Download M0214-06PLM-P2-1501169991-01

M0214
ADVANCED TOPICS OF INFORMATION SYSTEMS
BUSINESS INTELLIGENCE IN BANKING
By
Sukianti 1501169991
Class : 06 PLM
Bina Nusantara University
Jakarta
2014
Abstract
The purpose of this research is to know about Business Intelligence (BI) and the
benefit using Business Intelligence. This research gives us information about BI
architecture, data warehouse and the benefit of BI in banking sector. This research also
leads us to know about BI applications and its particular users. The research methodology
used in this research is library and internet research. This research conducted by looking
for references from textbooks, journals, articles, and also the Internet. For the library
research we determining keywords related to our research topic. These keywords help us to
find any textbooks or scientific journals we need easier. The second step is to select the
information based on our research objectives. The Expected Outcome is to have a good
understanding about Business Intelligence, the BI architecture for implementation process
and also the benefit of BI in banking sector. The conclusion of this research is Business
Intelligence is able to improve the organizational business process, it help the company to
have an effective and efficient business process by providing a good management service
program, so it can increase customers satisfaction.
Keywords
Business Intelligence, Banking, BI Architecture, Business Process
CHAPTER 1
INTRODUCTION
1.1. Background
Currently the banking industry is facing the challenge of a very complicated
market such as requiring a highly secure transaction environment, global economic
conditions are uncertain, strict government regulation, and the demands of customer
who always expects high. Banks need to develop a strategy not only to maintain
existing customer, but also need to develop a strategy to get a new customer.
The demand includes also identifying and supporting profitable customer,
improving operations at the grassroots level and providing a fast response over the
performance of the portfolio.
With BI Maker decision then all levels ranging from the top-level management,
management middle, until Operational staff can take decisions quickly and
accurately, all stock holders will get thorough information in accordance with its
Business Role. The company will have a 'Single View of the Truth' to all the
information on all levels of the organization.
BI will provide a new perspective on the various levels of enterprise-level
performance to the level of individual staff, from the atomic transaction to the
transaction summary.
The objective is what all companies, including banking industry, would like to
achieve for improving their business performance.
According to Gibler (2013),
banks use Business Intelligence systems 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. All activities are
conducted for creating better productivity, customer satisfaction, transparency, and
efficiency (Yellowfin International Pty Ltd, 2013).
1.2. Scope
This paper aims to learn deeply about Business Intelligence (BI) in real
industries, especially banking sector. Firstly, we will identify what banking sector has
benefited from Business Intelligence (BI) application as BI has been used for
collecting needed information, supporting decision-making, and optimizing business
processes.
Secondly, we will learn about BI architecture alongside data warehouse. Thirdly,
we will learn about BI applications and its particular users. We will use an
application sample in order to have a good comprehension about BI practice in real
industries.
1.3. Objectives and Benefits
The objectives of this paper are:
a. To identify benefits of Business Intelligence (BI) in banking sector
b. To learn storage that is needed in BI implementation
c. To learn about BI architecture and data warehouse
d. To learn about BI applications and its particular users
The benefits of this paper are:
a. To understand benefits of Business Intelligence (BI) in banking industry
b. To understand storage that is needed in BI implementation
c. To understand BI architecture and data warehouse
d. To understand BI applications and its particular users through the screenshots
provided.
1.4.Research Methodology
The methodology used in this paper is library research. It is done by looking
for references from textbooks, journals, articles, and the Internet. The steps in doing
the library research are:
1. Determining keywords related to our research topic. The keywords help us easier
to find any textbooks or scientific journals we need.
2. Selecting the information based on our research objectives. The information
should also be analyzed since it is from various sources.
CHAPTER 2
THEORETICAL FRAMEWORK
2.1. Business
Business, also known as an enterprise or a firm, is an organization involved in the
trade of goods, services, or both to consumers. Businesses are prevalent in capitalist
economies, where most of them are privately owned and provide goods and services
to customers in exchange of other goods, services, or money. Businesses may also
be not-for-profit or state-owned. A business owned by multiple individuals may be
referred to as a company.
2.2. Intelligence
Intelligence has been defined in many different ways such as in terms of one's
capacity for logic, abstract thought, understanding, self-awareness, communication,
learning, emotional knowledge, memory, planning, creativity and problem solving.
2.3. Business intelligence (BI)
Business intelligence (BI) is a set of theories, methodologies, architectures, and
technologies that transform raw data into meaningful and useful information for
business purposes. BI can handle enormous amounts of unstructured data to help
identify, develop and otherwise create new opportunities. BI, in simple words,
makes interpreting voluminous data friendly. Making use of new opportunities and
implementing an effective strategy can provide a competitive market advantage and
long-term stability.
2.4. Bank
A bank is a financial intermediary that accepts deposits and channels those deposits
into lending activities, either directly by loaning or indirectly through capital
markets. A bank links together customers that have capital deficits and customers
with capital surpluses.
2.5. Business Intelligence portal
A Business Intelligence portal (BI portal) is the primary access interface for Data
Warehouse (DW) and Business Intelligence (BI) applications. The BI portal is the
users first impression of the DW/BI system. It is typically a browser application,
from which the user has access to all the individual services of the DW/BI system,
reports and other analytical functionality. The BI portal must be implemented in
such a way that it is easy for the users of the DW/BI application to call on the
functionality of the application. The BI portal's main functionality is to provide a
navigation system of the DW/BI application. This means that the portal has to be
implemented in a way that the user has access to all the functions of the DW/BI
application.
2.6. Data warehouse
Data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a database
used for reporting and data analysis. Integrating data from one or more disparate
sources creates a central repository of data, a data warehouse (DW). Data
warehouses store current and historical data and are used for creating trending
reports for senior management reporting such as annual and quarterly comparisons.
2.7. Data mining
Data mining (the analysis step of the "Knowledge Discovery in Databases" process,
or KDD), an interdisciplinary subfield of computer science, is the computational
process of discovering patterns in large data sets involving methods at the
intersection of artificial intelligence, machine learning, statistics, and database
systems. The overall goal of the data mining process is to extract information from a
data set and transform it into an understandable structure for further use. Aside from
the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, interestingness metrics, complexity
considerations, post-processing of discovered structures, visualization, and online
updating.
2.8. Business Intelligence Architecture
Business intelligence architecture is a framework for organizing the data,
information management and technology components that are used to build business
intelligence (BI) systems for reporting and data analytics. The underlying BI
architecture plays an important role in business intelligence projects because it
affects development and implementation decisions.
2.9. Data
Data are tokens that can be interpreted as some kind of value, usually either as a
quantitative measurement of, or a qualitative fact about something. Data are
manipulated either as values or variables by encoding them into information. Data
which are derived through reason or which are employed in the course of behaving,
are collectively called knowledge.
2.10. Business process
Business process or business method is a collection of related, structured activities
or tasks that produce a specific service or product (serve a particular goal) for a
particular customer or customers.
2.11. Implementation
Implementation is the realization of an application, or execution of a plan, idea,
model, design, specification, standard, algorithm, or policy.
2.12. System
A system is a set of interacting or interdependent components forming an integrated
whole or a set of elements (often called 'components' ) and relationships which are
different from relationships of the set or its elements to other elements or sets.
2.13. Computer hardware
Computer hardware is the collection of physical elements that constitutes a computer
system. Computer hardware refers to the physical parts or components of a computer
such as the monitor, mouse, keyboard, computer data storage, hard drive disk
(HDD), system unit (graphic cards, sound cards, memory, motherboard and chips),
etc. all of which are physical objects that can be touched. In contrast, software is
instructions that can be stored and run by hardware.
2.14. Computer software
Computer software, or simply software, also known as computer programs, is the
non-tangible component of computers. Computer software contrasts with computer
hardware, which is the physical component of computers. Computer hardware and
software require each other and neither can be realistically used without the other.
2.15. Analysis
Analysis is the process of breaking a complex topic or substance into smaller parts
to gain a better understanding of it. The technique has been applied in the study of
mathematics and logic since before Aristotle (384–322 B.C.), though analysis as a
formal concept is a relatively recent development.
2.16. Logistic
Logistics is the management of the flow of goods between the point of origin and
the point of consumption in order to meet some requirements, for example, of
customers or corporations. The resources managed in logistics can include physical
items, such as food, materials, animals, equipment and liquids, as well as abstract
items, such as time, information, particles, and energy. The logistics of physical
items usually involves the integration of information flow, material handling,
production, packaging, inventory, transportation, warehousing, and often security.
2.17. Extract, Transform, Load (ETL)
ETL systems are commonly used to integrate data from multiple applications,
typically developed and supported by different vendors or hosted on separate
computer hardware. The disparate systems containing the original data are
frequently managed and operated by different employees. For example a cost
accounting system may combine data from payroll, sales and purchasing.
2.18. Extract
The first part of an ETL process involves extracting the data from the source
systems. In many cases this is the most challenging aspect of ETL, since extracting
data correctly sets the stage for how subsequent processes go further.
Most data warehousing projects consolidate data from different source systems.
Each separate system may also use a different data organization and/or format.
Common data source formats are relational databases and flat files, but may include
non-relational database structures such as Information Management System (IMS)
or other data structures such as Virtual Storage Access Method (VSAM) or Indexed
Sequential Access Method (ISAM), or even fetching from outside sources such as
through web spidering or screen-scraping.
2.19. Transform
The transform stage applies a series of rules or functions to the extracted data from
the source to derive the data for loading into the end target. Some data not require
and transformation at all. In other cases, one or more of the following
transformation types may be required to meet the business and technical needs of
the server or data warehouse.
2.20. Load
The load phase loads the data into the end target, usually the data warehouse (DW).
Depending on the requirements of the organization, this process varies widely.
Some data warehouses may overwrite existing information with cumulative
information; frequently, updating extracted data is done on a daily, weekly, or
monthly basis. Other data warehouses (or even other parts of the same data
warehouse) may add new data in a historical form at regular intervals—for
example, hourly. To understand this, consider a data warehouse that is required to
maintain sales records of the last year. This data warehouse overwrites any data
older than a year with newer data. However, the entry of data for any one year
window is made in a historical manner.
2.21. Online analytical processing (OLAP)
In computing, online analytical processing, or OLAP is an approach to answering
multi-dimensional analytical (MDA) queries swiftly. OLAP is part of the broader
category of business intelligence, which also encompasses relational database,
report writing and data mining. Typical applications of OLAP include business
reporting for sales,
marketing, management reporting, business
process
management (BPM), budgeting and forecasting, financial reporting and similar
areas, with new applications coming up, such as agriculture. The term OLAP was
created as a slight modification of the traditional database term Online Transaction
Processing ("OLTP").
2.22. Online transaction processing (OLTP)
Online transaction processing, or OLTP, is a class of information systems that
facilitate and manage transaction-oriented applications, typically for data entry and
retrieval transaction processing. The term is somewhat ambiguous; some
understand a "transaction" in the context of computer or database transactions,
while others (such as the Transaction Processing Performance Council) define it in
terms of business or commercial transactions. OLTP has also been used to refer to
processing in which the system responds immediately to user requests.
2.23. OLTP system
OLTP system is a popular data processing system in today's enterprises. Some
examples of OLTP systems include order entry, retail sales, and financial
transaction systems. Online transaction processing system increasingly requires
support for transactions that span a network and may include more than one
company. For this reason, modern online transaction processing software use client
or server processing and brokering software that allows transactions to run on
different computer platforms in a network.
2.24. System integration
In engineering, system integration is defined as the process of bringing together the
component subsystems into one system and ensuring that the subsystems function
together as a system. In information technology, systems integration is the process
of linking together different computing systems and software applications physically
or functionally, to act as a coordinated whole.
The system integrator brings together discrete systems utilizing a variety of
techniques such as computer networking, enterprise application integration, business
process management or manual programming.
CHAPTER 3
DISCUSSION
3.1. The Benefits of BI in Banking Sector
 Balancing business risks and business development while managing the
Regulatory Compliance
 Empowering investments in existing resources and its infrastructure by collecting
data from various data sources available.
 Meet the needs of regulators in a way that is fast and accurate information.
 Report from BI to monitor the performance of Management in Branches
 Provides information about the Customer to a smart promotional activities
 Provides a high degree view of the customer profile.
 Facilitate the assessment of all aspects of the performance of an organization such
as income, profit, customer satisfaction, flexibility, migration and growth.
 Helps to perform Profit Analysis per branch, looking for the most customer
benefit, service the most profitable, or the location of the most profitable.
 Managing Credit Risk, create a balance sheet to report profit / loss, standardized
portfolio and credit analysis.
 Provides high degree views of the Financial and Operational results.
3.2. BI Implementation Problem
-
Poor data quality and BI technology
o The data of the organization is not clean and the time and effort to correct
this or handle this, destroys the success of the BI project.
For example, there are many different coding systems for the same objects
or entities in different systems e.g. customer is coded differently in the
Finance system to that held in the Sales system. Also there could be many
different definitions for the same item.
o The BI technology chosen turns out to be so rigid and painstaking to change
that the takes too long and costs too much to complete the project on time.
o The BI technology used deters use of the system because:

The quality of the presentation or visualization of the information is
poor or limited.

The response times (speed) to present the data is too slow and not
acceptable.

The flexibility to ask new questions of the BI technology is limited or
too difficult or time consuming to do for either the End Users or BI
expert.
-
Support from Organizations and People
o Management within your organization are not convinced that data driven or
evidence based decisions really works for them. They prefer to run the
operation from instinct.
o There is no clear overall business strategy laid out with objectives and
measures related to those objectives to assess business progress.
o IT personnel are overloaded and have no resource available to source the
data you need for your Business Intelligence (BI)system.
o There are no incentives for the staff within your organization to improve the
performance of the business either using BI or not.
o The business is in a state of stress or high change or flux. There is no
apparent or perceived time to establish a BI system.
o The eventual consumers of the BI system do not really know what they
want from a BI system until they see it. This means lots of changes are
required to the solution before it is accepted.
o IT experts building the system do not really understand the business, and so
many changes are needed to have the system accepted by the organization.
o The company does not have sufficient expertise or is not able to hire such
expertise to manage a project implementation on time and within budget or
to design the system adequately.
3.3. The Storage Needed for BI Implementation
o Storage and computing hardware. 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.
o Applications and data sources. To develop an effective BI solution, source data
will need to be scrubbed and organized. The challenge is that source data can
come from any number of applications, most using proprietary data formats and
application-specific data structures. Customer Relationship Management (CRM),
Supply Chain Management (SCM), and Enterprise Resource Planning (ERP)
systems, and other applications are the common sources of data. The trend over
the next five years will be for applications to standardize the data format using
eXtensible Markup Language (XML) schema and leverage BI specific standards
like XML for Analysis.
o Data integration. Middleware allows different systems supporting different
communication protocols, interfaces, object models, and data formats to
communicate. Firms will need to invest in these "connectors" to allow data from
source applications to be integrated with the BI repository. Extraction,
transformation and loading (ETL) tools pull data from multiple sources, and load
the data into a data warehouse. Again, the trend in data integration and
Enterprise Application Integration, in general, is toward standardization through
XML and web services.
o Relational databases and data warehouses. Firms will need a data warehouse to
store and organize tactical or historical information in a relational database.
Organizing data in this way allows the user to extract and assemble specific data
elements from a complete dataset to perform a variety of analyses.
o OLAP applications and analytic engines. Online analytic processing (OLAP)
applications provide a layer of separation between the storage repository and the
end user's analytic application of choice. Its role is to perform special analytical
functions that require high-performance processing power and more specialized
analytical skills.
o Analytic applications. Analytic applications are the programs used to run queries
against the data to perform either "slide-and-dice" analysis of historical data or
more predictive analyses, often referred to as "drill-down" analysis. For
example, a customer intelligence application might enable a historical analysis of
customer orders and payment history. Alternatively, users could drill down to
understand how changing a price might affect future sales in a specific region.
o Information presentation and delivery products. The results of a query can be
returned to the user in a variety of ways. Many tools provide presentation
through the analytic application itself and offer dashboard formats to aggregate
multiple queries. Also, enterprises can purchase packaged or custom reporting
products, such as Crystal Reports. An important trend in BI presentation is
leveraging XML to deliver analyses through a portal or any other Internetenabled interface, such as a personal digital assistant (PDA).
3.4. Business Intelligence Architecture
Business intelligence architectures come in all shapes and sizes. There is no
one way to build a data warehouse and BI environment. Many organizations try
various approaches until they find one that works, and then they evolve that
architecture to meet new business demands. Along the way, each BI team needs to
allocate responsibility for various parts of a BI architecture between corporate and
business unit teams. Finding the right place to draw the proverbial architectural line
is challenging.
1. The Business Intelligence Stack
Figure 1 shows a typical BI stack with master data flowing into a data
warehouse along with source data via an ETL tool. Data architects create business
rules that are manifested in a logical model for departmental marts and business
objects within a BI tool. BI developers who write code and assemblers who stitch
together predefined information objects also creates reports and dashboards for
business users. Typically, most databases and servers that power operational and
analytical systems run in a corporate data center. Using this high-level architectural
model, we can study the impact of the three organizational models described in Part
2 of this article on BI architectures.
Figure 1: Typical BI Stack
2. Conglomerate – Shared Data Center
In a conglomerate model, business units have almost complete autonomy to
design and manage their own operations. Consequently, business units also
typically own the entire BI stack, including the data sources, which are operational
systems unique to the business unit. Business units populate their own data
warehouses and marts using their own ETL tools and business rules. They purchase
their own BI tools, hire their own BI developers and develop their own reports. The
only thing that corporate manages is a data center that houses business unit
machines and delivers economies of scale in data processing (Figure2).
Figure 2: Conglomerate Business Intelligence
3. Cooperative Business intelligence – Virtual Enterprise
In a cooperative model where business units sell similar – but distinct –
products, business units must work synergistically to optimize sales across an
overlapping customer base. Here, there is a range of potential BI architectures
based on an organization’s starting point. In the virtual enterprise model, an
organization starts to move from a conglomerate business model to a more
centralized model to develop an integrated view of customers for cross-selling and
up-selling purposes, and to maintain a single face to customers who purchase
products from multiple business units. In a virtual enterprise, business units still
control their own operational systems, data warehouses, data marts, BI tools and
employ
their
own
BI
staff. Corporate
hasn’t
yet
delivered
enterprise ERP applications but is thinking about it. Its first step towards
centralization is to identify and match mutual customers shared by its business
units. To do this, the nascent corporate BI team develops a master data
management (MDM) system, which generates a standard record or ID for each
customer that business units can use in sales and service applications (Figure 3).
Figure 3: Cooperative Business Intelligence - Virtual Enterprise
The corporate group also creates a fledgling enterprise data warehouse to
deliver an enterprise view of customers, products and processes common across all
business units. This enterprise data warehouse is really a data mart of distributed
data warehouses. That is, it sources data from the business unit data warehouses,
not directly from operational systems. This can be a persistent data store populated
with an ETL or virtual views populated on the fly using data virtualization
software. A persistent store is ideal for non-volatile data (i.e., dimensions that don’t
change much) or when enterprise views require complex aggregations,
transformations, multi-table joins or large volumes of data. A virtual data store is
ideal for delivering enterprise views quickly at low cost, as well as for building
prototypes and short-lived applications.
4. Cooperative Business Intelligence – Shared BI Platform
The next step along the spectrum is a shared BI platform. Here, corporate
expands its appetite for data processing. It replaces business unit operational
applications with enterprise (ERP) applications (e.g., finance, human resources,
sales, service, marketing, manufacturing, etc.) to create a more uniform operating
environment. It also fleshes out its BI environment, creating a bonafide enterprise
data warehouse that pulls data directly from various source systems – including the
new ERP applications – instead of departmental data warehouses. This reduces
redundant extracts and ensures greater information consistency (Figure 4).
Figure 4: Cooperative Business Intelligence - Shared Platform
Meanwhile, business units still generate localized data and require custom
views of information. While they may still have budgets and licenses to run their
own BI environments, they increasingly recognize that they can save time and
money by leveraging the corporate BI platform. To meet them halfway, the
corporate BI team forms a BI Center of Excellence and teaches the business units
how to use the corporate ETL tool to create virtual data marts inside the enterprise
data warehouse. The business units can upload local data to these virtual marts,
giving them both enterprise and local views of data without having to design, build
and maintain their own data management systems. In addition, the corporate BI
team builds a universal semantic layer of shared data objects (i.e., a semantic layer)
that it makes available within the corporate BI tool. Although business units may
still use their own BI tools, they increasingly recognize the value of building new
reports with the corporate BI tools because they provide access to the standard
business objects and definitions that they are required or highly encouraged to use.
The business units still hire and manage their own BI developers and assemblers,
but they now have dotted-line responsibility to the corporate BI team and are part
of the BI Center of Excellence. This is the architectural approach used by Intuit
(see Part 1 of this series).
5. Centralized Business Intelligence – Shared Service
Some organizations, like Dell, don’t stop with a shared platform model;
they continue to centralize BI operations to improve information integrity and
consistency and squeeze all redundancies and costs out of the BI pipeline. Here, the
corporate BI team manages the entire BI stack and creates tailored reports for each
business unit based on requirements. For example, Dell’s EBI 2.0 program (see Part
2 of this series) reduced the number of report developers in half and reassigned
them to centralized reporting teams under the direction of a BI Competency Center
where they develop custom reports for specific business units. The challenge here,
as Harley-Davidson discovered, is to keep the corporate BI bureaucracy from
getting too large and lumbering and ensure it remains responsive to business unit
requirements. This is a tall task, especially in a fast-moving company whose
business model, products and customers change rapidly.
Federation is the most pervasive BI architectural model, largely because
most organizations cycle between centralized and decentralized organizational
models. A BI architecture, by default, needs to mirror organizational structures to
work effectively. Contrary to popular opinion, a BI architecture is a dynamic
environment, not a blueprint written in stone. BI managers must define an
architecture based on prevailing corporate strategies and then be ready to deviate
from the plan when the business changes due to an unanticipated circumstance such
as a merger, acquisition or new CEO. Federation also does the best job of balancing
the dual need for enterprise standards and local control. It provides enough uniform
data and systems to keep the BI environment from splintering into a thousand
pieces, preserving an enterprise view critical to top executives. But it also gives
business units enough autonomy to deploy applications they need without delay or
IT intervention. Along the way, it minimizes BI overhead and redundancy, saving
costs through economies of scale.
3.5 The usage Data warehouse in BI
Business Intelligence refers to a set of methods and techniques that are used by
organizations for tactical and strategic decision making. It leverages technologies
that focus on counts, statistics and business objectives to improve business
performance.
A Data Warehouse (DW) is simply a consolidation of data from a variety of
sources that is designed to support strategic and tactical decision making. Its main
purpose is to provide a coherent picture of the business at a point in time. Using
various Data Warehousing toolsets, users are able to run online queries and 'mine"
their data.
Many successful companies have been investing large sums of money in business
intelligence and data warehousing tools and technologies. They believe that up-todate, accurate and integrated information about their supply chain, products and
customers are critical for their very survival.
BI/DW (Business Intelligence/ Data Warehousing) process is broken into
following steps (Business Intelligence and Data Warehousing (BIDW), 2005):
-
Raw data is stored. Raw data is typically stored, retrieved, and updated by
an organization’s on-line transaction processing (OLTP) system. Additional
data that feeds into the data warehouse may include external and legacy data
that is useful to analyze the business.
-
Information is cleansed and optimized. The information is then cleansed
(for example, all duplicate items are removed) and optimized for decision
support applications (i.e. structured for queries and analysis vs. structured
for transactions). It is usually “read only” (meaning no updates allowed) and
stored on separate systems to lessen the impact on the operational systems.
-
Data mining, query and analytical tools generate intelligence. Various data
mining, query and analytical tools generate the intelligence that enables
companies to spot trends, enhance business relationships, and create new
opportunities.
-
Organizations use intelligence to make strategic business decisions. With
this intelligence, organizations can make effective decisions, and create
strategies and programs for competitive advantage.
-
The system is regulated by an overall corporate security policy. Information
in a data warehouse is typically confidential and critical to a company's
business operations. Consequently, access to all functions and contents of a
data warehouse environment must be secure from both external as well as
internal threats and should be regulated by an overall, corporate security
policy.
-
Business performance management applications track results. A well-run
BIDW operation also includes Business Performance Management (BPM)
applications, which help track the results of the decisions made and the
performance of the programs created.
3.6 BI Application
-
OLAP (Online Analytical Processing)
OLAP Tools are used to analyze multi-dimensional data. These powerful tools allow users
to identify observe trends and then to "drill-down" to discover the details behind those
trends.
-
Digital dashboards.
Digital dashboard is an “easy-to-read” single page, real-time user interface showing
graphical presentation of the current status or snapshots and historical trends
organizations key performance indicator to enable instantaneous and informed
decisions to be made at glance.
-
Data warehouse
Data warehouse (DW, DWH), or an enterprise data warehouse (EDW), is a
database used for reporting and data analysis. Integrating data from one or more
disparate sources creates a central repository of data, a data warehouse (DW). Data
warehouses store current and historical data and are used for creating trending
reports for senior management reporting such as annual and quarterly comparisons.
-
Data mining
Data Mining Tools are analytical engines that use data in a Data Warehouse to
discover underlying correlations. Data Mining Tools are used by analysts to gain
business intelligence by identifying and observing trends, problems and anomalies.
-
ETL
While the selection of a database and a hardware platform is a must, the selection of
an ETL tool is highly recommended, but it's not a must. When you evaluate ETL
tools, it pays to look for the following characteristics:
 Functional capability: This includes both the 'transformation' piece and the
'cleansing' piece. In general, the typical ETL tools are either geared towards
having strong transformation capabilities or having strong cleansing
capabilities, but they are seldom very strong in both. As a result, if you know
your data is going to be dirty coming in, make sure your ETL tool has strong
cleansing capabilities. If you know there are going to be a lot of different data
transformations, it then makes sense to pick a tool that is strong in
transformation.
 Ability to read directly from your data source: For each organization, there is a
different set of data sources. Make sure the ETL tool you select can connect
directly to your source data.
 Metadata support: The ETL tool plays a key role in your metadata because it
maps the source data to the destination, which is an important piece of the
metadata. In fact, some organizations have come to rely on the documentation
of their ETL tool as their metadata source. As a result, it is very important to
select an ETL tool that works with your overall metadata strategy.
Popular Tools
-
IBM WebSphere Information Integration (Ascential DataStage)
-
Ab Initio
-
Informatica
-
Talend
3.7. Business Intelligence User
- IT users
IT users are people who largely use BI tools for development purposes, using the
product suites for data modeling, data integration, report generation, presentation
and delivery.
-
Business users
Business users are usually consists of managers who review the analyses presented
by the power users, and may even do their own ad hoc queries, take the results of
those queries and may import those into desktop productivity tools in order to
create their own reports and presentations; business users are savvy about the data,
and may cross the line into becoming power users in their own right.
-
Extra-Enterprise users
Extra-enterprise users are the external parties, customers, regulators, external
business analysts, partners, suppliers, or anyone with a need for reported
information for tactical decision-making.
3.8 Example of Business Intelligence Screenshots in Banking and Explanation of How
to Read the Data
Sample of CIB-Credit information Bureau, a State Bank of Pakistan’s
department responsible for maintaining the information related to borrowing related
to any person, company, and/or group of companies.
CIB maintains this information by frequently fetching borrowing’s related
information from various banks and institutes throughout Pakistan.
This is a requirement imposed by SBP on all financial institution that they need
to get the credit worthiness report before granting a loan above a certain amount to
a customer. The current practice of obtaining a credit worthiness report is that the
Financial Institution submits a form in SBP. Here the report is prepared manually
by the SBP staff and handed over to the requesting institution on the following day.
The reason behind to maintain such an information is to track the net amount
hold by any borrower, to eliminate the manual work at SBP end, and to provide
quick and easy service to the Financial Institution. This application will allow the
user to access the Credit Information Bureau central repository in SBP.
Data Transformation
In order to extract data from Oracle 9i Server and then loads it into SQL
server, we have used MS-SQL Data Transformation Services.
Starting from OLTP Data bases, to Data extraction, transformation, loading,
generation of multidimensional data store, and finally a very user friendly User
Interface providing Drill-down and Roll-up facilities. OLBA allows user to
view aggregation of facts on various levels as designed in the Data warehouse
Cube.
OLBA – User Interface
OLBA – Graphical Analysis
OLBA – Numerical Analysis
CHAPTER 4
CONCLUSIONS AND SUGGESTIONS
4.1. Conclusions
Business Intelligence gives a lot of benefits for organization such as
Balancing business risks and business development while managing the
Regulatory Compliance, Empowering investments in existing resources and its
infrastructure by collecting data from various data sources available and also
meet the needs of
To implement the BI solution, there may be some challenge like poor data
quality and also lack of support from the stakeholder.
Business Intelligence Application is software that will help us to collect and
analyze the data for reporting process. BI application: OLAP, ETL, Data mining
and Digital dashboard.
All business people will deal with data in any kind of form and with computer
technologies such as BI to get their job done. Type of Business intelligence user: IT
Users, Business Users, and Extra Enterprise Users.
4.2. Suggestions

Before implementing BI make sure that :
o We already have a good quality data that will we use in the implementation
process
o Determine a ROI and benefits we will get, before the start.

Do not spend a lot of time to build a perfect reporting system, because the need will
change as the business changes. Serve the most important reports quickly, and make
changes as needed.

Focus on business goals.
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CURRICULUM VITAE