Looking Ahead 192

Source: “Cisco Visual Networking Index”, Cisco, 2014

Total Internet Data Traffic(In Exabytes per Month, 2013-2018)

Although it is hard to measure data accurately, it is clear that data is growing at exponential rates around the world. The sheer volume of information will require new analytical skills and more storage capacity. One type of data that could be measured is online data/mobile data

ONLINE DATA TRAFFIC VOLUME IS GROWING AT UNPRECEDENTED RATES with mobile data growing the fastest albeit from a low base

LEVERAGING BIG DATA31

60

90

120

30

0

105

135

15

45

75 Other Internet

Mobile

2018201520142013 20172016

18%

61%

CAGR(In %,

2013-2018)

0.40.81.2

2.0

3.43.44.24.3

5.0

9.6

Air TransportConsultingRetail Construction Publishing TelecomFoodProducts

Steel AutoMobile IndustrialInstruments

18%49% 39% 20% 19% 18%ProductivityIncrease 17%20% 17%36%

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0.40.81.2

2.0

3.43.44.24.3

5.0

9.6

Air TransportConsultingRetail Construction Publishing TelecomFoodProducts

Steel AutoMobile IndustrialInstruments

18%49% 39% 20% 19% 18%ProductivityIncrease 17%20% 17%36%

Big Data

Productivity and Sales Increase Due to Big Data Analytics(In % and US$ Billion, 2010)

VARIETY VOLUME VELOCITY

>2,500

North America

Europe

>2,000

Japan >400Rest ofAPAC >300

China >250Middle East

>200India>50

Latin America

People to Machine

Virtual Communities

Social Networks

Web logs

Archives

Digital TVs

E-Commerce

Sensors

GPS devices

Surveillance Cameras

People to People

Machine to Machine

1.2 Billionusers worldwide

196.3 Billionemail sent per day

2.095 Trilliongoogle searches in

2014

Amount of Big Data Stored across the Globe(In Petabytes, 2011)

Sources- Upper and Lower Charts: “Big data: The next frontier for innovation, competition, and productivity”, McKinsey Global Institute, 2011; “Email Statistics Report 2014-2018”, The Radicati Group, 2014; “Google Annual Search Statistics”, Statistic Brain, 2014; “Big Data”, Wipro Applying Thought, 2011

Analytics of interactions between people, between machines, and between people and machines have been helpful in identifying trends about consumers and have become a part of companies’ efforts to improve their productivity and sales. Still, to get insights from Big Data, companies have to overcome certain challenges, such as filtering noise

THE VOLUME, VELOCITY AND VARIETY OF “BIG DATA” have led to higher productivity and have enabled customization

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Insta

Frequency (per day) Large User Country Demographic Characteristics

2.5 Billion piecesof content shared

>100 Millionusers in India alone

925 Thousandnew users

500 Milliontweets

>172Thousand users

sign-up

1.5 Million usersin Thailand alone

23% of teensconsider it theirfavorite social

network

80%female

Facebook Youtube Google+ Twitter LinkedIn Instagram Pinterest

SaudiArabia

is the biggest userper capita

200 Million hoursof video watched

144 thousandhours uploaded

Sources- Upper and Lower Charts: Digital Insights Website; We Are Social; The Social Media Hat Website

The surge in the use of social media is producing a new stream of data. While social networks are mostly made up of young users, older users are joining at an even more rapid pace as smartphones prompt this age cohort to experiment with new applications. The number of active users of the largest social networks is now equivalent to the population of entire countries or continents; Facebook’s user base rivals the population of China. The frequency of use of social networks—with people accessing them, in many cases, several times a day—hints at the immense amount of information on the web

Leading Social Networks by Country/Region Equivalence in Terms of Number of Users(In Million People and In Million Active Users, Latest Available Data)

Key Statistics

UNSTRUCTURED DATA IS ALSO ON THE RISE, driven by the popularity of social networks, which now reach 28% of the global population

1,350

1,000

540

300 284187 182250

316

596

1,032

1,357

Google+LatinAmerica &Caribbean

40

China

30

YoutubeAfricaFacebook Oceania

Pinterest Snapchat

Malaysia

LinkedIn

Indonesia

Twitter

30

InstagramUS Pakistan

.

37

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Insta

Frequency (per day) Large User Country Demographic Characteristics

2.5 Billion piecesof content shared

>100 Millionusers in India alone

925 Thousandnew users

500 Milliontweets

>172Thousand users

sign-up

1.5 Million usersin Thailand alone

23% of teensconsider it theirfavorite social

network

80%female

Facebook Youtube Google+ Twitter LinkedIn Instagram Pinterest

SaudiArabia

is the biggest userper capita

200 Million hoursof video watched

144 thousandhours uploaded

Note: (1) PaaS refers to Platform as a Service, IaaS refers to Infrastructure as a Service, and SaaS refers to Software as a ServiceSource- Upper Chart: “Cloud Forecast and Methodology 2013-2018”, Cisco, 2014 Source- Lower Charts: “Global Mobile Data Traffic Forecast Update, 2013-2014”, Cisco, 2014

Breakdown of Cloud Traffic by Type(In %, 2013 and 2018)

Global Data Traffic over Time from Traditional and Cloud Data Centers(In Exabytes per Year, 2013-2018)

AN INCREASING AMOUNT OF DATA IS BEING STORED IN THE CLOUD, particularly from users in Asia and North America

For many organizations and people, the cloud has emerged as the answer to their data challenges – providing a scalable, flexible and automated platform that can handle both structured and unstructured data. The cloud is also a cost-effective way to support big data technologies (and data analytics) while reducing overhead costs. Risks remain, however, especially with respect to data security

7,072

1,941(27%)

2017

5,131(73%)

1,680(36%)

3,050(64%)

2014

3,828

1,551(41%)

2,277(59%)

2013

3,064

1,417(46%)

1,647(54%)

6,496(76%)

2018

CloudData Center

8,575

2,079(24%)

TraditionalData Center

2016

5,799

1,805(31%)

3,994(69%)

2015

4,730

Forecast

CAGR(In %, 2013-2018)

32

8

23

Consumer Business

2013

2018

59% 41%

66% 34% 2013

2018

22% 78%2013

2018

41% 44%

Public Private PaaS(1) Iaas(1) Saas(1)

31% 69%

15%

59% 28%13%

North AmericaAsia Pacific Western EuropeCentral and Eastern Europe Latin America Middle East and Africa

Cloud Trafficby Region(In %, 2013 and 2018)

2013 2018

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Source: Advanced Micro Devices

Computer Power Milestones(In Floating Point Operations per Second, 1960-2019)

INFORMATION MANAGEMENT HAS BEEN FACILITATED BY THE INCREASED POWER OF TRANSISTORS, which have become much faster

As processing power has increased, the estimate of how much time it will take to match the capacity of the human brain has decreased to around the year 2020. It would appear that Moore’s law--predicting the doubling of the number of transistors on an integrated circuit every two years--has been met. In fact, some experts believe we have reached “More Moore”, with transistor size and cost decreasing

1960

2009: Cray XT5-HE Goes Live

1970 1980 1990 2000 2010 2020

103

106

2008: PetaFLOPBarrier Broken

109

1012

1015

1018

1960: Univac LARC Goes Live

1976: Cray 1 Goes Live

1984: M-13 Supercomputer1993: CM-5/1024 Supercomputer

1999: ASCI Blue Pacific Goes Live

2003: Human Genome Mapped

2005: Millennium Run Simulation

2009: First World-class GPU-powered Supercomputer

2019: exaFLOPBarrier to be Reached?

Floating Point Operations perSecond (FLOP)

Equivalent computing power

Human Brain

Mouse Brain

Insect Brain

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Sources- Upper Chart: Direct Capital; AmazonSource- Lower Chart: “No Moore? A Golden Rule of Microchips Appears to be Coming to an End”, The Economist, Nov. 2013 (based on Linley Group)

97.0 100.0

219

5.5

660

7

1,049

0.0 95.5 96.594.5 98.5 99.594.0 97.593.593.0 95.0 99.096.0 98.0

1970s

1980

1984

1965COMPUTERS

CALCULATORS(Sharp)

PHOTOCOPIER(Xerox)

RAM (1 GB Data)

X Price in US$, Latest Available Data

MOBILE PHONES

Year of Reference% Decrease

Price of Sample Technologies over Time(In % Decrease, Various Years)

Transistors’ size will continue to decrease in the future but prices will rise slightly, according to the Linley Group. With price reductions as high as 100% for 1GB of RAM since 1980, and big improvements in computation capacity in personal computers, the handling, storage and analysis of huge amounts of data have become possible. Technological developments have also enabled the processing of data in the cloud where there have been additional savings

TRANSISTOR AND TECHNOLOGY-DEVICE PRICES HAVE DROPPED SUBSTANTIALLY; going forward transistors will continue to drop in size but prices may inch back up

Evolution of Transistors(In Millions of Transistors bought per US$ and In Nanometer Length, 2002-2015)

192020

16

11

7

4

2002 2004 2006 2010 2012 201520142008

3

180nm

130nm

28nm

16nm

90nm

65nm

40nm20nm

20MnNail

Comparative Sizes (In Nanometers)

Bacteria2,000 Hair

90,000

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