The application of Business intelligence in railways market in Malaysia and across Asia Pacific

1. MBA 5714 – Information Technology for Management
Business Intelligence for Competitive Advantage
21 February 2011
Analysis by : RALPH YEW email: etyew@hotmail.com
Application of BI in Railways Market Page 1

2. Table of Contents
1. Executive Summary 3
2. What is Business Intelligence 3
3. The advantages of Business Intelligence ( BI ) 4
4. Best practices case of applying BI in rail market 7
5. Challenges, drivers and restraints of Business Intelligence 8
6. The Growth of Business Intelligence in Malaysia 9
7. The Best Practices for Success in BI Integration 10
8. Strategic Analysis of BI Software Market in Malaysia 11
9. Conclusions 12
10. References & Bibliography 13
11. Glossary 14
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3. 1.0 Executive Summary
Today many industry players ranging from banking, financial services, insurance,
retail/distribution, IT, property developers, healthcare, telecommunications and others
are deploying business intelligence to grow the company’s financial results. The use of
such advanced business applications is one key enabler to grow their company which
gives them an edge of over the competition. The research paper will detail the
importance of using business intelligence for competitive advantage.
Companies of tomorrows are building a culture that is based on fact-based decisions.
The decisions are made through analysis using the business analytics systems which
help in anticipating and solving complex business problems throughout the organization.
By embracing an analytical approach, these companies identify their most profitable
customers, setting the right pricing, a faster product innovation, optimize supply chains
and identify the true drivers of financial performance.
Futurists and trend spotters all predicted that the environment of tomorrow will mandate
the decimal-point precision in product quality, service and feature provision that only
informed, innovative and time-compressed application of analytics can provide. By
embedding Business Intelligence ( BI ) into the core culture of the organization will be
the next biggest task of most modern companies today.
2.0 What is Business Intelligence (commonly term as BI)
Business intelligence or BI is a category of applications and technologies for gathering,
storing, analyzing, and providing access to data to help business users make better
business decisions. BI applications include decision support systems, query, reporting,
online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
The BI term was first used by Hans Peter Luhn of IBM in 1958. Then in 1989 researcher
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4. Dresdner of Gartner Group term BI as "concepts and methods to improve business
decision making by using fact-based support systems”.
BI as a discipline is made up of several related activities, including data mining, online
analytical processing (OLAP), querying and reporting ( Mulchay, June 2009, CIO.com).
Companies use BI to improve decision making, cut costs and identify new business
opportunities. BI is more than just corporate reporting and more than a set of tools to
coax data out of enterprise systems.
3.0 The advantages of Business Intelligence can bring to your organization
Firstly, it improves the flow and flexibility of data. High-quality data must be integrated
and accessible across your organization. It should also be structured in a flexible way
that allows your analysts to discover new insights and provide leaders the information
they need to adjust strategies quickly. Strengthening and flexing the data backbone of
your enterprise will pay off when you need to change business processes quickly in
response to market shifts, regulatory or stakeholder demands.
Secondly, it gets the right technology in place. The company approach to data
management and analytics will result in better decisions. Whereas, disconnected silos
of data and technology will be gradually reduced. BI technology portfolio may include:
• Data integration and data quality software.
• Optimized data stores to support core business processes.
• Analytical software with the means to effectively explore and share results.
• And integrated analytical applications.
Thirdly, in developing the talent of an organization; BI will help to develop analytic
thinkers who seek and explore the right data to make discoveries. And, to make
analytics work, analysts must also be able to communicate effectively with leaders
and link analytics to key decisions and the bottom line.
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5. Fourthly, demand fact-based decisions. An analytical company makes a wide range
of decisions. Some ad hoc; some are automated and some are transformative.
Managers should ask the right questions about the data to get maximum insight.
Hence, results are then deployed where it is important. Other operation systems
such as customer relationship management (CRM) applications can be generated
into interactive dashboards so to ensure decision makers have the information they
need when they need it.
Fifthly, BI can keep the business process become transparent. Transparency implies
openness communication and accountability; this is the key to successful business
analytics projects. The value delivered from an investment in business analytics
must be visible and measureable. Who the analysts are and what they are seeking
to accomplish should be clearly communicated to the business. Same goes to their
findings.
Finally, BI advantageous is that it fosters an analytical center of excellence as part of
the organization culture. In creating centralized team approach where the
organization promotes the use of data analytics and associated best practices. The
effective implementations address all elements of the organization’s analytic infra-
structure: people, process, technology and culture to support the business’ strategy
and operations. The CEO and leader must address the need to set a strong
analytics culture by always emphasizing that his/her communications to its internal
team members; as part of learning and growing.
4. Best practices case of applying Business Intelligence (BI) in rail sector
On selecting the right BI technologies, the need to consider “risk-to-value”; like can
the technology live up to its promise in helping to reduce costs while same time
increasing company’s revenue. It should allow some experimentation as part of
learning, and employees should be given permission to learn from trying new things.
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6. And as always to keep one ahead, the organization should revise its strategies to
vise
fight the competition. Thus the development of new capabilities and skills is
he Thus,
essential. Below is a summary of BI roadmap model
Summary of the BI model (source : Moss and Shaku Atre, BI Roadmap)
The Result of introducing BI is achieving the Competitive Intelligence (source :
Moss and Shaku Atre, BI Roadmap)
Application of BI in Railways Market
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7. Image above showing Active Dashboard showing the company sales performance ( source : Biz cubic )
Image above showing Active Dashboard for Sales for different product ( source : Dotnet charting)
ctive
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8. Strategic Dashboard for Employees ( source : InformationBuilders)
5. Challenges, drivers and restraints of Business Intelligence
The challenges for many companies on their deployment of BI is to successfully
build a culture that use and apply analytics and data mining as a key competitive
advantage in running their business. Other reason will be the integration all their
other business processes and application to work coherently with the BI.
The drivers for adoption of BI among organization is primarily to meet the
corporate governance and regulatory norms, the need to have quality data, the
increase in IT expenditure with fast evolving technology and lastly the
government initiatives. Today the business landscapes are forcing the
organization to act fast with accurate and timely info. The exploding size of
database had made BI the obvious choice to mine the data for quality info
leading to making more sales to the targeted customers for the specific products.
The restraints factor for not adopting BI by organization are due to several factors
like high cost for the BI software and maintenance cost, non-standardized BI
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9. platform, concern over data security, and end-user dissatisfaction especially
among business user in using the complex data mining tools. Another issue
brought up is the scarcity of in-house skilled resources to implement the BI
project successfully. Many organizations like banks still have their legacy system
and they may not choose to migrate easily to new BI, considering the lack of
technical expertise and the poor data quality that need to be clean prior to
migrating to new BI platform.
6. The Growth of Business Intelligence in Malaysia
With recent government initiative for the corporate sector to improve the
corporate governance and adhered to regulatory there is now more need to
produce comprehensive data and report. A few vertical markets are main
early adopters of BI in Malaysia they are telecommunication, banks,
government and manufacturing.
The case of Telekom Malaysia being the largest telecommunication company
in Malaysia implemented BI for competitive edge resulting in productivity,
better revenue, efficiency and better decision making. Telekom Malaysia
opted for special customized BI solutions like network specialization,
customer churn control, up sell and cross sell product and fraud detection.
Telekom Malaysia is able to increase the flow of information between its
business units with the help of BI. Other case include Bank Rakyat deploying
BI software to analyze customer profitability and product revenue analysis.
Thus, the bank has more informed decision when it is making a finance
product launch and identifying newer customer segment, thereby an increase
in its revenue. Finally the case by Department of Statistics of Malaysia (DOS)
implementing BI which brought down processing time on request for its
information. Highly complex activities in related to data analysis can be easily
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10. and generating report became easy. It allow for more productivity and less
manual work.
For Malaysia the international BI vendors partner with local system integrators
and value added resellers to implement their BI solutions. Such a trend due to
the local system integrators understanding the Malaysian clients needs and
requirement better. The BI vendors are listed on the table below:
BI Software Tools Market and Vendors Product Types
BI Vendors Data Report & Query Analytics High Level
Integration Analytics
SAS
Business Objects
Microsoft
Cognos
Hyperion
SAP
IBM
Oracle
Source: Frost & Sullivan Business Intelligence Report 2007
7. Best Practices for Success in BI Integration
Firstly, BI applications require a clear and intimate understanding of the business
itself and it is only by working on business and IT issues in tandem that the real
value of BI is realized. '
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11. Secondly, 'Enterprises should use the pressure of compliance to achieve greater
things, such as cleaning up the many data silos, creating more ownership around
performance data and eliminating many of the thousands of spreadsheets'.
Thirdly, 'Data quality issues need to be addressed on an ongoing basis and
enterprises need to accept that these are not just IT issues.'
Fourthly, 'Always compare your enterprise application vendor's solution with that
of a market leading specialty vendor. ' and Building in the same limitations in to a
new system is one of the greatest inhibitors to success. BI needs to evolve but BI
projects should not - they should start and stop and not evolve.'
Fifthly, 'Enterprises must define their BI key competencies and capabilities in
order to determine what to in- or outsource. As ever, the golden rule of
outsourcing applies; avoid the temptation to outsource everything and only
outsource things that are not a core competency.'
Finally, the 'Companies must have a solid and stable BI infrastructure in place
first. They should then create a networked approach where these new
technologies are able to communicate with other BI technologies inside and
outside the organization, as well as with other technologies such as business
process management and application integration.'
8. Strategic Analysis of BI Software Market in Malaysia
Although there could be many factors that could affect the implementation
process of a BI system, researchers showed that the following are the critical
success factors for business intelligence implementation:
i. Business-driven methodology & project management
ii. Clear vision & planning
iii. Committed management support & sponsorship
iv. Data management & quality
v. Mapping solutions to user requirements
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12. vi. Performance considerations of the BI system
vii. Robust & expandable framework
8. Conclusions
Like any new and challenging initiative BI needs a successful buy-in considering
it involve the change of entire company work culture. The organization which
committed to deploying their best human resource – their people, technologies
and business processes in new ways that shift them to the next level of playing
fields will survive and thrive in their business when applying BI.
For BI project to be a success; it need to ensure that the organization have
senior level business sponsorship for BI project. Organization must achieve a
unified BI infrastructure by leveraging ERP investments by implementing a
strategy to utilize Business Intelligence (BI) to improve performance.
Finally, organization must be able to leverage existent knowledge management
and continue to evolve the BI initiatives.
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13. 9.0 References & Bibliography
1. Efraim Turban and Linda Volonio (2010): Information Technology for
Management, John Wiley & Sons (Asia) Pte Ltd, Danvers
2. Communications News(Jan2009), Vol. 46 Issue 1, p24-26, 3p
3. Mulchay (2009) , What is Business Intelligence, CIO.com
4. Barrington( 2009) , Customer Relationship Management, CRM Today.
5. Moss and Atre ( 2003), Business Intelligence Roadmap, Addison-Wesley
Longman
6. Thierauf ( 2001 ), Effective Business Intelligence Systems, Quorum Book
7. Fleisher ( 2005), Competitive Intelligence and Global Business, Praeger
8. Zimmerman (2005), Business Intelligence, Search Business Analytics
9. Voloudakis (2005), Successfully Navigating BI Pitfalls, Educase Annual
Conference, Bearing Point
10. Vitt, Lukevich, Misner ( 2008 ), Business Intelligence, Microsoft Press
11. Vercellis ( 2009 ), Business Intelligence, John Wiley & Sons, pg 1-19
12. Loshin( 2003), Business Intelligence For Savvy Manager Guide, Kaufmann
13. Egger, Fiechter, Kramer (2004), SAP Business Intelligence, Galileo Press
14. Biera ( 2003), Business Intelligence for Enterprise, IBM Press
15. Hancock, Toren (2005), Practical Business Intelligence with SQL Server,
Microsoft Press
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14. Glossary (source: Sdgcomputing.com)
Data Cleansing: Removing errors and inconsistencies from data being imported into a data
warehouse.
Data Migration: The movement of data from one environment to another.This happens
when data is brought from a legacy system into a data warehouse.
Data Mining: The process of finding hidden patterns and relationships in the data.
Analyzing data involves the recognition of significant patterns. Human analysts can see
patterns in small data sets. Specialized data mining tools are able to find patterns in large
amounts of data. These tools are also able to analyze significant relationships that exist only
when several dimensions are viewed at the same time.
Data-Based Knowledge: Knowledge derived from data through the use of Business
Intelligence Tools and the process of Data Warehousing.
Data Mining: The process of finding hidden patterns and relationships in the data.
Analyzing data involves the recognition of significant patterns. Human analysts can see
patterns in small data sets. Specialized data mining tools are able to find patterns in large
amounts of data. These tools are also able to analyze significant relationships that exist only
when several dimensions are viewed at the same time.
Data Quality Assurance: Data Cleansing and Data Scrubbing. The process of checking the
quality of the data being imported into the data warehouse.
Decision Support System (DSS) : A computer system designed to assist an organization
in making decisions.The Decision Support Systems and Enterprise Information Systems of
the 1980's and early 1990's were forerunners of today's Business Intelligence Tools.
Database Management System (DBMS) : The software that is used to store, access, and
manage data.There are two main types of Database Management Systems used for
business intelligence and data warehousing - specialized Multidimensional Database
Management Systems (MDBMS) and the more widely used general purpose Relational
Database Management Systems (RDBMS)
ETL (Extract, Transform, and Load) : ETL refers to the process of getting data out of one
data store (Extract), modifiying it (Transform), and inserting it into a different data store
(Load).
OLAP (On-Line Analytical Processing) : The use of computers to analyze an
organization's data."OLAP" is the most widely used term for multidimensional analysis
software. The term "On-Line Analytical Processing" was developed to distinguish data
warehousing activities from "On-Line Transaction Processing" - the use of computers to run
the on-going operation of a business. In its broadest usage the term "OLAP" is used as a
synonym of "data warehousing". In a more narrow usage, the term OLAP is used to refer to
the tools used for Multidimensional Analysis
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15. OLTP (OnLine Transaction Processing) : The use of computers to run the on-going
operation of a business.
Relational Database Management System (RDBMS) : A Database Management System
based on relational theory.Most modern Database Management Systems (Oracle, Sybase,
Microsoft SQL Server) are relational databases. These databases support a standard
language - SQL (Structured Query Language).
SQL (Structured Query Language): The standard language for accessing relational
databases.
XML (eXtensible Markup Language): A method of sharing data between disparate data
systems, without needing a direct connection between them.
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