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

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

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Application business intelligence in railways Application business intelligence in railways Document Transcript

  • MBA 5714 – Information Technology for Management Business Intelligence for Competitive Advantage 21 February 2011 Analysis by : RALPH YEW email: etyew@hotmail.comApplication of BI in Railways Market Page 1
  • 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 14Application of BI in Railways Market Page 2
  • 1.0 Executive SummaryToday many industry players ranging from banking, financial services, insurance,retail/distribution, IT, property developers, healthcare, telecommunications and othersare deploying business intelligence to grow the company’s financial results. The use ofsuch advanced business applications is one key enabler to grow their company whichgives them an edge of over the competition. The research paper will detail theimportance 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 whichhelp in anticipating and solving complex business problems throughout the organization.By embracing an analytical approach, these companies identify their most profitablecustomers, setting the right pricing, a faster product innovation, optimize supply chainsand identify the true drivers of financial performance.Futurists and trend spotters all predicted that the environment of tomorrow will mandatethe decimal-point precision in product quality, service and feature provision that onlyinformed, innovative and time-compressed application of analytics can provide. Byembedding Business Intelligence ( BI ) into the core culture of the organization will bethe 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 betterbusiness 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 researcherApplication of BI in Railways Market Page 3
  • Dresdner of Gartner Group term BI as "concepts and methods to improve businessdecision making by using fact-based support systems”.BI as a discipline is made up of several related activities, including data mining, onlineanalytical processing (OLAP), querying and reporting ( Mulchay, June 2009, CIO.com).Companies use BI to improve decision making, cut costs and identify new businessopportunities. BI is more than just corporate reporting and more than a set of tools tocoax data out of enterprise systems.3.0 The advantages of Business Intelligence can bring to your organizationFirstly, it improves the flow and flexibility of data. High-quality data must be integratedand accessible across your organization. It should also be structured in a flexible waythat allows your analysts to discover new insights and provide leaders the informationthey need to adjust strategies quickly. Strengthening and flexing the data backbone ofyour enterprise will pay off when you need to change business processes quickly inresponse to market shifts, regulatory or stakeholder demands.Secondly, it gets the right technology in place. The company approach to datamanagement and analytics will result in better decisions. Whereas, disconnected silosof 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.Application of BI in Railways Market Page 4
  • 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.Application of BI in Railways Market Page 5
  • 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 arket Page 6
  • 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) ctiveApplication of BI in Railways Market arket Page 7
  • 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 BIApplication of BI in Railways Market Page 8
  • 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 easilyApplication of BI in Railways Market Page 9
  • 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. Application of BI in Railways Market Page 10
  • 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 vendors 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 requirementsApplication of BI in Railways Market Page 11
  • 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.Application of BI in Railways Market Page 12
  • 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 PressApplication of BI in Railways Market Page 13
  • 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 1980s and early 1990s were forerunners of todays 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 organizations 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 AnalysisApplication of BI in Railways Market Page 14
  • 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.Application of BI in Railways Market Page 15