TOPIK-TOPIK LANJUTAN SISTEM INFORMASI

TOPIC 10: CLOUD COMPUTING AND BIG DATA

Kelvina Wibowo 1501143323

Ignatius Albert 1501144566

Albertus Andika 1501152050

Schwanova Lucki 1501161811

Felix 1501167866

Class / Group: 06 PLM / 04

Universitas Bina Nusantara

Jakarta

2014

ABSTRACT

Big data is becoming one of the most important technology trends that has the potential for dramatically changing the way organizations use information to enhance the customer experience and transform their business models. How does a company go about using data to the best advantage? What does it mean to transform massive amounts of data into knowledge? Big data is not an isolated solution, however. Implementing a big data solution requires that the infrastructure be in place to support the scalability, distribution, and management of that data. Therefore, it is important to put both a business and technical strategy in place to make use of this important technology trend by also understanding about cloud computing. What is cloud computing? Cloud computing is a model for enabling, convenient, on-demand network access to a shared pool of configurable computing resources (eg. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. The purpose of writing is to understand about why we should, how to implement, and what is the big data and cloud computing, along with how is the condition of big data and cloud computing in practical way, and why should we utilize cloud computing and big data. Analysis methodology used in the writing of this paper is data collection methods. Data collection method is done by literature study from several journals and website to support the purpose of writing this paper. The result achieved is to know about why we should, how to implement, and what is the big data and cloud computing, along with how is the condition of big data and cloud computing in practical way, and why should we utilize cloud computing and big data. Conclusion of this study is cloud computing enable rapid scalability with lesser cost and big data, if utilzed in the right way can provide tremendous results for the company.

Keyword

Information, communication, technology, prospect, career, professional, banking.

Table of ContentsABSTRACT................................................................................................................................1

CHAPTER 1...............................................................................................................................1

Introduction.................................................................................................................................1

1.1 Background..................................................................................................................1

1.2 Scope............................................................................................................................1

1.3 Purpose and Benefits....................................................................................................2

1.3.1 Purpose..................................................................................................................2

1.3.2 Benefits.................................................................................................................2

1.4 Methodology................................................................................................................2

1.5 Systematic of Writing...................................................................................................2

CHAPTER 2...............................................................................................................................4

Literature Review........................................................................................................................4

2.1 Theory / General................................................................................................................4

2.1.1 Definition of Big Data................................................................................................4

2.1.2 Definition of Cloud Computing..................................................................................5

2.2 Benefits of Big Data........................................................................................................10

2.2.1 For Individual...........................................................................................................10

2.2.2 For Community.........................................................................................................11

2.2.3 For Organizations.....................................................................................................11

CHAPTER 3.............................................................................................................................13

Discussion.................................................................................................................................13

3.1 Sample of Cloud Computing Services............................................................................13

3.2 Cloud computing provider in Indonesia..........................................................................13

3.3 Fee structure the provider offer to use cloud computing................................................14

3.4 What type of data will be the source of Big data............................................................15

3.5 Structured data, unstructured data and semi structure data, give the example of each

type........................................................................................................................................17

3.6 How to use big data to give the benefit for company......................................................17

3.7 What is the reason not much company in Indonesia use cloud computing?...................18

3.8 How we can calculate the value of investment of Big Data............................................18

CHAPTER 4.............................................................................................................................19

Conclusion................................................................................................................................19

4.1 Conclusion.......................................................................................................................19

4.2 Suggestion.......................................................................................................................20

References.................................................................................................................................21

CURRICULUM VITAE.........................................................................................................22

CHAPTER 1

Introduction

1.1 BackgroundBig data is not a single market. Rather, it is a combination of data-management

technologies that have evolved over time. Big data enables organizations to store,

manage, and manipulate vast amounts of data at the right speed and at the right time to

gain the right insights. The key to understanding big data is that data has to be managed

so that it can meet the business requirement a given solution is designed to support. Most

companies are at an early stage with their big data journey. Many companies are

experimenting with techniques that allow them to collect massive amounts of data to

determine whether hidden patterns exist within that data that might be an early indication

of an important change. Some data may indicate that customer buying patterns are

changing or that new elements are in the business that need to be addressed before it is

too late. As companies begin to evaluate new types of big data solutions, many new

opportunities will unfold. For example, manufacturing companies may be able to monitor

data coming from machine sensors to determine how processes need to be modified

before a catastrophic event happens. It will be possible for retailers to monitor data in real

time to upsell customers related products as they are executing a transaction. Big data

solutions can be used in healthcare to determine the cause of an illness and provide a

physician with guidance on treatment options. Therefore, it is important to put both a

business and technical strategy in place to make use of this important technology trend by

also understanding about cloud computing. What is cloud computing? Cloud computing

is a model for enabling, convenient, on-demand network access to a shared pool of

configurable computing resources (eg. networks, servers, storage, applications, and

services) that can be rapidly provisioned and released with minimal management effort or

service provider interaction.

1.2 ScopeThis paper about big data and cloud computing is limited by the scope of the data

gathering from web on big data and cloud computing, especially in practical way.

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1.3 Purpose and Benefits

1.3.1 Purpose

to understand about why we should, how to implement, and what is the big

data and cloud computing, along with how is the condition of big data and

cloud computing in practical way, and why should we utilize cloud computing

and big data.

1.3.2 Benefits

The benefit that could be attained will listed in below:

- For The Writer

o Have an information about big data and cloud computing.

o We could understand more about the advantages and disadvantages

of using big data nad cloud computing.

1.4 MethodologyThe method that is being used in this paper is data collection methods. Data

collection method is done by literature study from several journals and website

to support the purpose of writing this paper.

1.5 Systematic of WritingChapter 1: Introduction

In this chapter explains about background of establishing this

paper, scope, purpose and benefits, methodology and systematic of

writing as well.

Chapter 2: Literature Review

In this chapter explains about all the theories that is going to be

used and as a framework within the writing and arranging in this

paper.

Chapter 3: Discussion

In this chapter describes about Electronic Customer Relationship

Management. We will discuss about the definition, advantages and

disadvantages of Electronic Customer Relationship Management.

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Chapter 4: Conclusion and Suggestion

In this chapter consists of essays about the conclusion that has been

done by completing research and suggestions that we found during

the research.

CHAPTER 2

Literature Review

2.1 Theory / GeneralInformation technology, or IT, describes any technology that powers or enables the

storage, processing and information flow within an organization. Anything involved with

computers, software, networks, intranets, Web sites, servers, databases and

telecommunications falls under the IT umbrella.

2.1.1 Definition of Big Data

According to (Gartner, 2014) big data is high volume, high velocity, and/or high variety

information assets that require new forms of processing to enable enhanced decision

making, insight discovery and process optimization.

2.1.1.1 Definition of Variety

According to (Merriam Webster, 2014), Variety is

 noun \və-ˈrī-ə-tē\

: a number or collection of different things or people

: the quality or state of having or including many different things

: a particular kind of person or thing

2.1.1.2 Definition of Volume

According to (Dictionary.com, 2014), Volume is

noun

a collection of written or printed sheets bound together and constituting a book.

one book of a related set or series.

a set of issues of a periodical, often covering one year.

History/Historical  . a roll of papyrus, parchment, or the like, or of manuscript.

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the amount of space, measured in cubic units, that an object or substance occupies.

2.1.1.3 Definition of Velocity

According to (Merriam Webster, 2014), velocity is NOUN (plural velocities)

The speed of something in a given direction: the velocities of the emitted particles

2.1.2 Definition of Cloud Computing

According to (Buyya, Vecchiola, & Thamarai, 2013), cloud computing is a

technological advancement that focuses on the way we design computing systems,

develop applications, and leverage existing services for building software. It is based on

the concept of dynamic provisioning, which is applied not only to services but also to

compute capability, storage, networking, and information technology (IT) infrastructure

in general. Resources are made available through the Internet and offered on apay-per-

usebasis from cloud computing vendors. Today, anyone with a credit card can subscribe

to cloud services and deploy and configure servers for an application in hours, growing

and shrinking the infrastructure serving its application according to the demand, and

paying only for the time these resources have been used.

2.1.2.1 Cloud Computing Model

According to (Aidan, Vredevoort, Lownds, & Flynn, 2012), there are three widely

accepted types of cloud service models. Each serves a different purpose. A business

may choose to use just one, two, or even all three of the cloud types simultaneously as

the need arises

2.1.2.1.1 Software as a Service (SaaS)

This model was around long before anyone started talking about cloud

computing. SaaS is an online application that you can use instead of one that

you install on a server or a PC. One of the oldest examples is webmail. People

have been using Hotmail, Yahoo! Mail, and others since the 1990s. Many

users of these services do not install an email client; instead they browse to

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the website of the service provider, log in, and correspond with their friends,

family, and colleagues.

Since then the variety of personal and business applications has exploded.

Rather than deploying an Exchange Server and a SharePoint farm in a small

business or a branch office (which requires servers and time), you can

subscribe to Microsoft Office 365 and deploy mailboxes and SharePoint sites

in a matter of hours, and users can access those services from anywhere on

the planet if they have Internet access.

Other examples include Salesforce CRM, Microsoft Dynamics CRM,

Microsoft Windows Intune, and Google Apps.

The strength of SaaS is that any user can subscribe to a service as quickly as

they can pay with their credit card. In addition to this, the company doesn't

have to deploy or manage an application infrastructure. The experience is not

that different from purchasing an app for a smartphone: you find something

that meets your needs, you pay for it, and you start using it—with maybe

some local configuration on the PC to maximize service. The disadvantage is

that these systems are not always flexible and may not integrate well with

other business applications your organization requires. SaaS is a generalized

service that aims to meet the needs of the majority of the market. The rest of

the market must find something that they can customize for their own needs.

2.1.2.1.2 Platform as a Service (PaaS)

Ask any software developer what their biggest complaint about deploying

their solutions is, and there's a pretty good chance they'll start talking about

server administrators who take too long to deploy servers and never provide

exactly what the developers need.

PaaS aims to resolve these issues. It is a service-provider-managed

environment that allows software developers to host and execute their

software without the complications of specifying, deploying, or configuring

servers. An example of a PaaS

Is Microsoft Windows Azure. Developers can create their applications in

Visual Studio and load them directly into Microsoft's PaaS, which spans

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many data centers across the globe. There they can use compute power, an

available and scalable SQL service, application fabrics, and vast amounts of

storage space.

A widely used example is Facebook. Many people tend their virtual farms or

search for clues to solve murders from their offices using software that

executes on Facebook. The developers of those games take advantage of the

platform that this expansive social network gives them, and they can rapidly

reach a large audience without having to invest huge amounts of time and

money to build their own server farms across the world.

The strength of this solution is that you can deploy a new application on a

scalable platform to reach a huge audience in a matter of minutes. The hosting

company, such as Microsoft, is responsible for managing the PaaS

infrastructure. This leaves the developers free to focus on their application

without the distractions of servers, networks, and so forth. The weakness is

that you cannot customize the underlying infrastructure. For example, if you

require new web server functionality or third-party SQL Server add-ons, this

might not be the best cloud service model to use.

2.1.2.1.3 Infrastructure as a Service (IaaS)

Because it is based on a technology most IT pros already know, IaaS is a

model of cloud computing that is familiar to them. IaaS allows consumers to

deploy virtual machines with preconfigured operating systems through a self-

service portal. Networking and storage are easily and rapidly configured

without the need to interact with a network administrator.

Virtualization, such as Microsoft Hyper-V, is the underlying technology that

makes IaaS possible. An IaaS cloud is much more than just server

virtualization. Network configuration must be automated, services must be

elastic and measured, and the cloud should have multitenant capabilities. This

requires layers of management and automation on top of traditional

virtualization.

The resulting solution allows consumers of the service to rapidly deploy

preconfigured collections of virtual machines with no fuss. Software

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developers or department administrators can customize the virtual machines

to suit the needs of the applications that will be installed in them. The

working environment is familiar and can easily integrate with almost all

technologies in an organization. The disadvantage for some is that there are

virtual machines to deploy and operating systems to create and maintain.

Subsequent chapters explain how Microsoft Virtual Machine Manager 2012

helps IaaS administrators deal with these concerns.

2.1.2.2 Cloud-Computing Deployment Models

According to (Aidan, Vredevoort, Lownds, & Flynn, 2012), each of these cloud

service models can exist in different locations and have different types of owners,

which dictate the deployment model of the cloud.

2.1.2.2.1 Private Cloud

A private cloud is entirely dedicated to the needs of a single organization. It can be

on or off premises. An on-premises private cloud resides in the owner's computer

room or data center and is managed by the organization's own IT staff. With the on-

premises approach, a company has complete control of the data center, the

infrastructure, and the networks. An off-premises private cloud takes advantage of

the existing facilities and expertise of an outsourcing company, such as a colocation

hosting facility. The off-premises approach is attractive to those organizations that

don't want to or cannot afford to build their own computer room or data center.

The advantage of a private cloud is that an organization can design it and change it

over time to be exactly what they need. They can control the quality of service

provided. With the right systems in place, regulatory compliance, security, and IT

governance can be maintained. The disadvantage of this deployment model is that it

can require a significant investment of expertise, money, and time to engineer the

solution that is right for the business.

Private clouds change the role of the IT administrators. Without a private cloud, they

are involved in many aspects of application deployment, including virtual machines

or physical servers, network configurations, network load balancers, storage,

installation of applications such as SQL Server, and so on. With a private cloud, their

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role becomes one of managing the centralized shared resources and managing the

service level of the infrastructure. IT admins create and manage the pools of reusable

components and systems that empower and enable businesses to deploy their own

services. This means that they provide smarter, higher levels of service that are more

valued by businesses.

2.1.2.2.2 Public Cloud

A public cloud is a multitenant cloud that is owned by a company that typically sells

the services it provides to the general public. Public clouds are readily available in

different types. There are huge geo-located presences such as Windows Azure,

Microsoft Office 365, and Amazon Elastic Compute Cloud. You can also find

smaller service providers that offer custom services to suit the unique needs of their

clients The big advantage of public cloud computing is that it is always ready to use

without delays. A new business application can be deployed in minutes. The business

does not need to invest in internal IT infrastructure to get the solution up and

running. Doesn't this sound like it might be the way forward? Doesn't it sound as if

outsourcing is finally going to happen and make IT pros redundant? Not so fast, my

friend!

There are a few issues that can affect the choice of an informed decision maker.

Where is the public cloud located? What nationality is the company that owns that

cloud? The answers to these questions can affect compliance with national or

industrial regulations. What sort of support relationship do you have with your

telecom provider? Do you think a public cloud service provider will be that much

different? Maybe the public cloud service provider has a fine support staff—or

maybe they prefer to keep you 5,000 miles away on the other end of an email

conversation. How much can you customize the service on the public cloud and how

well does it integrate with your internal services? Maybe your job as an IT engineer

or administrator is safe after all.

2.1.2.2.3 Cross-Premises Cloud

Things are not always black or white. The strengths of the private cloud complement

the weaknesses of the public cloud, and vice versa. Where one is weak, the other is

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strong. Most organizations can pick and choose the best offerings of both cloud

deployment models.

The cross-premises cloud, also known as a hybrid cloud, uses a private cloud and a

public cloud at the same time, with services spanning both deployments.

Recall the online retail company that needs to rapidly expand and reduce their online

presence for seasonal demands. This company can use a private cloud to store

sensitive customer information. The private cloud data can be integrated with a

public cloud such as Windows Azure. Azure provides huge data centers; application

administrators can quickly expand their capacity during the peak retail season and

reduce it when demand subsides. The company gets the best of both worlds: control

of security and compliance from the private cloud, cost-effective elasticity and

scalability from the public cloud, and a single service spanning both.

This book describes how to create such a cross-premises cloud using Virtual

Machine Manager 2012 and AppController.

2.1.2.2.4 Community Cloud

A community cloud is one that is shared by many organizations. This open cloud can

use many technologies, and it is usually utilized by organizations conducting

collaborative scientific research. It offers participants features of both the public and

the private cloud. Together, they can control the security and compliance of the cloud

while taking a shared risk. They also get access to a larger compute resource that

spans their cumulative infrastructures. Because of their open nature, community

clouds are extremely complex. A community cloud is a shared risk. Security and

compliance are only as strong as the weakest member, and there will be competition

for compute availability. Even in a private cloud, company politics are significant.

One can only imagine the role that politics will play in a community cloud that is

owned and operated by several state agencies.

2.2 Benefits of Big Data According to (Stanford University, 2014) the benefits of big data are:

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2.2.1 For Individual

Big data analysis provides a direct benefit to those individuals whose information is

being used. For example, the high degree of customization pursued by Netflix and

Amazon, which recommend films and products to consumers based on analysis of their

previous interactions. Data analysis benefits consumers and has been justified without

solicitation of explicit agreement. Similarly, Comcast’s decision in 2010 to proactively

monitor its customers’ computers to detect malware, and more recent decisions by

Internet service providers including Comcast, AT&T, and Verizon to reach out to

consumers to report potential malware infections, were intended to directly benefit

consumers. Also Google’s autocomplete and translate are based on comprehensive data

collection and real time analysis.

2.2.2 For Community

The collection and use of an individual’s data benefits not only individual, but also

community, such as users of a similar product of residents of a geographical area. Think

about Internet browser crash reports, which few users opt into not so much because of

real privacy concerns but rater due to a belief that others will do the job for them. Those

users who do agree to send crash reports benefit not only themselves, but also other

users of the same product. Similarly, individuals who report drug side effects confer a

benefit to other existing and prospective users.

2.2.3 For Organizations

Big data analysis often benefits those organizations that collect and harness the data.

Data-driven profits may be viewed as enhancing allocative efficiency by facilitating the

‘free’ economy. The emergence, expansion, and widespread use of innovative products

and services at decreasing marginal costs have revolutionized global economies and

societal structures, facilitating access to technology and knowledge and fomenting

social change. With more data, businesses can optimize distribution methods, efficiently

allocate credit, and robustly combat fraud, benefitting consumers as a whole. But in the

absence of individual value or broader societal gain, others may consider enhanced

business profits to be a mere value transfer from individuals whose data is being

exploited. In economic terms, such profits create distributional gains to some actors as

opposed to driving allocative efficiency.

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2.2.4 Society

Some data uses benefit society at large, for example, data mining for purposes of

national security. When weighting the benefits of national security driven policies, the

effects should be assessed at a broad societal level. Similarly, data usage for fraud

detection in the payment card industry helps facilitate safe, secure, and frictionless

transactions, benefiting society as a whole. And large-scale analysis of geo-location data

has been used for urban planning, disaster recovery, and optimization of energy

consumption.

CHAPTER 3

Discussion

3.1 Sample of Cloud Computing ServicesIaaS, PaaS and SaaS are cloud computing service models.

IaaS(Infrastructure as a service), as the name suggests, provides the computing

infrastructure, physical or (quite often) virtual machines and other resources like virtual-

machine disk image library, block and file-based storage, firewalls, load balancers, IP

addresses, virtual local area networks etc. Examples: Amazon EC2, Windows Azure,

Rackspace, Google Compute Engine.

PaaS (Platform as a service), as the name suggests, provides you computing platforms

which typically includes operating system, programming language execution

environment, database, web server etc. Examples: AWS Elastic Beanstalk, Windows

Azure, Heroku, Force.com, Google App Engine.

While in Saas (Software as a service) model you are provided with access to application

softwares often referred to as on-demand softwares. You don't have to worry about the

installation, setup and running of the application. Service provider will do that for you.

You just have to pay and use it through some client. Examples: Google Apps, Microsoft

Office 365.

As far as popularity of these services is concerned, they all are well known. It's the matter

which model suit your needs best. For example, if you want to have a Hadoop cluster on

which you would run MapReduce jobs, you will find EC2 a perfect fit, which is IaaS. On

the other hand if you have some application, written in some language, and you want to

deploy it over the cloud, you would choose something like Heroku, which is an example

of PaaS.

3.2 Cloud computing provider in IndonesiaThe Indonesia cloud computing market grew by 43% in 2012, to revenue of $31.4

million.

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To date, large telcos and key data center market participants in the country are playing a

significant role in encouraging the growth of this market. SaaS becomes a key

differentiator and means to generate a new stream of revenue for these players. For

example, PT Telekom.

Indonesia (Telkom) offers SaaS E-Office; recently, XL Axiata has partnered with 6

relevant cloud vendors (Huawei, IBM, Fujitsu, Microsoft, Intratec, and Mandawani) to

offer its upcoming X-Cloud. A key target market for Telkom and XL Axiata will be their

current corporate customers.

The telco market in Indonesia is led by Telkom Indonesia and IndoSat. Telkom Indonesia

provides its cloud offering through TelkomSigma. It offers infrastructure and applications

through its cloud portfolio. Its infrastructure services range from private cloud to public

cloud solutions, with bursting options. Its SaaS solutions include financial services

solutions, mobile workforce management, and office automation.

IndoSat has recently partnered with Dimension Data to launch an enterprise-class public

cloud service for the Indonesian market. The IndoSat Cloud, a public cloud

Infrastructure-as-a-Service (IaaS) offering, supports on-demand provisioning of cloud

servers with customized CPU, RAM, storage, as well as management of computers,

storage and networking.

3.3 Fee structure the provider offer to use cloud computingThe fee structure used for cloud computing usually depends on the number of

users and how much resources the enterprise wanted. For instance, Heroku provide

modular pricing, different resources, different support level, and different database

services will resulting in different pricing. It usually charged monthly or with contracts

that will be renewed annually. While google apps for business will cost the enterprise 5

US dollar/user/month.

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It is understood that the pricing arrangements for ongoing cloud-computing

services follow one of two models:

· Periodic charging which involves a set subscription fee based on the number of

users and an overall or per-user storage limit. This fee may be payable monthly, quarterly

or yearly. This offers a degree of certainty and the basic package is often sold cheap, with

service providers making most of their profit from upselling add-ons and premium

packages; and

· Usage-based charging where charges are paid according to the amount of usage of

the service by the customer. This can be attractive to customers, particularly where their

policies and practices enable them to make best use of the service and minimise wasted

charges. However this model makes charging less predictable and more unattractive to

the service provider, since the charges it receives will fluctuate from one charging period

to the next on a basis that is beyond its control.

3.4 What type of data will be the source of Big data1. Social network profiles—Tapping user profiles from Facebook, LinkedIn, Yahoo,

Google, and specific-interest social or travel sites, to cull individuals’ profiles and

demographic information, and extend that to capture their hopefully-like-minded

networks. (This requires a fairly straightforward API integration for importing

pre-defined fields and values – for example, a social network API integration that

gathers every B2B marketer on Twitter.)

2. Social influencers—Editor, analyst and subject-matter expert blog comments, user

forums, Twitter & Facebook “likes,” Yelp-style catalog and review sites, and

other review-centric sites like Apple’s App Store, Amazon, ZDNet, etc.

(Accessing this data requires Natural Language Processing and/or text-based

search capability to evaluate the positive/negative nature of words and phrases,

derive meaning, index, and write the results).

3. Activity-generated data—Computer and mobile device log files, aka “The Internet

of Things.” This category includes web site tracking information, application logs,

and sensor data – such as check-ins and other location tracking – among other

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machine-generated content. But consider also the data generated by the

processors found within vehicles, video games, cable boxes or, soon, household

appliances. (Parsing technologies such as those from Splunk or Xenos help make

sense of these types of semi-structured text files and documents.)

4. Software as a Service (SaaS) and cloud applications—Systems like

Salesforce.com, Netsuite, SuccessFactors, etc. all represent data that’s already in

the Cloud but is difficult to move and merge with internal data. (Distributed data

integration technology, in-memory caching technology and API integration work

may be appropriate here.)

5. Public—Microsoft Azure MarketPlace/DataMarket, The World Bank, SEC/Edgar,

Wikipedia, IMDb, etc. – data that is publicly available on the Web which may

enhance the types of analysis able to be performed. (Use the same types of

parsing, usage, search and categorization techniques as for the three previously

mentioned sources.)

6. Hadoop MapReduce application results—The next generation technology

architectures for handling and parallel parsing of data from logs, Web posts, etc.,

promise to create a new generations of pre- and post-processed data. We foresee

a ton of new products that will address application use cases for any kinds of Big

Data – just look at the partner lists of Cloudera and Hortonworks. In fact, we

won’t be surprised if layers of MapReduce applications blending everything

mentioned above (consolidating, “reducing” and aggregating Big Data in a

layered or hierarchical approach) are very likely to become their own “Big Data”.

7. Data warehouse appliances—Teradata, IBM Netezza, EMC Greenplum, etc. are

collecting from operational systems the internal, transactional data that is already

prepared for analysis. These will likely become an integration target that will

assist in enhancing the parsed and reduced results from your Big Data installation.

8. Columnar/NoSQL data sources—MongoDB, Cassandra, InfoBright, etc. –

examples of a new type of map reduce repository and data aggregator. These are

specialty applications that fill gaps in Hadoop-based environments, for example

Cassandra’s use in collecting large volumes of real-time, distributed data.

9. Network and in-stream monitoring technologies—Packet evaluation and

distributed query processing-like applications as well as email parsers are also

likely areas that will explode with new startup technologies.

10. Legacy documents—Archives of statements, insurance forms, medical record and

customer correspondence are still an untapped resource. (Many archives are full

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of old PDF documents and print streams files that contain original and only

systems of record between organizations and their customers. Parsing this semi-

structured legacy content can be challenging without specialty tools like Xenos.)

3.5 Structured data, unstructured data and semi structure data, give the

example of each typeStructured Data: Structured data refers to data that is identifiable because it is organized in a

structure. The most common form of structured data — or structured data records (SDR) — is

a database where specific information is stored based on a methodology of columns and rows.

Structured data is also searchable by data type within content. Structured data is understood

by computers and is also efficiently organized for human readers.

Unstructured or Semi-Structured Data: Refers to any data that has no identifiable structure.

For example, images, videos, email, documents and text are all considered to be unstructured

data within a data set. While each individual document may contain its own specific structure

or formatting that is based on the software program used to create the data, unstructured data

may also be considered “semi-structured data” because the data sources do have a structure

but all data within a data set will not contain the same structure.

Examples:

1. Word Doc & PDF’s & Text files - Unstructured data (Examples: Books, Articles)

2. Audio files - Unstructured data (Example: Call center conversations.)

3. eMail body - Unstructured data

4. Videos - Unstructured data (Example: Video footage of CCTV)

5. A Data Mart / Data Warehouse - Structured Data

6. XML - Semi Structured Data

3.6 How to use big data to give the benefit for company 1. Big Data can unlock significant value by making information transparent. There is still

a significant amount of information that is not yet captured in digital form, e.g., data

that are on paper, or not made easily accessible and searchable through networks. We

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found that up to 25 percent of the effort in some knowledge worker workgroups

consists of searching for data and then transferring them to another (sometimes virtual)

location. This effort represents a significant source of inefficiency.

2. As organizations create and store more transactional data in digital form, they can

collect more accurate and detailed performance information on everything from

product inventories to sick days and therefore expose variability and boost

performance. In fact, some leading companies are using their ability to collect and

analyse big data to conduct controlled experiments to make better management

decisions.

3. Big Data allows ever-narrower segmentation of customers and therefore much more

precisely tailored products or services.

4. Sophisticated analytics can substantially improve decision-making, minimise risks,

and unearth valuable insights that would otherwise remain hidden.

5. Big Data can be used to develop the next generation of products and services. For

instance, manufacturers are using data obtained from sensors embedded in products to

create innovative after-sales service offerings such as proactive maintenance to avoid

failures in new products.

3.7 What is the reason not much company in Indonesia use cloud

computing?We have slow internet connection, loss of privacy, undeveloped technical skills, and the lack

of knowledge for cloud computing. Also, we have relatively cheap IT staff. Plus, the

managers usually hesitate to let the legacy architecture and programming language go since

it’s all that they know.

3.8 How we can calculate the value of investment of Big DataIt’s difficult to say the value of big data investment since big data often include complex

algorithm and unrealized intangible benefit. But if we only want to measure from the revenue

perspective, we should measure the ROI of the investment. For example, ROI for Search

Engine Advertising. As long as users cost less than their conversion into customers,

advertising can be scaled without hesitation.

19

CHAPTER 4

Conclusion

4.1 Conclusion Cloud Computing Deployment model:

1. IaaS(Infrastructure as a service), as the name suggests, provides the computing

infrastructure, physical or (quite often) virtual machines and other resources..

2. PaaS (Platform as a service), as the name suggests, provides you computing

platforms which typically includes operating system, programming language

execution environment, database, web server etc.

3. Saas (Software as a service) model you are provided with access to application

softwares often referred to as on-demand softwares.

Example of Cloud Computing provider in Indonesia

1. PT Telkom Indonesia (Telkom) offers SaaS E-Office

2. XL Axiata has partnered with 6 relevant cloud vendors (Huawei, IBM, Fujitsu,

Microsoft, Intratec, and Mandawani) to offer its upcoming X-Cloud.

3. Telkom Indonesia and IndoSat. Telkom Indonesia provides its cloud offering

through TelkomSigma.

4. IndoSat has recently partnered with Dimension Data to launch an enterprise-class

public cloud service for the Indonesian market. The IndoSat Cloud.

Cloud computing pricing

1. Periodic charging which involves a set subscription fee based on the number of

users and an overall or per-user storage limit.

2. Usage-based charging where charges are paid according to the amount of usage of

the service by the customer.

Type of Data (based on the structure)

1. Structured Data: Structured data refers to data that is identifiable because it is

organized in a structure.

2. Unstructured or Semi-Structured Data: Refers to any data that has no identifiable

structure.

20

Big data benefits for company

1. Big Data can unlock significant value by making information transparent.

2. As organizations create and store more transactional data in digital form, they can

collect more accurate and detailed performance information on everything

3. Big Data allows ever-narrower segmentation of customers and therefore much more

precisely tailored products or services.

4. Sophisticated analytics can substantially improve decision-making, minimize risks,

and unearth valuable insights that would otherwise remain hidden.

5. Big Data can be used to develop the next generation of products and services.

4.2 SuggestionIndonesian companies should consider cloud computing as alternatives towards greener,

more scalable alternative to traditional IT resources utilization. Companies also should

consider to implement big data within the organization, especially for predicting market

sentiment and aiding the process of formulating strategic descision.

21

ReferencesAidan, F., Vredevoort, H., Lownds, P., & Flynn, D. (2012). Microsoft Private Cloud

Computing. Hoboken, NJ: John Wiley & Sons, Inc.

Bentley, L. D., & Whitten, J. L. (2007). Systems Analysis and Design for the Global Enterprise SEVENTH EDITION. New York: McGraw-Hill Companies, Inc.

Buyya, R., Vecchiola, C., & Thamarai, S. S. (2013). Mastering Cloud Computing: Foundations and Applications Programming. Waltham, MA: Elsevier Inc.

Dictionary.com. (2014, May 29). Variety. Retrieved from Dictionary.com: http://dictionary.reference.com/browse/volume

Gartner. (2014, May 29). Big Data Definition | IT Glossary. Retrieved from Gartner: http://www.gartner.com/it-glossary/big-data/

IDC. (2014, May 22). Press Release. Retrieved from IDC: http://www.idc.com/getdoc.jsp?containerId=prID24646214

Laney, D. (2014, May 30). Gartner Says Solving 'Big Data' Challenge Involves More Than Just Managing Volumes of Data. Retrieved from Gartner: http://www.gartner.com/newsroom/id/1731916

Merriam Webster. (2014, May 22). Communication. Retrieved from Merriam Webster: http://www.merriam-webster.com/dictionary/communication

Merriam Webster. (2014, May 22). Information. Retrieved from Merriam Webster: http://www.merriam-webster.com/dictionary/information

Merriam Webster. (2014, May 22). Technology. Retrieved from Merriam Webster: http://www.merriam-webster.com/dictionary/technology

Merriam Webster. (2014, May 29). Variety. Retrieved from Merriam Webster Dictionary: http://www.merriam-webster.com/dictionary/variety

Rainer, K. R., & Cegielski, C. G. (2011). Introduction to INFORMATION SYSTEMS Enabling and Transforming Business. Danvers: John Wiley & Sons, Inc.

Satzinger, J. W., Jackson, R. B., & Burd, S. D. (2005). Object-Oriented Analysis and Design with the Unified Process. Boston: Course Technology, Cengage Learning.

Satzinger, J. W., Jackson, R. B., & Burd, S. D. (2009). SYSTEM ANALYSIS AND DESIGN IN A CHANGING WORLD. Boston: Course Technology Cengage Learning.

Stanford University. (2014, May 29). Privacy and Big Data. Retrieved from Stanford Law Review: Individual

22

CURRICULUM VITAE

Name : Albert Komala

Birthplace and date : Jakarta - August 29, 1993

Gender : Male

Address : Kavling DKI Blok XI/42,

Meruya Utara, Jakarta Barat. 11620

Phone Number : +62 899 999 5352

Email : albertkomala@gmail.com

Education

1999 – 2005 : SDK Abdi Siswa, Jakarta

2005 – 2008 : SMPK Abdi Siswa, Jakarta

2008 – 2011 : SMAK Abdi Siswa, Jakarta

2011 – Present : Universitas Bina Nusantara, Jakarta

Pendidikan Non-Formal:2008-2011 : TOEFL 45 hours International Exam

Preparation High Intermediate Level.

TOEFL, Jakarta

Working Experience:6th of June 2012 : Commitee on “How To Print Money at Home”

seminar

June – July 2011 : Internship at CV.Sumber Makmur

12 – 13 September 2012 : Binus Online Job Expo 2012

11 – 12 September 2013 : Binus Online Job Expo 2013

Name : Albertus Andika

Birthplace and date : Jakarta, 18th of December 1991

23

Gender : Male

Address : Taman Alfa Indah blok i6/20.

Petukangan Utara. Pesanggrahan,

Jakarta selatan, 13360

Phone Number : +62 817 921 2766

Email : albertus.asia@gmail.com

Education

1998 – 2004 : SDK Sang Timur, Jakarta

2004 – 2008 : SMPK Abdi Siswa, Jakarta

2008 – 2011 : SMAK Abdi Siswa, Jakarta

2011 – Present : Universitas Bina Nusantara, Jakarta

Non-Formal Education:2008-2011 : TOEFL 45 hours International Exam

Preparation High Intermediate Level.

TOEFL, Jakarta

Working Experience:2011-2013 : PT. SmartFren Telecom, TBK. Telemarketer2013 : Binus Career, Event IT Support2012- 2013 : Binus Career, Part Time Promotional Team2012 : Vice President on “How To Print Money at Home”

Seminar2010- 2011 : CV Embrio Property Agent, Executive Marketing

Name : Felix Boenawan

Birthplace and date : 24th of December, 1993

Gender : Male

Address : Jl. Kelingkit 3 No. 83,

Rawa Buaya, Jakarta Barat, 11740

Phone Number : +62 8988 290 946

24

Email : felix.boenawan@yahoo.com

Education

1999 – 2005 : SD Lamaholot

2005 – 2008 : SMP Trinitas

2008 – 2011 : SMA Notre Dame

2011 – Present : Universitas Bina Nusantara, Jakarta

Non-Formal Education: 2000 – 2007 : ACE Kids English Course Intermediate LevelWorking Experience:2011 : Part Time Worker at PT KRAFT Indonesia2013 : English Tutor Bina Nusantara2012 – Present : Manager of Supernova Gaming Center

Name : Kelvina Wibowo

Birthplace and date : Jakarta, 23rd of September 1993

Gender : Female

Address : Jalan Lautze no. 6k. Jakarta

Pusat, 10710

Phone Number : 081932403390

Email :

Education

1999 – 2005 : SD Santo Yoseph

2005 – 2008 : SMP Santo Yoseph

25

2008 – 2011 : SMA Budi Mulia

2011 – Present : Universitas Bina Nusantara, Jakarta

Non-Formal Education:2011-2012 : Java Programming, LnT2006-2007 : English Little Star2000-2005 : ABC Patricia

Name : Schwanova Lucki

Birthplace and date : Jakarta, 13th of November 1993

Gender : Male

Address : Komplek Kresek Indah Blok T/18 Jl.

Rosalia RT 003/RW 012 Kel.

Duri Kosambi

Phone Number : +62 821 6803 8361

Email : limitless.arch@yahoo.co.id

Education

1999 – 2005 : SD Lamaholot

2005 – 2008 : SMP Santo Leo 2

2008 – 2011 : SMA Santo Leo 2

2011 – Present : Universitas Bina Nusantara