectors benefit from data-driven innovation

healthcare (e g.,, diagnosis and treatment), financial services (e g.,, analyzing market trends and economic conditions), and transportation and public administration

e g.,, metrics on what citizens want and where economic development is headed), to name a few. In

one example, a philanthropic research center stores and analyzes the cancer genome and the sequences and mutations of more than 10,000 cancer cases to

understand the complexity of the disease. 15 In another recent project, a university-based group of academics

mined data from 60 years of historical weather records to identify the factors that are most predictive

and other technical data to identify and prevent fraudulent activity in online payments, bolstering trust

that scours data on every domestic flight for the past 10 years and matches it to real-time conditions. 18 Finally

However, because data-driven innovation takes place across various sectors of the economy and society, it is sometimes difficult to quantify its full

our ability to quantify the value of data, and this gap misleads policymakers in their drive to maximize

†goods†and †services. ††22 Data are neither a good nor a service and so they escape traditional economic

data: although the value often creates an economic reward, such measurements are not easy to make

One example of innovative data use that has a difficult-to-quantify economic value proposition is

have been compared with official historic influenza data from relevant countries with surprisingly high levels of accuracy,

data from Flu Trends are open, available for everybody 1. 8: From Big data to Big Social and Economic Opportunities

Johns hopkins university, for example, used these data to develop a practical influenza forecast model designed to provide medical centers with advance warning of the

data are crucial to keeping the wheel of innovation rolling by allowing others to access

and manipulate the data in transformative ways Similarly, the rapid collection and processing of information has helped in recent natural disasters.

University analyzed calling data of over 2 million mobile phones to detect the pattern of population movements

and responsible ways of analyzing big sets of data and equally ethical and responsible ways of using

data-driven innovation. Studies suggest that there is a direct connection between data-driven decision-making in business and improved firm performance.

Firms that adopt data-driven decision-making have an output and productivity that is 5 percent to 6 percent higher than

would be expected, given their other investments and their information technology (IT) usage. 29 Another study has shown that the use of Internet computing tools can

to leverage data-driven analysis without needing to make huge investments in their IT infrastructure. 30

In fact, the public sector is one the most data -intensive sectors of all. According to Mckinsey, the US

government had over 848 petabytes of data stored in 2009†second only to the manufacturing sector. 31

been established to maintain data about the nation Thus data-driven policymaking is not new, but the

opportunities brought by the advances on information and communication technologies make data-driven policymaking increasingly accessible to government

officials. Further, open government initiatives put these data into the hands of the public, facilitating a

new kind of transparency and civic engagement for curious and interested citizens. Data can benefit society

when they are open. 33 By providing a way to check assumptions, detect problems, clarify choices,

data-driven policymaking injects data -based rationality into the policymaking process, all of which could also create economic benefits. 34 According

Development (OECD), by fully exploiting public data governments in the European union could reduce administrative costs by 15 percent to 20 percent

In other words, data-driven policymaking moves policymaking out of the realm of intuition and dogma by

still does not fully exploit the potential of the data it generates and collects, nor does it exploit the potential

of data generated elsewhere. The â€oerevolution of data†still needs to make its way within government agencies

greatest potential to capture value from data-driven innovation, it also has one of the lowest productivity

industry in fully embracing data Box 1: Hong kong Efficiency Unit The Hong kong Efficiency Unit acts as a single point

data, which in fact provided important feedback on public service. Using a platform called the â€oecomplaints Intelligence System, †they now use the complaints

SETTING THE STAGE FOR A DATA-DRIVEN ECONOMY Apart from producing and using data for better

policymaking processes, the public sector can also play its part by promoting and fostering data-driven

innovation and growth throughout economies. To realize the potential of data-driven innovation, policymakers need to develop coherent policies for the use of data

This could be achieved by:(1) making public data accessible through open data formats,(2) promoting balanced legislation,

and (3) supporting education that focuses on data science skills Open data initiatives The use of data across sectors can drive innovation and

economic growth. However, many generators of data†including governments†do not share their data. As we

have seen, the public sector is one of the main producers and collectors of data. Open data initiatives that

make data in the public sector accessible to everyone contribute to data-driven innovation and create value for

governments. For example, aggregate public transport data may be used by developers to create useful applications for passengers (see Boxâ 2). This access to

real-time information could result in a greater number of passengers and, subsequently, to more income for

the transport authorities. In addition, accessible public data usually lead to better data because data users

can test structure and help to fix mistakes (see Boxâ 3 Improvements in the quality of data mean better data

-based solutions and, ultimately, better policy It is important to note that opening up public data

does not necessarily lead to the disclosure of personal data. Public data that may contain personal information

of citizens should be shared in an aggregate or fully de-identified way to protect citizens†privacy.

We will go into more detail around the discussions on privacy and personal data in the following section

How to get the best of data-driven innovation The increasing ease of linking and analyzing information

usually raises concerns about individual privacy protection. Personal data are the type that has drawn the most attention, from a regulatory point of view, in relation

to data-driven innovation. The challenge is to achieve a reasonable balance between individuals†right to privacy

and the emerging opportunities in data-driven innovation For this reason, in order to capitalize on opportunities for economic growth via innovation, flexible

and adaptable policies are needed. We need to focus on using datasets responsibly and ensuring that personally

identifiable information is accessible only by those who are authorized to do so, without limiting innovation In other words, privacy protection frameworks should

support secure and reliable data flows while enhancing responsible, risk-reducing behavior regarding the use of

between data-driven innovation and the principle of data minimization. This principle essentially states that

the collection of personal data should be limited to what is relevant and necessary to accomplish a specific

conclusion that fewer data are better A key dividend of data-driven innovation is the

possibility of finding new insights by analyzing existing data and combining them with other data.

This can sometimes blur the lines between personal and non -personal data, as well as the uses for which consent

data should be based on the real possibility of identifying an individual during the treatment of data. 37 This is why

applying existing approaches to personal data may result in overly broad definitions that can have unintended negative consequences for data-driven innovation

For the same reason that combining and correlating datasets is a key feature of data-driven innovation, the full

potential of data collected may not be clear at the time of collection. A consent model that is appropriate to the

data-driven economy should provide a path for individuals to participate in research through informed consent.

In this model, they would become aware of the benefits of their participation as well as potential privacy risks.

reason, the legislative considerations for data collection should not assume that less is always more and should

take into consideration the data-intensive direction of some of the economy†s growing sectors

School studied the relationship between transit data format and accessibility and the number of applications

reluctant agency to adopt open data, Washington DC€ s Metro, had only 10 applications serving its customers in

data analysis, information science, metadata and data visualization. The demand for engineers who specialize in technologies such as machine learning and natural

skills may hinder data-driven innovation†s full potential The United states itself will need up to 190,000 more

data-driven innovation†s potential benefits for economies CONCLUSION We have begun already to see the impact technology

which data may be generated, analyzed, and put to use. Thirty years ago we needed an army of data-entry clerks to feed

an information into a system; today, the information is already available in a machine-readable format.

Talking about this phenomenon as â€oebig data, †however, misses the true potential of data.

Instead, we should focus our discussion on data-driven innovation as this relates to the results and outcomes of data

use†from generating innovative products and service to improving business and government efficiency. Many other examples provided earlier have shown that data

-driven solutions have transformative social impact as well However, achieving the full potential of data-driven

innovation demands challenging the outdated paradigms established in a significantly less data-intensive world To achieve the maximum benefits from data-driven

innovation, policymakers must take into account the possibility that regulation could preclude economic and societal benefits. Decisions that affect data

-driven innovation are focused usually on the problems of privacy and data protection, but fail to consider

economic and social benefits that regulation could preclude. It is by looking at the big picture surrounding

big data that we can create the right environment for data-driven innovation, and that the individuals

organizations, and economies that may benefit from it can thrive NOTES 1 Gray 2013 2 The Economist 2013a

paradigm is actually not the increasingly large amount of data itself, but its analysis for intelligent decision-making. â€

Can open data lead to better data Moscow†s city government published about 170 datasets with geo coordinates at the Moscow opendata

After examining the data, Russian members of the Openstreetmap community found many errors and mistakes, including wrong geo coordinates.

reviewing open statistical data from the United Kingdom†s National Health Service, found that records said that

After this research was published, data systems were improved Source: Open Knowledge Foundation, 2013 The Global Information technology Report 2014 85

How Does driven Data Decisionmaking Affect Firm Performance? †April 22. http://dx. doi. org/10.2139/ssrn. 1819486

Promise of Data-Driven Policymaking in the Information Age April. Center for American Progress. Available at http://www

/Hemerly, J. 2013. â€oepublic Policy Considerations for Data-driven Innovation. †Computer (IEEE Computer Society) 46 (6:

Hilbert, M. 2013. â€oebig Data for Development: From Information-to Knowledge Societies. †January 15. Available at http://dx. doi

Rise of the Data-Driven Economy. †Progressive Policy Institute Policy Memo. October. Available at http://www. progressivepolicy

-Goods-and-Services the-Unmeasured-Rise-of-the-Data-Driven -Economy. pdf Manyika, J.,M. Chui, B. Brown, J. Bughin, R, Dobbs, C. Roxburgh, and

A. H. Byers. 2011. â€oebig Data: The next Frontier for Innovation Competition, and Productivity. †Mckinsey Global Institute Report

Mccormick University, 2012. â€oebig-Data Approach Leads to More Accurate Hurricane Forecasting. †News from Mccormick

2013. â€oeexploring Data-Driven Innovation as a New Source of Growth: Mapping the Policy Issues Raised by †Big Dataâ€. †OECD

Open Knowledge Foundation. 2013. â€oehow Can Open Data Lead to Better Data Quality? †September 3.

Available at http://blog. okfn org/2013/09/03/how-can-open-data-lead-to-better-data-quality

/Platzman, G. W. 1979. â€oethe ENIAC Computations of 1950: Gateway to Numerical Weather Prediction. †Bulletin of the American

Open Data. †Transparency Policy Project, Harvard Kennedy School, June. Available at http://www. transparencypolicy. net

the Economic and Social Value of Data. †SIIA White paper Available at http://goo. gl/QWJGHY

Sims, D. 2011. â€oebig Data Thwarts Fraud. †O†Reilly Strata, February 8. Available at http://strata. oreilly. com/2011/02/big data-fraud

Talbot, D. 2013. â€oebig Data from Cheap Phones. †MIT Technology Review, April 23. Available at http://www. technologyreview. com

United nations. 2012. â€oebig Data for Development: Challenges & Opportunities. †Global Pulse, May. Available at http://www

exaggerated hype surrounding â€oebig data, †the fundamental assertion is true: data†and the decisions

data are indeed staggering: productivity-led savings worth US$300 billion a year for the US healthcare

honed by the monthly clickstream data of 45 million online shoppers, tailors offerings to online

data. The total market for big data hardware, software and services in 2012 was US$11. 5 billion, whereas the

types of information†transactions, log data, mail documents, social media interactions, machine data geospatial data, video and audio data, to name just a

few†much of which is â€oeunstructured. †Traditional types of business data were available in a format that was

structured and could have been automatically analyzed†for example, a spreadsheet quantifying customer returns of different products at different stores over

time. However, much of the value in big data exists in unstructured information†for example, the transcript of a

Synthesizing unstructured data from numerous sources and extracting relevant information from it can be as much art as science

data requires an extremely diverse set of skills†deep business insights, data visualization, statistics, machine learning, and computer programming.

Flawed data governance Big data is not a substitute for†much less a solution for†flawed information management practices

If anything, it requires much more rigorous data governance structures. Without those improvements information technology (IT) systems that have not been

upgraded to handle large volumes of data are likely to collapse under the sheer weight of the data being

processed. Surveys suggest that business leaders are excited often more about the potential of big data Box 1:

that supports the processing of large data sets in a distributed computing environment. Hadoop is written in the Java programming language and is a top-level

stores data tables as sections of columns of data rather than as rows of data,

as in most relational databases, providing fast aggregation and computation of large numbers of similar data items

•HDFS: A distributed, scalable, and portable file system written in Java for the Hadoop framework

of Hadoop, providing data summarization, query and analysis. It permits queries over the data using a

familiar SQL-like syntax •Flume: A tool for collecting, aggregating, and moving large amounts of log data from applications to

Hadoop •Mahout: A library of Hadoop implementations of common analytical computations •Oozie: A workflow scheduler system developed to

statisticians and data miners for developing statistical software and data analysis •Sqoop: A tool facilitating the transfer of data from

relational databases into Hadoop •Zookeeper: A centralized service for maintaining configuration information, naming, providing distributed

Lack of a data-driven mind-set Because mind-set can be hard to pin down, its power

data investments to deliver value if business leaders do not have driven a data mind-set†that is,

if they do not believe that it is important for decisions to be based on cold, hard numbers rather than gut feel and experience

data-driven business leaders will have a tremendous incentive to treat data, and therefore the IT and analytics professionals who help deliver it in an

understandable form, as a strategic asset. And these leaders will make it a priority to ease the flow of data

across organizational silos Lack of technical know-how Big data represents a convergence of IT and data science

where data reside on main memory as opposed to disk storage). ) Data science includes, among many other areas

machine learning (systems that learn from data) and data warehousing. Big data professionals are expected to be familiar with both disciplines,

The onus of collecting data should be shared by the IT and analytics teams, but analysis must be the sole

and how can data help you with it? †are a good place to start.

fundamental questions that can unlock the value of data For instance, marketing professionals could ask, â€oewhat

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 Step 3: Take stock of all data â€oeworth analyzing. â€

Valuable business insight can come from many sources including social media feeds, activity streams, and â€oedark data†(data that are currently unused but that

have already been captured), machine instrumentation and operational technology feeds. It is important to explore these sources and to experiment with new

Organizations†data typically fit into four buckets •Operational data, such as data emanating from smart grid meters, embedded systems (examples

include microwave sensors and chips inserted in automobiles), transactions logs (such as payment transactions), radio-frequency identification chips

•Streaming data, such as computer network data phone conversations, and so on •Documents and content, such as PDFS, web

data and for which the potential payback is high Functions such as marketing, customer service, supply chain management,

In comparing views of data from a traditional business intelligence perspective versus a big data one, consider the following the questions:

What data are we capturing today? What are the limitations of this kind of structured data?

What extra value will we get by collecting external, context-specific, and unstructured data? Where will we find data

and how will we collect them? Would our business act upon the insights 0 5 10 15 20 25 30 35 40 45 50

0 10 20 30 40 50 Figureâ 1: Potential payback of big data initiatives Source: Gartner, 2013

in mind the complexity of both the type of data and the type of analysis the data will require

As we mentioned above, much of what is meant by â€oebig data†is unstructured information†data that

traditionally have been impossible to break down and categorize as they are collected. Such data are not only difficult to analyze

but can also be misinterpreted easily when taken out of context. Thus it makes sense to

experiment in the beginning with data that are relatively easy to analyze Different types of analysis also present varying

data flows. Many traditional and even state-of-the -art technologies were designed not for today†s or

tomorrow†s level of data volume, velocity, and variety Even as datasets grow exponentially along those

and train data scientists and analysts in Hadoop programming, or to buy an enterprise-ready version of Hadoop

business analyst, programmer, data scientist, and data visualizer†will need to have cross-functional familiarity Building this team is a five-step process

data-crunching capabilities, and make data-driven decisions •Hire people with needed skills if they are not

available or cannot be acquired by cross-training existing employees •Hire people with related skills if the needed skills

common data scientists •Start small and scale up. In the beginning, your needs will be modest. A few hires may be adequate

within enterprises†Chief Data Officer, Chief Digital Officer Chief Analytics Officer, and so on. That said, the structure

line responsibilities, a CDO (whichever flavor, Data or Digital) or a CAO would have little leverage to execute the

data capabilities and optimize its initiatives Instead, big data and business analytics expertise should fall within existing functions†for example

-sector open data. We believe that open data will be an essential characteristic of future public policy.

It is important that such a vision percolate down from the top to garner support from ministries and civil servants alike

so that open data initiatives function effectively Communicating from the very top that open data is an essential characteristic of public policy is crucial

Furthermore, governments should create an easy-to -use platform for the public to access the data in a form

that is easily digestible and ready for analysis. It is also advisable to develop rules and regulations for taxing the

commercial use of open data Governments should spearhead the effort to ensure the privacy and security of personal data.

is gaining new insights from analyzing and combining data on Hadoop with data from traditional databases to turn its

marketing staff from â€oemad Men†to â€oemath Men. †•A US-based provider of business outsourcing solutions

analysis of data •Australia-based telecommunications companies use big data to determine which of their customers are less likely

to pay their bills, allowing them to focus collection efforts on that group rather than across the whole customer

business and for gathering data it sells to its clients. It now also sells this big data platform through its newly

local seed and crop protection data from its test sites to provide a service that generates field performance

The plan should identify all government data worth analyzing, define data collection responsibilities, outline steps to ensure data quality,

and determine where big data technologies and analysis capabilities should be first deployed Finally, each government should establish a big

data center of excellence (BDCOE. The BDCOE should be the focal point of expertise, long-range thinking and

policy formulation, and training and development. It should also be the repository of best practices.

authority on all matters related to data management CONCLUSION Big data analytics is not a passing fad. It will be a

Data (the research arm of HCL Technologies ††â€. 2013b. CIO Straight Talk Issue 3. Quincy, Mass, US and

A. Hung Byers. 2011. â€oebig Data: The next Frontier for Innovation Competition, and Productivity. †Mckinsey Global Institute Report

variables matches that of the data tables in the next sec -tion of the Report, which provide descriptions, rankings

indicated in the corresponding data table. For more in -formation on the framework and computation of the NRI

ONLINE DATA PORTAL In complement to the analysis presented in this Report, an online data portal can be accessed via www

weforum. org/gitr. The platform offers a number of analyt -ical tools and visualizations, including sortable rankings

Following a correction on the data for indicators 8. 02 â€oegovernment online service Index†and 10.04 â€oee-participation Indexâ€, the country profile for Morocco has been

Data Tables  2014 World Economic Forum  2014 World Economic Forum How to Read the Data Tables

The Global Information technology Report 2014 251 The following pages provide detailed data for all 148 economies included in The Global Information

Technology Report 2014. The data tables are organized into 10 sections, which correspond to the 10 pillars of

the Networked Readiness Index (NRI Environment subindex 1st pillar: Political and regulatory environment 2nd pillar:

the data corresponds) follows the description. When the period differs from the base period for a particular

When data are not available or are outdated too â€oen/a†is used in lieu of the rank and the value

Because of the nature of data, ties between two or more countries are possible. In such cases, shared

ONLINE DATA PORTAL Complementing the analysis presented in this Report, an online data portal can be accessed via www. weforum

org/gitr. The platform offers a number of analytical tools and visualizations, including sortable rankings, scatter

Data Tables RANK COUNTRY/ECONOMY VALUE 1 3. 6 7 1 Singapore...6. 1 2 Finland...

Index of Data Tables Environment subindex 1st pillar: Political and regulatory environment...255 1. 01 Effectiveness of lawmaking bodies*..

Data Tables RANK COUNTRY/ECONOMY VALUE 1 3. 6 7 1 Singapore...6. 1 2 Finland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 3. 9 7 1 Luxembourg...5. 9 2 Singapore...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 3. 9 7 1 New zealand...6. 7 2 Finland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 3. 8 7 1 Singapore...6. 1 2 Finland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 3. 5 7 1 Finland...5. 9 2 Hong kong SAR...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 3. 8 7 1 Finland...6. 2 2 Singapore...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 United states...19 2 Luxembourg...20 3 Japan...21

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Ireland...21 1 Singapore...21 3 Rwanda...23

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Singapore...150 2 New zealand...216 3 Bhutan...225

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 9 7 1 Finland...6. 5 2 Sweden...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 2. 7 7 1 Hong kong SAR...4. 6

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Macedonia, FYR...8. 2 2 Timor-Leste...11.0

Data Tables RANK COUNTRY/ECONOMY VALUE 1 New zealand...1 2 Georgia...2 2 Macedonia, FYR...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Canada...1 1 New zealand...1 3 Armenia...2

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 9 7 1 Japan...6. 2 2 Taiwan, China...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Korea, Rep...100.8 2 Finland...95.5 3 United states...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 2 7 1 Switzerland...6. 1 2 Belgium...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 3. 5 7 1 Qatar...5. 6 2 Singapore...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Iceland3...54,817. 2 2 Norway3...29,244. 2 3 Canada3...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Azerbaijan...100.0 1 Bahrain...100.0 1 Bhutan...100.0

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Luxembourg...4, 088.5 2 Hong kong SAR...1, 426.6

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Iceland...3, 139.3 2 Netherlands...2, 803.7 3 Korea, Rep...2, 751.6

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 9 7 1 Iceland...6. 6 2 Finland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Liberia3...0. 00 2 Sierra Leone3...0. 00

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Sri lanka...8. 22 2 Israel3...8. 39 3 Bangladesh...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Argentina1...2. 00 1 Australia5...2. 00 1 Austria6...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 3. 7 7 1 Switzerland...6. 0 2 Finland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 0 7 1 Singapore...6. 3 2 Finland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Australia...133.0 2 Spain...128.5 3 Netherlands...128.4

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Estonia...99.8 2 Latvia...99.8 3 Azerbaijan5...99.8

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Hong kong SAR...229.2 2 Saudi arabia...187.4 3 Kazakhstan...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Iceland...96.2 2 Norway...95.0 3 Sweden...94.0

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Netherlands...97.2 2 Iceland...96.0 3 Bahrain...92.7

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Korea, Rep...97.4 2 Iceland...95.0 3 Netherlands...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Switzerland...39.9 2 Netherlands...39.8 3 Denmark...38.8

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Singapore...126.1 2 Japan...115.1 3 Finland...106.6

Data Tables RANK COUNTRY/ECONOMY VALUE 1 5. 5 7 1 Iceland...6. 7 2 United kingdom...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 7 7 1 Sweden...6. 2 2 Iceland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 3. 6 7 1 Switzerland...5. 8 2 Finland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Japan1...301.1 2 Sweden1...294.5 3 Switzerland1...293.5

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 8 7 1 Finland...6. 2 2 Switzerland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 4 7 1 United kingdom...6. 3 2 Korea, Rep...6. 2

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 0 7 1 Switzerland...5. 6 2 Finland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 0 7 1 United arab emirates...5. 9 2 Qatar...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Korea, Rep...1. 00 1 Singapore...1. 00

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 3 7 1 Rwanda...6. 2 2 United arab emirates...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 3 7 1 Finland...5. 8 2 Korea, Rep...5. 7

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Japan1...118.9 2 Finland1...110.1 3 Sweden1...88.8

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 1 7 1 Finland...5. 7 2 Qatar...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Luxembourg...57.2 2 Singapore10...51.0 3 Switzerland...49.8

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 2 7 1 Qatar...6. 1 2 United arab emirates...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 2 7 1 Iceland...6. 6 2 Finland...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 4. 1 7 1 Singapore...6. 1 2 United arab emirates...

Data Tables RANK COUNTRY/ECONOMY VALUE 1 Korea, Rep...1. 00 1 Netherlands...1. 00

The present section complements the data tables by providing additional information for all 54 indicators

the number of the data table that reports ranks and scores for all economies on this particular indicator.

*The data used in this Report represent the most recent available figures from various international agencies and national authorities at the time when the

data collection took place. It is possible that some data have been updated or revised since then

1st pillar: Political and regulatory environment 1. 01 Effectiveness of lawmaking bodies *How effective is your national parliament/congress as a law

Organization (UNESCO), UNESCO Institute for Statistics Data Centre (accessed November 5, 2013; World bank, World Development Indicators 2013 (December edition;

as of 2010 for the majority of countries (for others, data are available as of 2009,2011, or 2012.

which data are available. Full liberalization across all categories yields a score of 2, the best possible score.

Organization (UNESCO), UNESCO Institute for Statistics Data Centre (accessed November 5, 2013; World bank, World Development Indicators (December 2013 edition;

when data are missing, we apply a value of 99 percent for the purposes of calculating the NRI

Organization (UNESCO), UNESCO Institute for Statistics Data Centre (accessed November 5, 2013; national sources 6th pillar:

mobile broadband subscriptions via data cards or USB modems Subscriptions to public mobile data services, private trunked

mobile radio, telepoint or radio paging, and telemetry services are excluded also. It includes all mobile cellular subscriptions that

He is responsible for policy engagement and data -driven analytical research on technology issues related to the potential of IT and network connectivity for

data-driven innovation, and accessibility. She received a MIMS from the University of California, Berkeley.

of strategies that capture value from data, and how to embrace opportunities from big data/advanced analytics

in data governance and personal data management. Her work is focused on shaping relevant long-term technology policies globally by engaging with stakeholders and raising

powerful data scientists in the world, †along with Google founders and the CTO of the United states, and in 2013

this year together with data tables for each of the 54 indicators used in the computation of the NRI

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