Download M0214-06PLM - Sukianti Binusian

M0214
ADVANCED TOPICS OF INFORMATION SYSTEMS
BUSINESS INTELLIGENCE IN BANKING
By
Yesika Kristina
1501146792
Dea Pradana Darmawan
1501155014
Sukianti
1501169991
Merianti
1501171320
Meshiya Caterlee
1501171485
Class / Group : 06 PLM / 06
Bina Nusantara University
Jakarta
2014
Abstract
The purpose of this research is to study how Business Intelligene (BI) plays its roles
in banking industry since banks possess immense data that must be handled properly for
supporting decision- making. The research methodology used in this research is library and
internet research. It is conducted by looking for references from textbooks, journals, articles,
and Internet. There are two steps in doing the library research. The first step is 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 information should also be analysed since it is taken from various
sources. The Expected Outcome is to have a good comprehension about Business
Intelligence, including its benefits, architecture and implementation. The conclusion of this
research is Business Intelligence is able to improve its streamline operational efficiencies,
bolster sales and marketing strategies, and develop customer service programs. It can also help
mitigate risks by developing more appropriate risk management process.
Keywords
Business Intelligence, BI Architecture, Banking, Benefit, Data
CHAPTER 1
INTRODUCTION
1.1. Background
In any kinds of industries, data are one of the valuable assets for companies. The data
may contain facts about their business, customers, business partners, and transactions
they have recorded since they started their businesses. As time goes by, the companies
surely grow in size, channels, and geographical footprint (Sahu, 2012). The growth has
also affected their transactions to be jumped multifold and thus resulted immense data.
One of industries that experience this matter is the banking industry. The banking
sector is becoming more competitive and facing ever-changing regulatory requirements,
making it more and more challenging for banks to keep up with the changes—and with
the competition. With a vast range of customers and customer needs, ongoing regulatory
changes,
and increasing consolidation,
banking enterprises
need
information
management solutions that will allow them to make smart decisions (Business
Intelligence and Banking , 2008).
As they store immense data, they certainly need a proper data management to result
information for making decisions and reports. Therefore, they start using Business
Intelligence systems. According to Negash (2004), BI systems combine data gathering,
data storage, and knowledge management with analytical tools to present complex
internal and competitive information to planners and decision makers. In other words,
business intelligence systems provide actionable information delivered at the right time,
at the right location, and in the right form to assist decision makers. The objective is to
improve the timeliness and quality of inputs to the decision process, hence facilitating
managerial work.
The objective which was mentioned earlier 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 Intelligence
Business Intelligence is a set of theories, architecture, technology, and
methodologies, that transform raw data into a meaningful and useful information for
business process in one organization. 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.
CIO.com wrote in their website that BI is a term that refers to a variety of software
application used to analyze an organization’s raw data. BI as a discipline is made up of
several related activities, including data mining, online analytical processing, querying
and reporting.
2.2 Data Warehouse
A data warehouse is a relational database that is designed for query and analysis
rather than for transaction processing. It usually contains historical data derived from
transaction data, but it can include data from other sources. It separates analysis
workload from transaction workload and enables an organization to consolidate data
from several sources.
Infotech.wsu.edu wrote that data warehouse is the concept of data extracted from
operational systems and made available as historical snapshots for ad-hoc queries and
scheduled reporting. Characteristics that distinguish data in the data warehouse from data
found in the operational environment are that it is: organized in such as way that relevant
data is clustered together for easy access, several copies of the data from various points
in time are kept together, and once the data is placed into the data warehouse it is not
updated. Rather, the historical snapshots stored in the Data Warehouse are periodically
refreshed with data from the operational databases.
2.3 Business Intelligence Architecture
TDWI wrote that Business Intelligence Architecture is a set of frameworks to
organize the data, management, and technical components used to build BI systems.
Architecture plays an important role in BI programs and projects, ensuring that the
development efforts of multiple projects fit neatly together as a cohesive whole.
Comprehensive architecture addresses data, technology, integration, business rules,
processes, projects, and more.
Search Business Analytics also wrote in their web that 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.
2.4 Online Campaign
Online campaign, also called as Online Advertising or Internet advertising, uses the
Internet to deliver promotional marketing messages to consumers. It includes email
marketing, search engine marketing, social media marketing, many types of display
advertising (including web banner advertising), and mobile advertising.
2.5 Pilot Project
A Pilot Project or Pilot Experiment, also called a Pilot Study, is a small scale
preliminary study conducted in order to evaluate feasibility, time, cost, adverse events,
and effect size (statistical variability) in an attempt to predict an appropriate sample size
and improve upon the study design prior to performance of a full-scale research project.
2.6 Data Source
A data source, also called a data file, is simply a collection of records that store
data. This data is used to populate merge fields in mail merges. These files can be
databases from Access, FileMaker Pro,etc. In theory, any Open Database Connectivity
(ODBC) database can be used as a data source. They can also be created in spreadsheets
like Excel or Quattro Pro. It can be a simple table in a word processing document.
2.7 Data Visualization
Data visualization or data visualization is a modern branch of descriptive statistics.
It involves the creation and study of the visual representation of data, meaning
"information that has been abstracted in some schematic form, including attributes or
variables for the units of information".
Search business analytics wrote that Data visualization is a general term that
describes any effort to help people understand the significance of data by placing it in a
visual context. Patterns, trends and correlations that might go undetected in text-based
data can be exposed and recognized easier with data visualization software.
2.8 OLAP application
OLAP (online analytical processing) is an approach to answering multidimensional 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.
Searchdatamanagement wrote that OLAP (online analytical processing) is
computer processing that enables a user to easily and selectively extract and
view data from different points of view. For example, a user can request that data be
analyzed to display a spreadsheet showing all of a company's beach ball products sold in
Florida in the month of July, compare revenue figures with those for the same products
in September, and then see a comparison of other product sales in Florida in the same
time period.
2.9 Storage Area Network (SAN)
A Storage Area Network (SAN) is a dedicated network that provides access to
consolidated, block level data storage. SANs are primarily used to enhance storage
devices, such as disk arrays, tape libraries, and optical jukeboxes, accessible to servers so
that the devices appear like locally attached devices to the operating system. A SAN
typically has its own network of storage devices that are generally not accessible through
the local area network (LAN) by other devices.
Webopedia wrote that Storage area network, or SAN, is a high-speed network of
storage devices that also connects those storage devices with servers. It provides blocklevel storage that can be accessed by the applications running on any networked servers.
SAN storage devices can include tape libraries, and, more commonly, disk-based
devices, like RAID hardware.
2.10 Network Attached Storage (NAS)
Network-attached storage (NAS) is file-level computer data storage connected to
a computer network providing data access to aheterogeneous group of clients. NAS not
only operates as a file server, but is specialized for this task either by its hardware,
software, or configuration of those elements.
Webopedia wrote that Network-Attached Storage (NAS) device is a server that is
dedicated to nothing more than file sharing. NAS does not provide any of the activities
that
a
server
in
a
server-centric
system
typically
provides,
such
as e-
mail, authentication or file management.
Techopedia wrote that Network Attached Storage (NAS) is a dedicated server,
also referred to as an appliance, used for file storage and sharing. NAS is a hard drive
attached to a network, used for storage and accessed through an assigned network
address. It acts as a server for file sharing but does not allow other services.
2.11 Hierarchical Storage Management (HSM)
Hierarchical Storage Management (HSM) is a data storage technique, which
automatically moves data between high-cost and low-cost storage media. HSM systems
exist because high-speed storage devices, such as hard disk drive arrays, are more
expensive (per byte stored) than slower devices, such as optical discs and magnetic tape
drives. HSM systems store the bulk of the enterprise's data on slower devices, and then
copy data to faster disk drives when needed.
Techopedia wrote that Hierarchical Storage Management (HSM) is a data storage
software tool that is used to transparently move data between various types of storage
media. The HSM technique is designed to automate the migration and retrieval of data
between expensive storage media such as hard disk drives and low-cost media such as
optical disks and magnetic tapes.
2.12 Capacity Planning
Capacity planning is the process of determining the production capacity needed
by an organization to meet changing demands for its products. In the context of capacity
planning, design capacity is the maximum amount of work that an organization is
capable of completing in a given period. Effective capacity is the maximum amount of
work that an organization is capable of completing in a given period due to constraints
such as quality problems, delays, material handling, etc.
Techopedia wrote that
capacity planning is a process through which the
procurement of IT resources, infrastructure and services are planned over a specific
period of time. It is an IT management practice to predict and forecast the future
requirements of an enterprise IT environment and its associated essential
entities/services/components.
2.13 ETL
Data Integration Info wrote that ETL comes from Data Warehousing and stands
for Extract-Transform-Load. ETL covers a process of how the data are loaded from the
source system to the data warehouse. Currently, the ETL encompasses a cleaning step
as a separate step. The sequence is then Extract-Clean-Transform-Load. Let us briefly
describe each step of the ETL process.
● Extract
The Extract step covers the data extraction from the source system and makes it
accessible for further processing. The main objective of the extract step is to retrieve
all the required data from the source system with as little resources as possible.
● Transform
The transform step applies a set of rules to transform the data from the source to the
target. The transformation step requires joining data from several sources,
generating aggregates, generating surrogate keys, sorting, deriving new calculated
values, and applying advanced validation rules.
● Load
During the load step, it is necessary to ensure that the load is performed correctly
and with as little resources as possible. The target of the Load process is often a
database. In order to make the load process efficient, it is helpful to disable any
constraints and indexes before the load and enable them back only after the load
completes.
2.14 Data Mining
Data mining is the practice of automatically searching large stores of data to
discover patterns and trends that go beyond simple analysis. Data mining uses
sophisticated mathematical algorithms to segment the data and evaluate the probability
of future events. Data mining is also known as Knowledge Discovery in Data (KDD).
The key properties of data mining are:

Automatic discovery of patterns

Prediction of likely outcomes

Creation of actionable information

Focus on large data sets and databases
2.15 Change Management
Change management is an approach to transitioning individuals, teams, and
organizations to a desired future state. In a project management context, change
management may refer to a project management process wherein changes to the scope
of a project are formally introduced and approved or the definition of change
management defined on this page.
CHAPTER 3
DISCUSSION
3.1. The Business Intelligence Benefits in Banking
A lot of banks want to use customer-level data, also for the product holdings, channel
activity and profitability to improve the audience targeting online. They also want to make
online campaigns and make account application and funding processes to be more
seamless and effective and efficient. To be able to do so, many banks have kept their eyes
to benefit themselves by implementing a BI solution.
By implementing BI solution to analyse its organisational data, banks can improve its
streamline operational efficiencies. Not only bolster sales and marketing strategies, also
develop customer service programs. It can also help mitigate risks by developing more
appropriate risk management process.
The actionable information generated by BI solution can mitigate risk in banking
sector, it is explained as following:
● It can quickly and efficiently detect and reduce incidents of fraudulent activity.
● It can calculate the probability of customers will default of loan and estimate the
cost of recovery.
● It can accurately estimate the risk of customers loan based on:
o The financial assets and earning capacity of borrower.
o The prevailing economic climate.
● It can analyse credit portfolios, enable banks to quickly identify potential
delinquency cases and act early as preventative measure.
● It can ensure compliance with statutory and regulatory requirements.
3.2. Business Intelligence Implementation Problem
We believe that there will be no two BI system implementation that are exactly the
same, as it is very depending on the individual circumstances and condition, along with
the implementer and the business enterprises. Though each implementation may have
their own method, challenges and solution, we can note down several generalised issues in
BI implementation.
The possible issues that can arise in implementing BI solutions are:
● Strategic alignment
It draws attention to the symbiotic relationship between BI solution and
institutional strategic planning. Also, the importance of them both, mutually
informing each other.
● Process improvement
It covers how BI solution can help an organisation uncover existing
inefficiencies in their processes. Also, help to have better connect management
with the process responsible for delivering data and reduce the need for periodic
large-scale readjustment process.
● System implementation
It is about the benefit and potential problems which are seen implicitly in
running a pilot project. Also, the difficulties of dealing with large volumes of data
and projects, seeking to utilise open data sources.
● Change management
It covers the importance of engaging both senior management and potential
users, and the dangers of not doing so. It also encourages to look at the importance
of looking at potential impact of your project from the broadest possible
perspective.
● Data usage
It is about the likely challenges of relying on data from both internal and
external sources. It also examines the potential benefits that can be earned in
implementing a data warehouse.
● Data definition and management
It covers the common issues faced that affect the data quality and the
importance of tackling them early during BI solution implementation.
● Data visualisation
This section sounds a cautionary note regarding the use of data visualisations,
stressing the need not to get carried away and to always bear in mind the needs of
the users and the facts that simple is often the best.
● Vendor issues
This section is about acknowledging that not all BI solution initiatives will
involve a vendor or supplier, but where they do stress some of the practical points
which are necessary to establish in the relationship to ensure a smooth and
productive outcome.
3.3. The Storage Needed for Business Intelligence Implementation
To implement the BI solution, enterprises may need to plan some new investments and
upgrade their hardwares technologies to build the BI technology stack. It is designed to
highlight the different layers of technology that will be affected by BI project. All the way
from the hardware hosting the data at the bottom of the stack to the portal the products
used to present information to users at the top of the stack. Starting from the bottom, the
seven layer includes:
● Storage and computing hardware.
● Application and data sources.
● Data integration.
● Relational database and data warehouse.
● OLAP application and analytic engines.
● Analytic applications.
● Information presentation and delivery products.
As for the storage needed for Business Intelligence Implementation, enterprises would
need to upgrade their data storage infrastructure. This will include the Storage Area
Network (SAN), Network Attached Storage (NAS), Hierarchical Storage Management
(HSM), and silo-style tape libraries.
To analyse this deeper, enterprises are doing BI Sizing or known as Capacity Planning.
It is useful to determine:
● Hardware specs for future Data Warehouse hardware genetics such as memory,
CPU cores and storage drive.
● ETL / ELT tier hardware specs, with the RAM, CPU cores and storage drive.
● BI reporting tier hardware specs, RAM, CPU cores and storage drive.
To make sure that these tiers are sufficient so the BI solution to work properly,
enterprises may need to take a note of these things beforehand:
● Initial ETL volumes or amount historic of data which will be stored, and planned
to be loaded on future warehouse.
● Incremental ETL volumes which will define daily ETL processing windows.
● Number of functional areas which are planned to be implemented.
● Number of source databases, used in ETL extracts.
● Data Warehouse (target) hardware specs and warehouse DB configuration.
● ETL tier hardware specs.
● LAN / WAN factor.
● BI reporting tier hardware specs.
● Any resource sharing on Source / ETL / Target tiers in the same LAN as your
source of DB.
● BI reporting tier hardware specs.
● Any resource sharing on Source / ETL / Target hardware.
● Number of named users working with BI reports.
● Number of concurrent end users.
● Number of power users.
● Caching effectiveness in ETL and BI reports.
● Human factor (BI admins, DBA, etc).
3.4. Business Intelligence Architecture
Business Intelligence Architecture is a framework of organising 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.
It is essential to have a solid BI architecture. If the underlying architecture is not
designed properly, inconsistencies arise among the different components may lead to
problems such as inability to share information among the components, inability to meet
business requirements, and poor business performance. In the worst case, a bad BI
architecture may lead to the scenario where wrong information is delivered to the wrong
person at the wrong time. Even in the case where BI systems are functional despite bad
architecture, organizations will not be able to maximize the value they should have gotten
from their BI investments (Ong, Siew, & Wong, 2011).
In this paper, the framework of BI architecture being discussed here contains five
layers that should be included when implementing BI systems. The five layers are data
source, ETL (Extract-Transform-Load), data warehouse, end user, and metadata layers.
It is important for organizations to clearly identify their data sources. Knowing where
the required data can be obtained is useful in addressing specific business questions and
requirements, thereby resulting in significant time savings and greater speed of
information delivery. Furthermore, the knowledge can also be used to facilitate data
replication, data cleansing, and data extraction. This is because even though there are
many existing data sources, some of them might be inaccessible, unreliable or irrelevant
to current business needs. With correct identification of data sources, problems such as
inconsistent information, difficulty in finding root causes, and issues of data isolation can
be avoided.
ETL (Extract-Transform-Load) focuses on three main processes: extraction,
transformation and loading. Extraction is the process of identifying and collecting relevant
data from different sources. Usually, the data collected from internal and external sources
are not integrated, incomplete, and may be duplicated. Therefore, the extraction process is
needed to select data that are significant in supporting organizational decision making.
Transformation is the process of converting data using a set of business rules (such as
aggregation functions) into consistent formats for reporting and analysis. Data
transformation process also includes defining business logic for data mapping and
standardizing data definitions in order to ensure consistency across an organization. As for
data cleansing, it refers to the process of identifying and correcting data errors based on
pre-specified rules. If there is an error found on the extracted data, then it is sent back to
the data source for correction. Once data have been transformed and cleansed, they are
stored in the staging area. This can prevent the need of transforming data again if the
loading processes fail or terminate. Loading is the last phase of the ETL process. The data
in staging area are loaded into target repository.
The data warehouse layer has three components:
a. An operational data store (ODS) is used to integrate all data from the ETL layer and
load them into data warehouses. It provides an integrated view of near real-time data
such as transactions and prices. In addition, the data stored in ODS is volatile, which
means it can be over-written or updated with new data that Blow into ODS.
b. Data warehouse has characteristics described as follows data warehouse is a central
storage that collects and stores data from internal and external sources for strategic
decision making, queries, and analysis.
c. Data mart is a subset of the data warehouse that is used to support analytical needs of a
particular business function or department. It contains historical data that can help
users to access and analyze different data trends.
There are many different types of metadata to support a BI architecture such as data
source, ETL, reporting, OLAP, and data mining metadata. Data source metadata consists
of information about access mode, structure of data sets (e.g., relational tables, views,
stored procedures), and referential integrity constraints. As data are integrated into the
data warehouse layer using ETL tools, an extraction log is maintained to record the
changes made to data element during the extraction process to ensure the quality of data.
This log is ETL metadata and it is stored in metadata repository. ETL metadata generally
contains information about sources, targets, transformation rules, and mapping. Metadata
repository is also used to document the information about data contained in the data
warehouse layer. It includes description of data structure (schema, dimensions, and
hierarchies) and definitions of conformed dimensions and conformed facts.
The end user layer consists of tools that display information in different formats to
different users. These tools can be grouped hierarchically in a pyramid shape (as shown in
Figure 3.1 below). As one moves from the bottom to the top of the pyramid, the degree of
comprehensiveness at which data are being processed and presented increases. This is to
tailor to increasing complexity in decision-making as one moves up organizational
hierarchy. For instance, the highest level of pyramid consists of analytical applications
which are usually used by top management while the lowest level consists of query and
reporting tools which are used mostly by operational management level.
Figure 3.1 Five-layered BI Architecture
3.5. The Usage of Data Warehouse in Business Intelligence
BI relies on data warehouse, making cost-effective storing and managing of warehouse
data critical to any BIDW solution. Without an effective data warehouse, organizations
cannot extract the data required for information analysis in time to facilitate expedient
decision-making.
BI/DW (Business Intelligence/ Data Warehousing) process is broken into following
steps (Business Intelligence and Data Warehousing (BIDW), 2005):
a.
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 isuseful to
analyze the business.
b.
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.
c.
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.
d.
Organizations use intelligence to make strategic business decisions. With this
intelligence, organizations can make effective decisions, and create strategies and
programs for competitive advantage.
e.
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.
f.
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. Business Intelligence Application
Business Intelligence Application is one type of application software which is designed
to retrieve, analyse, and report the data for Business Intelligence. It reads data that have
been previously stored, often in data warehouse.
The types of Business Intelligence Application are:
●
Spreadsheet.
It is an interactive computer application program for organisation and analysis of
data in tabular form.
●
Reporting and querying software.
Examples: Eclipse BIRT Project, GNU Enterprise, JasperReports.
●
OLAP (Online Analytical Processing).
It is an approach to answering multi-dimensional analytical queries swiftly.
●
Digital dashboards.
It is an “easy-to-read” single page, real-time user interface showing graphical
presentation of the current status or snapshots and historical trends organisations
key performance indicator to enable instantaneous and informed decisions to be
made at glance.
●
Data mining.
It is a computational process of discovering patterns in large data sets involving
methods at the intersection of artificial intelligence, machine learning, statistics
and database system.
●
Data warehousing.
It is a database used for reporting and data analysis.
●
Decision engineering.
It is a framework that unifies a number of best practice for organisational decision
making.
●
Process mining.
It is a process management technique that allows for the analysis of business
process based on event logs.
●
Business performance management.
It is a set of management and analytic process that enables the management of an
organisation’s performance to achieve one or more pre-selected goals.
●
Local information system.
It is a form of information system built with business intelligence tools, and it’s
designed primarily to support geographic reporting.
3.7. Business Intelligence Users
All business people will deal with data in any kind of form and with computer
technologies such as BI to get their job done. In BI, people who know how to do things
with the same tool you are using that you are not aware of will be find. This section will
discuss about Business Intelligence users. Technet classified BI users into five groups :
1. 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.
2. Power users
Power users are people who is a sophisticated “professional analysts” who are have
experience using complex tools, and are the individuals who often use BI tools to
manipulate data within analysis environments, but are less likely to be reviewing predefined reports.
3. 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.
4. Casual users
Casual users are the people who have are given the ability to review pre-design reports.
These decision-makers may take actions based on actionable knowledge presented to
them, and may want to customize the presentation and delivery of BI based on
adjustments to defined parameters.
5. 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
This paper uses Mysis Business Intelligence as the example Business Intelligence
application. Misys Business Intelligence (MBI) is a packaged solution powered by IBM
for monitoring bank performance in the areas of financial analysis, profitability analysis
and credit risk analysis. It transforms operational data into business intelligence,
providing relevant data to management, in a user-friendly format, to facilitate sound
decision making.
The data are read in statistical forms in order to get less time in analyzing and making
reports.
Below pictures are screenshots of Mysis Business Intelligence
Figure 3.2 Screeenshot of Mysis Business Intelligence Software
Figure 3.2 One of Mysis Business Intelligence Software’s Features
(Profitability Analysis)
Figure 3.2 One of Mysis Business Intelligence Software’s Features
(Credit Risk Analysis)
CHAPTER 4
CONCLUSIONS AND SUGGESTIONS
4.1. Conclusions
By implementing BI solution to analyse its organisational data, banks can improve its
streamline operational efficiencies. Not only bolster sales and marketing strategies, also
develop customer service programs. It can also help mitigate risks by developing more
appropriate risk management process.
Though each implementation may have their own method, challenges and solution, we
can note down several generalised issues in BI implementation. The possible issues that
can arise in implementing BI solutions are:
● Strategic alignment
● Process improvement
● System implementation
● Change management
● Data usage
● Data definition and management
● Data visualisation
● Vendor issues
To implement the BI solution, enterprises may need to plan some new investments and
upgrade their hardwares technologies to build the BI technology stack. It includes: storage
and computing hardware, application and data sources, data integration, relational
database and data warehouse, OLAP application and analytic engines, analytic
applications, information presentation and delivery products.
Business Intelligence Application is one type of application software which is designed
to retrieve, analyse, and report the data for Business Intelligence. It reads data that have
been previously stored, often in data warehouse.
All business people will deal with data in any kind of form and with computer
technologies such as BI to get their job done. Technet classified BI users into five groups:
IT Users, Power Users, Business Users, Casual Users, and Extra Enterprise Users.
4.2. Suggestions
After doing this research, we have come up with some suggestions about the ERP
system implementation. The suggestions are as follows:

Implementing Business Intelligence software in banking industry needs a solid
preparation and thus its objective can be aligned with banks’ business strategies,

Enterprises, including banks, should be pro-active and ready to face issues that might
occur during the business intelligence implementation

If banks choose to purchase BI software from vendors, they will have to make sure
that the chosen BI software meets their needs and is able to enhance their business
performance.
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