"IMPACT OF BUSINESS INTELLIGENCE TOOLS IN EXECUTIVE INFORMATION SYSTEMS"
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – & TECHNOLOGY January- February (2013), © IAEME 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1,(IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), pp. 01-07 IJCET © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2012): 3.9580 (Calculated by GISI) ©IAEME www.jifactor.com IMPACT OF BUSINESS INTELLIGENCE TOOLS IN EXECUTIVE INFORMATION SYSTEMS 1 Mrs.R.Sharmila Research Scholar, Manonmaniam Sundaranar University, Tirunelveli Asst.Prof, Department of Applied Science Er. Perumal Manimekalai College of Engineering, Hosur. e-mail: email@example.com 2 Dr.A.Subramani Research Supervisor, Manonmaniam Sundaranar University, Tirunelveli Prof & Head, Department of MCA, KSR College of Engineering, Tiruchengode. e-mail: firstname.lastname@example.org ABSTRACT The Executive Information System (EIS) are designed to facilitate and support the information and strategic decision making needs of senior executives with the help of innovative tools and techniques. These technologies are known as Business Intelligence (BI) Tools. Business Intelligence in general deals with bringing the right information at the right time to the right people in the right format. The goal of the BI systems is to pull data from all internal systems and external sources to present a single version of the truth. This paper discusses about the importance and architecture of BI tools in EIS. Keywords: EIS, BI tools, Data Warehousing, OLAP tools, Dashboards, Reporting, etc. INTRODUCTION “Business Intelligence”, the term Coined by Gartnerin (1989) defined as using information effectively to make better decisions. BI is the core component of a company’s IT framework. The BI architecture consists of a Data Warehouse server which consolidates data from several operational databases, and serves a variety of front-end querying, reporting, and analytic tools. The back-end of the architecture is a data integration pipeline for populating the data warehouse by extracting data from distributed and usually heterogeneous operational sources; cleansing, integrating and transforming the data; and loading it into the data warehouse. Business intelligence tools require software applications capable of supporting: 1 International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME • Business intelligence solution design • Analytic business intelligence use • Business intelligence reporting and • Decision support requirements Executive Information System (EIS) is one of the most potent forms of computing, that serves the information needs of the top executives. Through EIS, the BI tools can identify problems and present trends that are of vital importance to the organization. EIS represents one of the most sophisticated applications of computer technology. Deploying EIS involves many risks: system design, data quality, and technology obsolescence. Finding Information needs is the very crucial stage in the design of Business Intelligence Tools and it is carried out with the support of Structured Interviews like IBM's Business System Planning (BSP), Critical Success Factors (CSF) and Ends/Means (E/M) Analysis. The key general categories of business intelligence tools are: Spreadsheets, Reporting and querying software (tools that extract, sort, summarize, and present selected data ), OLAP: Online analytical processing , Digital dashboards, Data mining, Data warehousing, Decision engineering, Process mining, Business performance management and Local information systems. Eclipse BIRT Project, RapidMiner, SpagoBI, R and KNIME are some of the open-source free products whereas Jaspersoft (Reporting, Dashboards, Data Analysis, and Data Integration) , Palo (OLAP database: OLAP Server, Worksheet Server and ETL Server) and Pentaho (Reporting, analysis, dashboard, data mining and workflow capabilities) are some of the open source commercial products. MOTIVATION AND BACKGROUND Business Intelligence Tools promotes managerial learning and provides manager’s access to the data delivers trends and assists in measuring performance in a timely manner. Instead of a small number of analysts spending 100% of their time analyzing data, all managers and professionals should spend 10% time using BI software. The Executive Information System developed by Executive information committee of Pennsylvania University in 1998 provided a view of the Admission information. Paul Frech, president of Lockheed-Georgia, monitors employee contributions to company-sponsored programs (United Way, blood drives) as a surrogate measure of employee morale (Houdeshel and Watson 1987). C. Robert Kidder, CEO of Duracell, found that productivity problems were due to salespeople in Germany wasting time calling on small stores and took corrective action (Main 1989). Vandenbosch and Huff (1992) from the University of Western Ontario found that Canadian firms using an EIS achieved better business results if their EIS promoted managerial learning. Potentially valuable content is frequently trapped in organizational silos, lost in transit from one system to another, bypassed by inadequately tuned data collection systems, or presented in user-unfriendly formats. Although wired with layers of information-gathering technology, organizations still find it difficult to deliver the right data to the right people. At the heart of these difficulties are inadequate executive information systems, supposedly designed to help top management easily access pertinent internal and external data for managing a company 2 International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME Jack Rockart’s (1979, 1982) field research stimulated the development of executive information systems (EIS) and executive support systems (ESS). These systems evolved from single user, model-driven decision support systems and from the development of new relational database products. The first EIS generally used pre-defined information displays maintained by analysts for senior executives. For example, in the Fall of 1978, Lockheed- Georgia began development of an EIS called Management Information and Decision Support (MIDS) system (cf., Houdeshel and Watson, 1987). EIS were specifically tailored to an executive's information needs. So there was a targeted user group. Managers using EIS were able to access data about specific issues and problems as well as read aggregated reports. Executive Information Systems, business intelligence and data warehousing technologies are converging in the marketplace. Twenty years ago, EIS used proprietary databases that required many staff people to update, maintain and create. This approach was very expensive and remains hard to justify. Organizing external data may however be best done in a dedicated database. Today executives need both structured and unstructured external data. Realistically external data becomes obsolete quickly and IS/IT staff aren't the appropriate maintainers for such data. Data warehouses, business intelligence technologies, the Web and OLAP have made Executive Information Systems potentially more powerful and more practical. Modern EIS should report key results to managers. Performance measures in an EIS must be easy to understand and collect. Wherever possible, data should be collected as part of routine work processes. An EIS should not add substantially to the workload of managers or staff. EIS should create value. According to Wikipedia, an EIS is commonly considered as a specialized form of a Decision Support System (DSS). We need information systems that are easy for senior executives to use. Modern EIS should provide timely delivery of secure, sensitive decision relevant company information; present information in a context that helps executives understand what is important and what is happening; provide filters and drill- down to reduce data overload; assist in tracking events, finding reports and monitoring results; and finally, a modern EIS should increase the efficiency and effectiveness of executive decision makers. ARCHITECTURE BI architecture was designed for strategic decision making, where a small number of expert users analyze historical data to prepare reports or build models, and decision making cycles last weeks or months. The architecture requires the data to be stored in a Data warehouse server. The data warehouse is described in terms of four inter‐related dimensions as shown in Fig.1: 1. Applications (or the business intelligence layer). 2. Data. 3. Technology and security. 4. Support—processes and organization 3 International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME Fig. 1 Data Warehouse components Umeshwar Dayal et. al (2009) has proposed the architecture as an information supply chain as shown in Fig. 2. Data Warehousing is a complex systems integration problem. A full-blown Data Warehousing System may encompass the data warehouse, various data marts (department, function, or application-specific DSSs, using Relational, Multidimensional (MOLAP), or Column-based Servers), One or more Operational Data Stores (ODSs) and One or more Data Staging Areas. Data from various heterogeneous sources like database files, flat files, raw files such as online transaction processing processing (OLTP) systems is extracted, cleansed, integrated, transformed and loaded into the Data Warehouse system. The first Data Warehousing Systems architecture begins with Extraction, Transformation, Migration, and Loading (ETML) process, Establishes the data warehouse first, along with centralized metadata repository, Data Marts are constituted from extracted and summarized data warehouse data and metadata. Organizations stores subsets of data in Data Marts for easier accessing. These processes are done by the BI such as ETL (Extract – Transform – Load) tools such as Informatics, Oracle’s Warehouse Builder, PowerCenter, Carleton, Sybase Adaptive server and so on. Fig.2 Architecture of Business Intelligence system 4 International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME For the information in the data warehouse to be valuable, it needs to be delivered in way that makes it useful to campus personnel in doing their jobs. This is the job of business intelligence applications. The warehouse includes a variety of these tools for reporting (or information delivery, including variants such as dashboards and alerts), Query and Modeling, Planning and Forecasting. REPORTING Data is useless if all it does is it in the data warehouse. As a result, the presentation layer is of very high importance. Reporting applications deliver information in a form which is useful to users. In their simplest form—fixed and printed reports. They imply a partnership between users who need information and specialists who help design reports, displays and graphics to deliver the required information. The best reports provide just the information a user needs for a specific purpose, delivered in a way which makes the information usable and actionable. Systems such as BAIRS and Cal‐Profiles focus on reporting. Most of the OLAP tools allow users to call up pre-defined reports or create adhoc reports. SAP Business Objects, MicroStrategy, IBM Cognos, Actuate, Jaspersoft, Pentaho, etc. are some of the tools used for reporting. DASHBOARDS AND ALERTS In the interest of delivering just the information most useful to support decision‐making or action, it will be useful to complement conventional reports with information dashboards and alerts. Dashboards communicate information with quickly‐comprehended graphics. Typically dashboards are used to report on established performance indicators, measured at predefined intervals. Properly planned, dashboards make it easy for personnel managing a complex process, such as undergraduate admissions, for example, to quickly assess current state and progress against goals. Likewise, sometimes reporting will be confined to exception conditions. Exceptions may deliver periodically as reports. More often, though, they will be delivered as alert messages—emails or similar notifications. For example, departmental administrators might get alerts when course enrollments exceed critical thresholds. Or a grant administrator might get an email when grant expenditures exceed plan for a given period. QUERY AND ANALYSIS People who have analytical skills and jobs requiring analysis will need the ability to explore the information in the warehouse. Available Data Warehousing query languages like MDX and Oracle OLAP support only numeric data and moreover require skilled DW developers to design queries. OLAP is well established in traditional business intelligence. To ensure premium performance, it is supported in InetSoft software by a special XMLA data source type. InetSoft provides an integrated OLAP front-end that allows business users to tap into this rich source of data. The ability to leverage a data warehouse investment, high performance, the ability to mashup OLAP data with other sources are the advantages of using OLAP based analysis. InetSoft's rich ad hoc analysis options empower business users of all skill levels by providing the OLAP server tools necessary to access insightful information. 5 International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME CASE STUDY Pepsi Co. CONCLUSION Information Systems are software products intended to store and handle data in an organization. They must meet the informational needs of all levels of management - operational, middle and top and they must be designed accordingly. Ann EIS should be designed to allow managers who are not trained to use query languages and advanced technologies, a fast, easy, and understandable way to navigate into data and identify trends and patterns. Developing EIS systems involves time, high-costs and human resources, efforts and an EIS must be capable to provide in real time representative information to the executive management. REFERENCES 1. Leslie Dolman, Frank Tompa, Iluju Kiringa, Rachel Pottinger and John Mylopoulos (2010), “Next generation Business Intelligence Tools”. Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research CASCON '10. Pp. 352-354 2. 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Dr. Manish Doshi, “Analysis Of Intelligent System Design By Neuro Adaptive Control, International Journal Of Advanced Research In Engineering & Technology (IJARET), Volume 2, Issue 1, 2011, pp. 1 - 11, Published by IAEME BIOGRAPHY Mrs. R.Sharmila is currently working as a Assistant Professor and Head, Department of Applied Science, Er.Perumal Manimekalai College of Engineering, Hosur and as a Research Scholar in Manonmaniam Sundaranar University, Tirunelveli. She received her M.Sc and MCA degrees from Periyar University, Salem and M.Phil., from Manonmaniam Sundaranar University, Tirunelveli .She published 10 technical papers in National Conference and 1 International conference. Her area of research is Data Mining. Dr. A. Subramani is currently working as a Professor and Head, Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode and as a Research Guide in various Universities. He received his Ph.D. Degree in Computer Applications from Anna University, Chennai. He is a Reviewer of 10 National/International Journals. He is in the editorial board of 6 International/National Journals. He is an Associate Editor of Journal of Computer Applications. He has published more than 30 technical papers at various International, National Journals and Conference proceedings. His areas of research include High Speed Networks, Routing Algorithm, Soft computing, Wireless Communications, Mobile Ad-hoc Networks. 7