Business Intelligence Systems: Design and Implementation Strategies

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1 139 Business Intelligence Systems: Design and Implementation Strategies G R Gangadharan IMIT Class of 2004, Scuola Superiore Sant'Anna, Pisa, Italy gangadharan_gr@ yahoo.com Sundaravalli N Swami Assistant Professor, Ramarao Adik Institute of Technology, Navi Mumbai, India sundar1469@ yahoo. com Abstract Managing an organization requires access to information in order to monitor activities and assess performance. Trying to understand what informatian an organization has can be challenging because the information systems collect and process vast amount of data in various forms. To flow in the running stream of rapidly changing, increasingly competitive global market scenario and increasingly volatile consumer and market behavior and rapidly shortening product life cycles, business enterprises today are necessary to analyze accurate and timely information about financial operations, customers, and products using familiar business terms, in order to gain analytical insight into business problems and opportunities. Enterprises are building business intelligence systems that support business analysis and decision making to help them better understand their operations and compete in the marketplace. This paper describes the life cycle comprising various phases in the development of a BI system. The paper elaborates the implementation issues of BI in an organization focusing a case study. Keywords Business Intelligence, Decision Support Systems 1. Introduction "Make no little plans. They have no magic to stir men's blood and probably themselves will not be realized. Make big plans; aim high in hope and work, remembering that a noble, logical diagram once recorded will never die, but long after we are gone will be a living thing, asserting itself with evergrowing insistency. Remember that our sons and grandsons are going to do things that would stagger us. Let your watchword be order and your beacon beauty. Think big." Student paper - Daniel Burnham, a prominent Chicago architect and civic planner. New and complex changes are emerging that will force enterprises to operate in entirely new ways. The interconnected linkage of supply chains, markets and businesses represents a new challenge for all enterprises. The key strategy for creating competitive advantage lies in understanding the data that will shape the networked marketplace. Finding ways of bringing together and making sense of the vast amounts of data flowing within and across the extended enterprise is becoming a key business success factor. The path to business insight [Ill follows the process of integration of data from disparate internal and external data sources, applying analysis tools and techniques to understand the information within the data, making decisions, and taking actions based on this gained insight. According to [61, businesses can achieve a true up-to-the-moment view in which: The information gleaned is actually current enough to be useful in managing and executing business processes, Efficiency is optimized by choosing among the hest options available given the circumstances at the time, and The organization is able to respond to its best customers. In the current emerging highly dynamic business environment, only the most competitive enterprises will achieve sustained market success [2]. In order to 2dh Int. Conf. Information Technology Interfaces IT1 2004, June 7-10, 2004, Cavtat, Croatia

2 140 capitalize on the business opportunities, these organizations will distinguish themselves by the capability to leverage information about their market place, customers, and operations. A central part of this strategy for long-term sustainable success is business intelligence. According to [12], BI is a term that encompasses a broad range of analytical software and solutions for gathering, consolidating, analyzing and providing access to information in a way that is supposed to let an enterprise's users make better business decisions. The term BI encompasses software for extraction, transformation and loading (ETL) [4], data warehousing, database query and reporting, multidimensional / on-line analytical processing (OLAP) [I] data analysis, data mining and visualization. The key, of course, is consolidating data from the many different enterprise operational systems into an enterprise data warehouse. Due to the vast scope of this effort, few organizations have a truly enterprise data warehouse. BI describes the result of in-depth analysis of detailed business data, including database and application technologies, as well as analysis practices. BI is technically much broader, potentially encompassing knowledge management, enterprise resource planning, decision support systems and data mining. According to [7], BI has different definitions from different fields of experts. To some CRM experts, BI is all about seamless integration of operational front-office applications with operational back-office applications. To some data warehouse experts, BI is just a new term for data warehousing; that is, providing decision support applications on a new technology platform. To some data mining statisticians, BI represents the advanced data mining algorithms, such as neural induction techniques. BI is an enterprise architecture for an integrated collection of operational as well as decision support applications and databases, which provides the business community easy access to their business data and allows them to make accurate business decisions. It is a new "discipline," in which data is finally treated as the corporate resource, that it is. Any operational system (including ERP and CRh4) and any decision support application (including data warehouses and data marts) are BI, if and only if they were developed under the umbrella and methodology of a strategic cross-organizational initiative. According to [31, BI technology has coalesced in the last decade around the use of data warehousing and OLAP. The various sources for the relevant business data are referred to as the operational data stores (ODS). The data are extracted, transformed, and loaded (ETL) from the ODS systems into a data mart. An important part of this process is data cleansing, in which variations on schemas and data values from disparate ODS systems are resolved. In the data mart, the data are modeled as an OLAP cube (multidimensional model), which supports flexible drilldown and rollup analyses. Tools from various vendors (e.g.. Hyperion, Brio, Cognos) provide the end user with a query and analysis front end to the data mart. Large data warehouses currently hold tens of terabytes of data, whereas smaller, problem-specific data marts are typically in the 10 to 100 gigabytes range. BI refers to the use of technology to collect and effectively use information to improve business potency. An ideal BI system gives an organization's employees, partners, and suppliers easy access to the information they need to effectively do their jobs, and the ability to analyze and easily share this information with others. BI provides critical insight that helps organizations make informed decisions. BI facilitates scrutinizing every aspect of business operations to find new revenue or squeeze out additional cost savings by supplying decision support information. 2. BI Methodology BI is a strategic initiative by which organizations measure and drive the effectiveness of their competitive strategy. BI projects go through the following phases as depicted in Fig. 1 : 2.1. Analysis Every BI project should clearly justify the cost and the benefits of solving a business problem. Requirement analysis is performed including a predefined set of the key performance indicators (KPIs) that are required by the end users. The analysis phase produces a high level design of the various components of the solution with the sources

3 141 of relevant information. Because of dynamic nature of BI projects, modifications in objective, people, estimate, technology, users and sponsors can severely impact the success of the project Designing Based on the complexity of the solution and the requirements, appropriate BI technologies are selected. Analysis for the functional deliverables is best done through prototyping. This gives them an opportunity to adjust their delivery requirements and their expectations. database design schema must match the access requirements of the business. Depending on the data cleansing and data transformation requirements developed during analysis, an ETL tool may or may not be the best solution. In either case, preprocessing the data and writing extensions to the tool capabilities are frequently required. The real payback for BI applications comes from the business intelligence hidden in the organization's data, which can only be discovered with data mining tools. Developing Meta Data Repository becomes a subproject of the overall BI project Deployment Once all components of the BI application are thoroughlv tested, the aoulication is deployed -. to the I I _. user ends. The success of BI project primarily lies on the quality of end user training and support. This phase requires an interactive approach, with extensive user training and adjustments to meet the user needs. This phase includes the development of predefined reports and analyses for the business users, and laying the groundwork for more advanced analytics in the future Evolution Figure 1. Life Cycle of SI System Measuring the success of application, extending the application across the enterprise and increasing cross-functional information sharing are the goals of evolution Development The life cycle of BI system repeats with the methodology operating at a new level of focus The full process of flow of information across the consisting analysis, re-evaluation, modification, organization should be modeled. optimization and tuning. The requirements for what type of meta data to capture and store must be documented in a meta model. In addition, the requirements for delivering meta data to the users have to be analyzed. If a meta data repository is purchased, it will most likely have to be extended with features that are required by BI applications. If a meta data repository is built, the database has to be designed based on the meta model developed during the previous step. The 3. BI Framework Business intelligence is a boon to enterprises because they pull together vast quantities of realtime information from disparate systems and distill them into focused views of the business. Business Intelligence needs are not only restricted to multinational corporations with huge investments and human resources. Small and medium enterprises (SMEs) have intelligence needs and should consider

4 142 seeking out relevant information. In all business situations, obtaining intelligence is critical. Gartner Research estimates that from 2002 to 2006, the percentage of BI deployments that provide instantaneous data currency will grow from 11 percent to 29 percent. The metrics for determining the necessity for implementing business intelligence within the organization are as follows: Generation of huge amount of data in contrast to small amount of information Finding history of business records Busiest IT section with no time for report generation. Enhancing business processes to become more profitable Unable to organize data in the way by which it should be organized Faster decisions making based on factual information Organizational structure wise report generation Measuring time spent in extracting and analyzing data The aspects that the organizations need to consider for implementing business intelligence solution in a way tailored to the particular requirements are as follows: What are the goals for using information and how are they prioritized? Who are the users of information in the organization and how do the information requirements change among user groups? Does the organization culture allow information to be used as a strategic asset? How does the organization share information with partners and customers? What are the corporate goals for implementing BI strategy? How are decisions made in the organization? Does BI support and facilitate collaboration around data? How do the competitors use BI for information sharing with customers and partners? How will BI deployment add value to existing applications? What are the best practices for deploying BI? Enterprises wishing to implement intelligence face the following challenges: business Providing access to extensive resources from devices with limited capacity. Benchmarks and performance targets Creating a new information infrastructure to support the development and deployment of multiple applications. * Integrating to existing enterprise I legacy systems and connecting with multiple networks. Creatiing solutions that perform in and out of both network coverage and managing the solution. a Enforcing security and role-defined access to the data warehouse. Based on [9], the completeness and adequacy of BI infrastructure is evaluated by the following guidelines: Effective data integration process to create required business intelligence on a daily basis. 0 Continuous monitoring processes to allow alerts - to be communicated immediately. Automated information delivery process. Fully automated warehouse administration infrastructure. Availability of information on standardized dimension such as customer, product and geography. Delivery of answers to all key business questions. Integrated enterprise portal infrastructure [SI to deliver business intelligence. Higher end user acceptance having a consistent look and feel across different applications and clear help desk and training policies. By organizing and deploying BI in a manner appropriate to the organization s own characteristics, the complete value of the data stored throughout the enterprise can be unleashed. 4. CaseStudy Following is a case study of implementation of BI in an electrical and electronics components manufacturing company. The company operates nine production plants that provide products to

5 retailers across India. The company uses multiple sales channels, including contract manufacturing, and direct to store distribution. The sales and distribution network of the company complicated the ability to forecast sales, production and distribution impacts. Poor service and high inventory levels can lead to significant losses in customer loyalty in distribution. To meet its customers requirements, the company needed the flexibility to analyse business results daily in an efficient and userfriendly manner. The reporting systems of the company delivered some data to clients, which were hard to use, inflexible, and often outdated. Also, administering and maintaining these systems required programming expertise. The first phase of implementation included exclusive and extensive system analysis followed by prototypes development. The second phase of implementation involved actual deployment of BI with legacy systems. The project intends to create a central source of information that delivers strategic business knowledge worldwide in a consistent, timely manner by the creation of a comprehensive data warehouse focusing on order processing, inventory analysis, purchasing, sales and service. The project begins with the analysis and determination of information requirements, and evaluation of key performance indicators that define overall business drivers. The phase is extended to examine supporting business processes to determine the foundation of overall architecture and develop a logical data model. Based on this information, a three-tier architecture model facilitating delivery of source data, supporting data warehouse and data marts, and accommodating presentation tools to simplify user access is formulated. To evaluate information across the key business areas with the flexibility to perform ad-hoc query analysis of summary and detailed data, the tools allowing dynamic, user-friendly, graphical, and drill-down-enabled analysis are selected. The system is integrated with web allowing worldwide access. In order to meet the company s specific requirements, BI system is implemented using the following softwares: RODIN (Coglin Mill), a highly advanced data warehouse management system, delivers a full range of essential features to extract, transform, and load data into the data warehouse. It provides a consistent, clean, single source of information facilitating delivery to all user-access and presentation tools. DataTracker (Silvon Software) supports subjectarea data marts-providing various dynamic query and drill-down analysis capabilities through ad-hoc and predefined templates. Users can access summary information across all data hierarchy levels. Crystal Reports (Seagate) offers more detailed analysis and reporting capabilities from the data warehouse when users want to investigate information further. MetaFrame (Citrix) software enables information from subject-area data marts to be delivered over an IntraneUIntemet connection for web browser integration. The result of BI implementation enabled decisionmakers to study ways of optimising the business and to respond quickly and more effectively to issues as they arise. This BI implementation provided the opportunity to keep staff on the road aware of the latest developments, to alert staff the moment a critical value changes, to enable staff to respond to the alert with the ability to look up related information to make a decision and to empower staff to act on their decisions by interacting with the application. Users have access to information that previously was unavailable including data on profit and cost drivers that directly impacts the business. Writing and maintaining complex reporting processes that deliver inconsistent and inaccurate results are not required further. Information that used to take hours or days to report is available instantaneously. Handling the following business operations efficiently by the implemented BI system boosted revenue by 36%:

6 144 Integrating sales, inventory and financial systems Estimating and forecasting sales and production Trend analysis - planning and determining strategies Order tracking Profitability analysis Monitoring and compliance to standards and rules Exception / ad hoc reporting Thus, Business intelligence acts as a source of competitive advantage turning operational data into a business asset that drives strategic decisions and improves performance for the company and its clients. All Product names are registered trademarks of their respective companies. 5. Conclusion Many industries are using BI applications to reach beyond the enterprise and share insights off the platform with vendors and customers IS]. Understanding what BI is, why one would apply it and the corresponding benefits are important in implementing BI across the enterprise. Implementing BI with in the enterprise is not the destination, but a joumey towards an ideal enterprise. 6. References [I] Alex Berson and Stephen Smith, Data Warehousing, data Mining, & OLAP, McGraw Hill Intemational Edition, 2001, [2] Alex Berson, Stephen Smith, and Kurt Thearling, Building Data Mining Applications for CRM, Tata Mc-Graw Hill, [3] W. F. Cody, J. T. Kreulen, V. Krishna, and W. S. Spangler, The Integration of Business Intelligence and Knowledge Management, IBM Systems Journal, Vol. 41, No. 4,2002. [4] Curt Hall, Data Warehousing for Business Intelligence, March 1999, [5] Erik Johnson, Meeting Industry Specific Challenges With Business Intelligence Solutions, DM Review, Jan [6] John Bates, Business In Real Time - Realizing the Vision, DM Review, May & EdID=6632&Topic=64 [7] Larissa T. Moss and Shaku Atre, Business Intelligence Roadmap: The Complete Project Lifecycle for Decision Support Applications, Addison Wesley Longman, [XI Marco Tilli, Next Generation Business Intelligence Portals, DM Direct, November [9] Mark Robinson, Business Intelligence Infrastructure, BI Report, May [IO] Richard Skriletz, Strategic Insight: Today s Business Intelligence Landscape, DM Review, Jun [ 1 I] Shari Rogalski and Dan Fisher, Business Intelligence: 360 Insight: Insight: A Powerful Combination of Capabilities, DM Review, Feb [12] Sid Adelman, Larissa Moss, and Les Barbusinski, I found several definitions of BI, DM Review Online, August 2002.