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 : [email protected]
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 : [email protected]
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 : [email protected]
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 : [email protected]
Education
1999 – 2005 : SD Lamaholot
2005 – 2008 : SMP Santo Leo 2
2008 – 2011 : SMA Santo Leo 2
2011 – Present : Universitas Bina Nusantara