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TOPIK-TOPIK LANJUTAN SISTEM INFORMASI
TOPIC 11: BUSINESS INTELLIGENCE
Kelvina Wibowo
1501143323
Ignatius Albert
1501144566
Albertus Andika
1501152050
Schwanova Lucki
1501161811
Felix
1501167866
Class / Group: 06 PLM / 04
Universitas Bina Nusantara
Jakarta
2014
I
ABSTRACT
We are experiencing a major growth in the use of computer-based decision support. Major
companies such as IBM, Oracle, and Microsoft are creating new organizational units focused
on analytics to help businesses get more effectiveness and efficiency out of their operations. As
more and more decision makers become computer and Web literate, they are using more
computerized tools to support their work. At the same time, consumers and organizations are
generating unprecedented quantities of data through their interactions with each other. These
data stores can be used to develop and promote appropriate products, services, and promotion
to customers, and to optimize operations within an organization. The purpose of writing is to
understand about why we should, how to implement, and what is the Business Inteligence in
Indonesia, along with what is the common problem during Business Inteligence
implementation. 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 ICT in Indonesia, along with how is the condition
of IT career in Indonesia, and how ICT works in banking. Conclusion of this study is it’s
important to implement business inteligence to understand how well our company is doing and
what startegic action should we consider.
Keyword
Information, communication, technology, prospect, career, professional, banking.
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Table of Contents
ABSTRACT ................................................................................................................................I
CHAPTER 1 ............................................................................................................................... 4
Introduction ................................................................................................................................ 4
1.1
Background .................................................................................................................. 4
1.2
Scope ............................................................................................................................ 4
1.3
Purpose and Benefits ................................................................................................... 4
1.3.1
Purpose ................................................................................................................. 4
1.3.2
Benefits ................................................................................................................. 5
1.4
Methodology ................................................................................................................ 5
1.5
Systematic of Writing .................................................................................................. 5
CHAPTER 2 ............................................................................................................................... 7
Literature Review ....................................................................................................................... 7
2.1 Theory / General ............................................................................................................... 7
2.1.1 Definition of Big Data ............................................................................................... 7
2.1.2 Definition of Cloud Computing ............................... Error! Bookmark not defined.
2.2 Benefits of Big Data ....................................................................................................... 14
2.2.1 For Individual........................................................................................................... 14
2.2.2 For Community ........................................................ Error! Bookmark not defined.
2.2.3 For Organizations..................................................... Error! Bookmark not defined.
CHAPTER 3 ............................................................................................................................. 15
Discussion................................................................................................................................. 15
3.1 Sample of Cloud Computing Services ............................................................................ 15
3.2 Cloud computing provider in Indonesia ......................................................................... 16
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3.3 Fee structure the provider offer to use cloud computing ................................................ 17
3.4 What type of data will be the source of Big data ............ Error! Bookmark not defined.
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? .................. 20
3.8 How we can calculate the value of investment of Big Data ........................................... 22
CHAPTER 4 ............................................................................................................................. 23
Conclusion ................................................................................................................................ 23
4.1 Conclusion ...................................................................................................................... 23
4.2 Suggestion....................................................................................................................... 24
References ................................................................................................................................ 25
CURRICULUM VITAE ........................................................................................................ 26
CHAPTER 1
Introduction
1.1 Background
The business environment (climate) is constantly changing, and it is becoming more and
more complex. Organizations, private and public, are under pressures that force them to
respond quickly to changing conditions and to be innovative in the way they operate. Such
activities require organizations to be agile and to make frequent and quick strategic,
tactical, and operational decisions, some of which are ve1y complex.
Making such decisions may require considerable amounts of relevant data, information,
and knowledge. Processing these, in the framework of the needed decisions, must be done
quickly, frequently in real time, and usually requires some computerized support.
That’s why we have to utilize business intelligence as computerized support for managerial
decision making. Business inteligence will provide us both on the theoretical and
conceptual foundations for decision support, as well as on the commercial tools and
techniques that are available.
1.2 Scope
This paper about business Inteligence is limited by the scope of data gathering from web
on IT within Indonesia, especially in professional and banking sector.
1.3 Purpose and Benefits
1.3.1
Purpose
To understand about why we should, how to implement, and what is the Business
Inteligence in Indonesia, along with what is the common problem during
Business Inteligence implementation.
4
5
1.3.2
Benefits
The benefit that could be attained will listed in below:
-
For The Writer
o Have an information about ICT and the implementation method.
o We could understand more about the advantages and disadvantages
of using ICT.
1.4 Methodology
The 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 Writing
Chapter 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.
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CHAPTER 2
Literature Review
2.1 Theory / General
Information 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 Business Inteligence
According to (Turban, Sharda, Dalen, & King, 2010) Business intelligence (BI) is an
umbrella term that combines architectures, tools, databases, analytical tools,
applications, and methodologies. It is a content-free expression, so it means different
things to different people.
Part of the confusion about BI lies in the flurry of acronyms and buzzwords that are
associated with it (e.g., business performance management [BPM]). Bi's major
objective is to enable interactive access (sometimes in real time) to data, to enable
manipulation of data, and to give business managers and analysts the ability to
conduct appropriate analysis. By analyzing historical and current data, situations, and
performances, decision makers get valuable insights that enable them to make more
informed and better decisions. The process of BJ is based on the transformation of
data to information, then to decisions, and finally to actions.
2.1.1.1 Business Inteligence Architecture
According to (Luc, 2013), BI system has four major components: a data
warehouse, with its source data; business analytics, a collection of tools for
manipulating, mining, and analyzing the data in the data warehouse; business
performance management (BPM) for monitoring and analyzing performance;
and a user inte1face (e.g., a dashboard).
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Notice that the data warehousing environment is mainly the responsibility of
technical staff, whereas the analytical environment (also known as business
analytics) is the realm of business users. Any user can connect to the system
via the user interface, such as a browser. Top managers may also use the BPM
component and also a dashboard.
DATA WAREHOUSING the data warehouse and its variants are the
cornerstone of any medium-to-large BI system. Originally, the data
warehouse included only historical data that were organized and summarized,
so end users could easily view or manipulate data and information. Today,
some data warehouses include current data as well, so they can provide realtime decision support (see Chapter 2).
BUSINESS ANALYTICS end users can work with the data and information
in a data warehouse by using a variety of tools and techniques. These tools
and techniques fit into two major categories:
1. Reports and queries. Business analytics include static and dynamic
reporting, all types of queries, discove1y of information, multidimensional
view, drill down to details, and so on. These are presented in Chapter 3. These
reports are also related to BPM.
2. Data, text, and Web mining and other sophisticated mathematical and
statistical tools. Data mining (described further in Chapters 2 through 6) is a
process of searching for unknown relationships or information in large
databases or data warehouses, using intelligent tools such as neural
computing, predictive analytics techniques, or advanced statistical method.
Mining can be done on Web or textual data as well.
BUSINESS PERFORMANCE MANAGEMENT also referred to as
corporate
performance
management
(CPM),
business
pedormance
management (BPM) is an emerging portfolio of applications and
methodology that contains evolving BI architecture and tools in its core. BPM
extends the monitoring, measuring, and comparing of sales, profit, cost,
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profitability, and other performance indicators by introducing the concept of
management and feedback. It embraces processes such as planning and
forecasting as core tenets of a business strategy. In contrast with the
traditional DSS, EIS, and BI, which support the bottom-up extraction of
information from data, BPM provides a top-down enforcement of corporatewide strategy. BPM is the topic of Chapter 3 and is usually combined with
the balanced scorecard methodology and dashboards.
THE USER INTERFACE: DASHBOARDS AND OTHER INFORMATION
BROADCASTING TOOLS Dashboards (which resemble automobile
dashboards) provide a comprehensive visual view of corporate performance
measures (also known as key performance indicators), trends, and exceptions.
They integrate information from multiple business areas. Dashboards present
graphs that show actual performance compared to desired metrics; thus, a
dashboard presents an at-a-glance view of the health of the organization.
In addition to dashboards, other tools that broadcast information are corporate
portals, digital cockpits, and other visualization tools (see Chapter 3). Many
visualization tools, ranging from multidimensional cube presentation to
virtual reality, are integral parts of BI systems. Recall that BI emerged from
EIS, so many visual aids for executives were transformed to BI software .
Also, technologies such as geographical information systems (GIS) play an
increasing role in decision support.
2.1.1.2 Event Driven Alerts
According to (Fleisher & Blenkhorn, 2001), one example of ADS is an eventdriven alert, which is a warning or action that is activated when a predefined
or unusual event occurs. For example, credit card companies have built
extensive predictive analysis models to identify cases of possible fraud and
automatically alert credit card customers for verification of transactions when
unusual activity is noted (e.g. large purchase in an atypical or foreign location
when the customer does not have a history of such transactions). If a customer
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makes a large deposit, the bank may make an offer of a higher interest rate
Certificate of Deposit (CD) or investment automatically-5uch alerts are also
used in generating promotions based on completion of other purchases. Of
course, the alerts are also presented through BPM dashboards to appropriate
managers responsible for key performance indicators when there are
significant deviations from expected results.
2.1.1.3 Automated Descision Making
A relatively new approach to supporting decision making is called automated
decision systems (ADS), sometimes also known as decision automation
systems (DAS; see Davenport and Harris, 2005). An AI)S is a rule-based
system that provides a solution, usually in one functional area (e.g., finance,
manufacturing), to a specific repetitive managerial problem, usually in one
industry (e.g., to approve or not to approve a request for a loan, to determine
the price of an item in a store).
ADS initially appeared in the airline industry, where they were called revenue
(or yield) management (or revenue optimization) systems. Airlines use these
systems to dynamically price tickets based on actual demand. Today, many
service industries use similar pricing models. In contrast with management
science approaches, which provide a model-based solution to generic
structured problems (e.g., resource allocation, inventory level determination),
ADS provide rule-based solutions. The following are examples of business
rules: "If only 70 percent of the seats on a flight from Los Angeles to New
York are sold three days prior to departure, offer a discount of .x to
nonbusiness travelers," "If an applicant owns a house and makes over
$100,000 a year, offer a $10,000 credit line," and "If an item costs more than
$2,000, and if your company buys it only once a year, the purchasing agent
does not need special approval." Such rules, which are based on experience
or derived through data mining, can be combined with mathematical models
to form solutions that can be automatically and instantly applied to problems
(e.g., "Based on the information provided and subject to verification, you will
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be admitted to our university"), or they can be provided to a human, who will
make the final decision (see Figure 1.4). ADS attempt to automate highly
repetitive decisions (in order to justify the computerization cost), based on
business rules. ADS are mostly suitable for Frontline employees who can see
the customer information online and frequently must make quick decisions.
The demand for instant, on-demand access to dispersed information has
grown as the need to close the gap between the operational data and strategic
objectives has become more pressing. As a result, a category of products
called real-time BI applications has emerged (see Chapter 3). The
introduction of new data-generating technologies, such as radio-frnqu.ency
identification (RFID), is only accelerating this growth and the subsequent
need for real-time BI. Traditional BI systems use a large volume of static data
that has been extracted, cleansed, and loaded into a data warehouse to produce
reports and analyses. However, the need is not just reporting, since users need
business monitoring, performance analysis, and an understanding of why
things are happening.
These can provide users, who need to know (virtually in real time) about
changes in data or the availability of relevant reports, alerts, and notifications
regarding events and emerging trends in Web, e-mail, or instant messaging
(IM) applications. In addition, business applications can be programmed to
act on what these real-time BI systems discover. For example, a supply chain
management (SCM) application might automatically place an order for more
"widgets" when real-time inventory falls below a certain threshold or when a
customer i·elationship management (CRM) application automatically triggers
a customer service representative and credit control clerk to check a customer
who has placed an online order larger than $10,000.
One approach to real-time BI uses the DW model of traditional BI systems.
In this case, products from innovative BI platform providers (like Ascential
or Informatica) provide a service-oriented, near-real-time solution that
populates the DW much faster than the typical· nightly e:xtract/transfe1(load
(ETL) batch update does (see Chapter 2). A second approach, commonly
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called business activity management (BAM), is adopted by pure play BAM
and or hybrid BAM-miclclleware providers (such as Savvion, Iteration
Software, Vitria, webMethods, Quantive, Tibco, or Vineyard Software). It
bypasses the DW entirely and uses Web services or other monitoring means
to discover key business events. These software monitors (or intelligent
agents) can be placed on a separate server in the network or on the
transactional application databases themselves, and they can use event- and
process-based approaches to proactively and intelligently measure and
monitor operational processes.
Data warehouse and BI initiatives typically follow a process similar to that
used in milita1y intelligence initiatives. In fact, BI practitioners often follow
the national security model depicted in Figure 1.5. The process is cyclical
with a series of interrelated steps.
Analysis is the main step for converting raw data to decision supporting
information. However, accurate and/or reliable analysis isn't possible unless
other steps along the way have been properly addressed. The details of the
process and its steps are provided in Krizan (1999) and in Chapter 4.
Once a data warehouse is in place, the general process of intelligence creation
starts by identifying and prioritizing specific BI projects. For each potential
BI project in the portfolio, it is important to use return on investments (ROI)
and total cost of ownership measures to estimate the cost-benefit ratio. This
means that each project must be examined through costing associated with
the general process phases as well as costs of maintaining the application for
the business user. Additionally, the benefits estimations need to involve enduser examinations of decision-making impacts, including measures reflecting
benefits like cashflow acceleration. Some organizations refer to the project
prioritization process as a form of Bl governance (Matney and Larson, 2004).
A major governance issue is who should serve as decision makers involved
in prioritizing BI projects. The two critical partnerships required for BI
governance are: (1) a partnership between functional area heads and/or
product/service area leaders (Middles), and (2) a partnership between
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potential customers and Providers (representatives of the business side and
representatives from the IT side). Middles can look across an organization to
ensure that project priorities reflect the needs of the entire business; they make
sure a project does not just serve to suboptimize one area over others .
Customers can offer insight into the potential usefulness of the intelligence
generated in a project, and providers are important from the standpoint of
reflecting delivery realities. A typical set of issues for the BI governance team
is to address: (1) creating categories of projects (investment, business
opportunity, strategic, mandatory, etc.); (2) defining criteria for project
selection; (3) determining and setting a framework for managing project risk;
(4) managing and leveraging project interdependencies; and (5) continually
monitoring and adjusting the composition of the portfolio. Intelligence and
Espionage Although many believe the very term intelligence sounds like a
cloak-and-dagger acronym for clandestine operations dedicated to stealing
corporate secrets, or the government's CIA, this couldn't be further from the
truth. While such espionage does of course occur, we are interested in how
modern companies ethically and legally organize themselves to glean as much
as they can from their customers, their business environment, their
stakeholders, their business processes, their competitors, and other such
sources of potentially valuable information. But collecting data is just the
beginning. Vast amounts of that data need to be cataloged, tagged, analyzed,
sorted, filtered, and must undergo a host of other operations to yield usable
information that can impact decision making and improve the bottom line.
The importance of these topics increases every clay as companies track and
accumulate more and more data. For example, exacerbating the exponential
growth in the amount of raw data is the emergence of sensor data including
Radio Frequency JDentification (RFID). Applications based upon sensor and
location data will likely be among the most exciting and fastest growing
application categories for the next generation of BI specialists. That, coupled
with new approaches to synthesize information from text sources through
"text mining" and from the Web via
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Web mining (Chapter 4), suggests that organizations are on the verge of an
explosive new era of BI for decision support. BI has adapted a set of
nomenclature, systems, and concepts that clearly distinguish it from its
espionage-oriented counterpart of national and international intelligence!
That said, there are many analogies between the two, including the fact that
major effort must be expended to achieve the collection of repu.table sources
of intelligence, the processing of that intelligence for purity and reliability,
the analysis of raw intelligence to produce usable and actionable information,
and the mechanisms for the appropriate dissemination of that information to
the right users.
2.2 Benefits of Business Inteligence
According to (Turban, Sharda, Dalen, & King, 2010), as illustrated by the opening vignette,
the major benefit of BI to a company is the ability to provide accurate information when
needed, including a real-tin1e view of the corporate performance and its parts. Such
information is a must for all types of decisions, for strategic planning, and even for survival.
Thompson (2004) also noted that the most common application areas of BI are general
reporting, sales and marketing analysis, planning and forecasting, financial consolidation,
statuto1y repo1ting, budgeting, and profitability analysis.
Organizations are being compelled to capture, understand, and harness their data to support
decision making in order to improve business operations. Legislation and regulation (e.g.,
the Sarbanes-Oxley Act of 2002) now require business leaders to document their business
processes and to sign off on the legitimacy of the information they rely on and report to
stakeholders. Moreover, business cycle times are now extremely compressed; faster, more
informed, and better decision making is therefore a competitive imperative. Managers need
the right information at the right time and in the right place. Organizations have to work
smart. It is no surprise, then, that organizations are increasingly championing BI. The
opening vignette discussed a BI success sto1y at Norfolk Southern. You will hear about
more BI successes and the fundamentals of those successes in Chapters 2 through 6.
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CHAPTER 3
Discussion
3.1 Business Intelligence Architecture
According to (Luc, 2013), BI system has four major components:
1. DATA WAREHOUSING the data warehouse and its variants are the cornerstone
of any medium-to-large BI system. Originally, the data warehouse included only
historical data that were organized and summarized, so end users could easily view
or manipulate data and information. Today, some data warehouses include current
data as well, so they can provide real-time decision support.
2. BUSINESS ANALYTICS end users can work with the data and information in a
data warehouse by using a variety of tools and techniques. These tools and
techniques fit into two major categories:
a. Reports and queries. Business analytics include static and dynamic
reporting, all types of queries, discove1y of information, multidimensional
view, drill down to details, and so on. These are presented in Chapter 3.
These reports are also related to BPM.
b. Data, text, and Web mining and other sophisticated mathematical and
statistical tools. Data mining (described further in Chapters 2 through 6) is
a process of searching for unknown relationships or information in large
databases or data warehouses, using intelligent tools such as neural
computing, predictive analytics techniques, or advanced statistical method.
Mining can be done on Web or textual data as well.
3. BUSINESS PERFORMANCE MANAGEMENT also referred to as corporate
performance management (CPM), business process management (BPM) is an
emerging portfolio of applications and methodology that contains evolving BI
architecture and tools in its core. BPM extends the monitoring, measuring, and
comparing of sales, profit, cost, profitability, and other performance indicators by
introducing the concept of management and feedback. It embraces processes such
16
as planning and forecasting as core tenets of a business strategy. In contrast with
the traditional DSS, EIS, and BI, which support the bottom-up extraction of
information from data, BPM provides a top-down enforcement of corporate-wide
strategy. BPM is the topic of Chapter 3 and is usually combined with the balanced
scorecard methodology and dashboards.
4. THE USER INTERFACE: DASHBOARDS AND OTHER INFORMATION
BROADCASTING TOOLS Dashboards (which resemble automobile dashboards)
provide a comprehensive visual view of corporate performance measures (also
known as key performance indicators), trends, and exceptions. They integrate
information from multiple business areas. Dashboards present graphs that show
actual performance compared to desired metrics; thus, a dashboard presents an ata-glance view of the health of the organization.
3.2 Data Warehouse in Business Intelligence
Today's business Intelligence systems are contrasted to more classical way of information
gathering in mining and crunching the data in the most optimal manner. In short we can
say BI simplifies information discovery and analysis. In this way the company will have a
competitive advantage of business and intelligently using the available data in strategic and
effective decision making. it has the ability to bring disparate data under one roof with a
meaningful information and ready for analysis.
Business intelligence usually refers to the information that is available for the enterprise to
make decisions on. A data warehousing (or data mart) system is the backend, or the
infrastructural, component for achieving business intelligence. Business intelligence also
includes the insight gained from doing data mining analysis, as well as unstructured data
(thus the need for content management systems).
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3.3 Business Intelligence Application

Spreadsheets

Reporting and querying software: tools that extract, sort, summarize, and present
selected data

OLAP: Online analytical processing

Digital dashboards

Data mining

Data warehousing

Decision engineering

Process mining

Business performance management

Local information systems
3.4 Business Intelligence User
The user itself, usually the top management or sometimes middle management in
multinational company.
3.5 Storage Needed for Business Intelligence Application
To implement BI, firms will need to invest or upgrade their data storage infrastructure. This
includes Storage Area Networks (SAN), Network Attached Storage (NAS), Hierarchical
Storage Management (HSM), and silo-style tape libraries. The trend over the next five
years is for storage resources to be amalgamated into a single, policy-managed, enterprisewide storage pool.
3.6 Implementation Problem
1. Cross-Organizational Collaboration. Traditionally, any business initiative,
including a decision-support project, was focused on a specific goal that was limited
to a set of products or an area of the business. Where BI is concerned, collaboration
is not limited to departments within the organization; it requires integration of
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knowledge about customers, competition, market conditions, vendors, partners,
products and employees at all levels.
2. Strong business sponsors truly believe in the value of the BI project. They champion
it by removing political roadblocks. Without a supportive and committed business
sponsor, a BI project struggles for support within an organization — and usually
fails. Business sponsors establish proper objectives for the BI application, ensuring
that they support the strategic vision. Sponsors also approve the business-case
assessment and help set the project scope. If the scope is too large, sponsors
prioritize the deliverables.
Specifically for BI projects, business sponsors should also launch a data-quality
campaign in affected departments. This task goes to business sponsors because it’s
business users who truly understand the data.
Finally, business sponsors should run a project review session at assigned
checkpoints to ensure that BI application functionality maps correctly to strategic
business goals, and that its return on investment (ROI) can be objectively measured.
3. Dedicated Business Representation More often than not, the primary focus of BI
projects is technical rather than business-oriented. The reason for this shortcoming:
most BI projects are run by IT project managers with minimal business knowledge.
These managers tend not to involve business communities. Therefore, it’s not
surprising that most projects fail to deliver expected business benefits. It’s
important to note that usually 20% of the key businesspeople use BI applications
80% of the time.
IT supports the operational systems and provides awareness about the backlog of
BI requests from different groups. In addition to providing technical expertise, the
IT staff in the BI project team must analyze and present BI-related requests.
Operations managers and staff make tactical business decisions. They provide the
link between strategic and operational information, making them important during
some key phases of a BI project.
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4. Availability of Skilled Team Members
A BI project team lacking BI application implementation experience will most
likely fail to deliver desired results in the first iteration. Since most BI projects have
aggressive timelines and short delivery cycles, an inexperienced and unskilled team
is a risk that must be avoided.
5. BI Application Development Methodology
To succeed, BI projects must adhere to a plan with clearly defined methodologies,
objectives and milestones. In this respect, they are hardly unique. However, unlike
other undertakings, BI projects are not limited to a confined set of departmental
requirements. Rather, their purpose is to provide cross-organizational applications.
Therefore, BI methodologies and deliverables differ.
Like any project, BI starts out by answering some basic questions, such as: What
will be delivered? What are the benefits and expected ROI? What is the total cost?
When will it be delivered? Who will do it? The answers collectively define the BI
project as follows:
Project deliverables map goals to strategic business objectives. These deliverables
should be measurable in business terms. For example, “In order to increase sales
20%, the sales data merged with pipeline data must be available to sales teams
within three days of month’s end.”
ROI for a BI project must be derivable from project deliverables. Project sponsors
must measure the effectiveness of delivered BI applications after the completion of
each phase to determine whether the project is delivering the promised ROI. If it
isn’t, improvements must be made.
6. Planning BI Projects
Due to the nature of the beast, BI projects tend to hit more unknowns than other
ERP projects. Why? ERP projects implement the processes of an organization,
which in turn represent the functions.
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7. Business Analysis and Data Standardization
Data merge and standardization. The biggest challenge faced by every BI project is
its team’s ability to understand the scope, effort and importance of making the
required data available for knowledge workers.
That data consists of fragments in various forms and structure and must be merged
into a common standardized database — not a easy task. Therefore, data merge and
standardization activities must be planned and started at the beginning of the BI
project.
8. The Silver Bullet Syndrome
There is neither a single technology nor a technique that will resolve all the
challenges to reach the goal of a successful BI environment. That is to say, there is
no silver bullet. BI projects have an enormous scope and cover multiple
environments and technologies.
BI project teams must always consciously strive for the lowest possible number of
tools. This will allow different BI activities to map to the same overall roadmap.
Conclusion: Maximizing ROI. BI applications, if implemented efficiently and
properly, have tremendous payoff. They can help an enterprise increase its business
agility, decrease operating costs and improve its customer loyalty and acquisition.
3.7 Business Intelligence Benefit in Banking
Business Intelligence tools can be used by banks for historical analysis, performance
budgeting, business performance analytics, employee performance measurement,
executive dashboards, marketing and sales automation, product innovation, customer
profitability, regulatory compliance and risk management. Examples of these applications
are;
1. Historical Analysis (time-series)
Banks can analyze their historical performance over time to be able to plan for the
future. The key performance indicators include deposits, credit, profit, income,
expenses; number of accounts, branches, employees etc. Absolute figures and
growth rates (both in absolute and percentage terms) are required for this analysis.
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Analyzing, interpreting and acting upon on the information is a subjective exercise.
Hence, the BI vendor shifted their focus to customer relationship management
(CRM). CRM continues to be the center of the attraction to banks today and risk
management comes to second.
2. Customer Relationship Management (CRM):
CRM is at the center stage of BI in banking. However, it is becoming difficult to
assess whether it is driven by technology or business. Traditional or conservative
banking business models of Indian banking industry relied heavily on personal
relationships that the bankers of yesteryears had with their customers.
With CRM software tools, a bank can build a database about its customers that
describes relationships with sufficient detail so that management, salespeople,
service people, and even the customers can access information, match customers
needs with product plans and offerings, remind customers of service requirements,
check payment histories, and so on.
A CRM helps a bank with the following:

Find customers

Get to know them’

Communicate with them

Ensure they get what they want (not what the bank offers)

Retain them regardless of profitability

Make them profitable through cross-sell and up-sell

Covert them into influencers

Strive continuously to increase their lifetime value for the bank.
Most CRM solutions in banks are, in reality, sales automation solutions. New
customer acquisition takes priority over retention. That leads to the hypothesis that
it is BI vendors that are driving CRM models in banks rather than banks themselves.
Product silos have moved from manual ledgers to digital records. An
22
implementation model of ‘relationship’ in Indian banking industry is hard to see as
of today.
Most of the BI applications cater to the needs of the top management in banks. But,
line managers have a different set of BI requirements, which differ from those of
the top management. The line managers of banks require operational business
intelligence.
3.8 Example of BI Screenshot and How to Read the Data
Profit is good
The gross margin is nearing threshold, we
should increase it. Revenue to personnel is
also nearing threshold, perhaps we should
increase the personnel efficiency or fire
some.
23
CHAPTER 4
Conclusion
4.1 Conclusion
Business Intelligence Architecture:
BI system has four major components: a data warehouse, with its source data; business
analytics, a collection of tools for manipulating, mining, and analyzing the data in the data
warehouse; business performance management (BPM) for monitoring and analyzing
performance; and a user inte1face (e.g., a dashboard).
Business intelligence usually refers to the information that is available for the enterprise to
make decisions on. A data warehousing (or data mart) system is the backend, or the
infrastructural, component for achieving business intelligence.
Business Intelligence Application

Spreadsheets

Reporting and querying software: tools that extract, sort, summarize, and present
selected data

OLAP: Online analytical processing

Digital dashboards

Data mining

Data warehousing

Decision engineering

Process mining

Business performance management

Local information systems
Business Intelligence Implementation Problem
1. Cross-Organizational Collaboration.
2. Strong business sponsors truly believe in the value of the BI project.
3. Dedicated Business Representation.
4. Availability of Skilled Team Members
5. BI Application Development Methodology
24
6. Planning BI Projects
7. Business Analysis and Data Standardization
8. The Silver Bullet Syndrome
4.2 Suggestion
Indonesian companies should consider business intelligence as enhancements towards
aiding or even automatizing the decision making process.
25
References
Bentley, L. D., & Whitten, J. L. (2007). Systems Analysis and Design for the Global
Enterprise SEVENTH EDITION. New York: McGraw-Hill Companies, Inc.
Fleisher, C. S., & Blenkhorn, D. L. (2001). Managing frontiers in competitive intelligence.
Westport, Conn.: Quorum Books.
Luc, Q. (2013). Competitive Inteligence 2.0 Organization, Innovation and Territory. London:
Wiley.
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.
Turban, E., Sharda, R., Dalen, D., & King, D. (2010). Business Intelligence (2nd Edition).
Boston, MA: Prentice Hall.
26
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
27
Name
: Albertus Andika
Birthplace and date
: Jakarta, 18th of December 1991
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
2013
2012- 2013
2012
2010- 2011
: PT. SmartFren Telecom, TBK. Telemarketer
: Binus Career, Event IT Support
: Binus Career, Part Time Promotional Team
: Vice President on “How To Print Money at Home”
Seminar
: CV Embrio Property Agent, Executive Marketing
28
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
Email
: +62 8988 290 946
: [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
Working Experience:
2011
2013
2012 – Present
: ACE Kids English Course Intermediate Level
: Part Time Worker at PT KRAFT Indonesia
: English Tutor Bina Nusantara
: Manager of Supernova Gaming Center
29
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
2008 – 2011
: SMA Budi Mulia
2011 – Present
: Universitas Bina Nusantara, Jakarta
Non-Formal Education:
2011-2012
2006-2007
2000-2005
: Java Programming, LnT
: English Little Star
: ABC Patricia
30
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