Impact of business intelligence tools in executive information systems


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Impact of business intelligence tools in executive information systems

  1. 1. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1,(IJCET) & TECHNOLOGY January- February (2013), © IAEMEISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online)Volume 4, Issue 1, January- February (2013), pp. 01-07 IJCET© IAEME: Impact Factor (2012): 3.9580 (Calculated by GISI) © 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: 2 Dr.A.Subramani Research Supervisor, Manonmaniam Sundaranar University, Tirunelveli Prof & Head, Department of MCA, KSR College of Engineering, Tiruchengode. e-mail: 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
  2. 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 • 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 canidentify problems and present trends that are of vital importance to the organization. EISrepresents one of the most sophisticated applications of computer technology. Deploying EISinvolves many risks: system design, data quality, and technology obsolescence. Finding Information needs is the very crucial stage in the design of BusinessIntelligence Tools and it is carried out with the support of Structured Interviews like IBMsBusiness 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 selecteddata ), OLAP: Online analytical processing , Digital dashboards, Data mining, Datawarehousing, Decision engineering, Process mining, Business performance management andLocal information systems. Eclipse BIRT Project, RapidMiner, SpagoBI, R and KNIME aresome of the open-source free products whereas Jaspersoft (Reporting, Dashboards, DataAnalysis, and Data Integration) , Palo (OLAP database: OLAP Server, Worksheet Server andETL Server) and Pentaho (Reporting, analysis, dashboard, data mining and workflowcapabilities) are some of the open source commercial products.MOTIVATION AND BACKGROUND Business Intelligence Tools promotes managerial learning and provides manager’saccess 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, allmanagers and professionals should spend 10% time using BI software. The Executive Information System developed by Executive information committee ofPennsylvania University in 1998 provided a view of the Admission information. Paul Frech,president of Lockheed-Georgia, monitors employee contributions to company-sponsoredprograms (United Way, blood drives) as a surrogate measure of employee morale (Houdesheland Watson 1987). C. Robert Kidder, CEO of Duracell, found that productivity problemswere due to salespeople in Germany wasting time calling on small stores and took correctiveaction (Main 1989). Vandenbosch and Huff (1992) from the University of Western Ontariofound that Canadian firms using an EIS achieved better business results if their EIS promotedmanagerial learning. Potentially valuable content is frequently trapped in organizational silos, lost in transitfrom one system to another, bypassed by inadequately tuned data collection systems, orpresented in user-unfriendly formats. Although wired with layers of information-gatheringtechnology, organizations still find it difficult to deliver the right data to the right people. Atthe heart of these difficulties are inadequate executive information systems, supposedlydesigned to help top management easily access pertinent internal and external data formanaging a company 2
  3. 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 Jack Rockart’s (1979, 1982) field research stimulated the development of executiveinformation systems (EIS) and executive support systems (ESS). These systems evolved fromsingle user, model-driven decision support systems and from the development of newrelational database products. The first EIS generally used pre-defined information displaysmaintained 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 executives information needs. So there was atargeted user group. Managers using EIS were able to access data about specific issues andproblems as well as read aggregated reports. Executive Information Systems, businessintelligence and data warehousing technologies are converging in the marketplace. Twentyyears ago, EIS used proprietary databases that required many staff people to update, maintainand create. This approach was very expensive and remains hard to justify. Organizingexternal data may however be best done in a dedicated database. Today executives need bothstructured and unstructured external data. Realistically external data becomes obsoletequickly and IS/IT staff arent the appropriate maintainers for such data. Data warehouses,business intelligence technologies, the Web and OLAP have made Executive InformationSystems potentially more powerful and more practical. Modern EIS should report key results to managers. Performance measures in an EISmust be easy to understand and collect. Wherever possible, data should be collected as part ofroutine work processes. An EIS should not add substantially to the workload of managers orstaff. EIS should create value. According to Wikipedia, an EIS is commonly considered as aspecialized form of a Decision Support System (DSS). We need information systems that areeasy for senior executives to use. Modern EIS should provide timely delivery of secure,sensitive decision relevant company information; present information in a context that helpsexecutives understand what is important and what is happening; provide filters and drill-down to reduce data overload; assist in tracking events, finding reports and monitoringresults; and finally, a modern EIS should increase the efficiency and effectiveness ofexecutive decision makers.ARCHITECTURE BI architecture was designed for strategic decision making, where a small number ofexpert users analyze historical data to prepare reports or build models, and decision makingcycles last weeks or months. The architecture requires the data to be stored in a Datawarehouse server. The data warehouse is described in terms of four inter‐related dimensionsas shown in Fig.1: 1. Applications (or the business intelligence layer). 2. Data. 3. Technology and security. 4. Support—processes and organization 3
  4. 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 Fig. 1 Data Warehouse components Umeshwar Dayal et. al (2009) has proposed the architecture as an information supplychain as shown in Fig. 2. Data Warehousing is a complex systems integration problem. Afull-blown Data Warehousing System may encompass the data warehouse, various datamarts (department, function, or application-specific DSSs, using Relational,Multidimensional (MOLAP), or Column-based Servers), One or more Operational DataStores (ODSs) and One or more Data Staging Areas. Data from various heterogeneous sources like database files, flat files, raw files suchas online transaction processing processing (OLTP) systems is extracted, cleansed,integrated, transformed and loaded into the Data Warehouse system. The first DataWarehousing Systems architecture begins with Extraction, Transformation, Migration, andLoading (ETML) process, Establishes the data warehouse first, along with centralizedmetadata repository, Data Marts are constituted from extracted and summarized datawarehouse data and metadata. Organizations stores subsets of data in Data Marts for easieraccessing. These processes are done by the BI such as ETL (Extract – Transform – Load)tools such as Informatics, Oracle’s Warehouse Builder, PowerCenter, Carleton, SybaseAdaptive server and so on. Fig.2 Architecture of Business Intelligence system 4
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMEFor the information in the data warehouse to be valuable, it needs to be delivered in way thatmakes it useful to campus personnel in doing their jobs. This is the job of businessintelligence applications. The warehouse includes a variety of these tools for reporting (orinformation 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 presentationlayer is of very high importance. Reporting applications deliver information in a form whichis useful to users. In their simplest form—fixed and printed reports. They imply a partnershipbetween users who need information and specialists who help design reports, displays andgraphics to deliver the required information. The best reports provide just the information auser needs for a specific purpose, delivered in a way which makes the information usable andactionable. Systems such as BAIRS and Cal‐Profiles focus on reporting. Most of the OLAPtools allow users to call up pre-defined reports or create adhoc reports. SAP BusinessObjects, MicroStrategy, IBM Cognos, Actuate, Jaspersoft, Pentaho, etc. are some of the toolsused for reporting.DASHBOARDS AND ALERTSIn the interest of delivering just the information most useful to support decision‐making oraction, it will be useful to complement conventional reports with information dashboards andalerts. Dashboards communicate information with quickly‐comprehended graphics. Typicallydashboards are used to report on established performance indicators, measured at predefinedintervals. Properly planned, dashboards make it easy for personnel managing a complexprocess, such as undergraduate admissions, for example, to quickly assess current state andprogress against goals. Likewise, sometimes reporting will be confined to exceptionconditions. Exceptions may deliver periodically as reports. More often, though, they will bedelivered as alert messages—emails or similar notifications. For example, departmentaladministrators might get alerts when course enrollments exceed critical thresholds. Or a grantadministrator might get an email when grant expenditures exceed plan for a given period.QUERY AND ANALYSISPeople who have analytical skills and jobs requiring analysis will need the ability to explorethe information in the warehouse. Available Data Warehousing query languages like MDXand Oracle OLAP support only numeric data and moreover require skilled DW developers todesign queries. OLAP is well established in traditional business intelligence. To ensurepremium performance, it is supported in InetSoft software by a special XMLA data sourcetype. InetSoft provides an integrated OLAP front-end that allows business users to tap intothis rich source of data. The ability to leverage a data warehouse investment, highperformance, the ability to mashup OLAP data with other sources are the advantages of usingOLAP based analysis. InetSofts rich ad hoc analysis options empower business users of allskill levels by providing the OLAP server tools necessary to access insightful information. 5
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMECASE STUDYPepsi Co.CONCLUSION Information Systems are software products intended to store and handle data in anorganization. They must meet the informational needs of all levels of management -operational, middle and top and they must be designed accordingly. Ann EIS should bedesigned to allow managers who are not trained to use query languages and advancedtechnologies, a fast, easy, and understandable way to navigate into data and identify trendsand patterns. Developing EIS systems involves time, high-costs and human resources, effortsand an EIS must be capable to provide in real time representative information to theexecutive 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. Jui-Yu Wu (2010), “Computational Intelligence-Based Intelligent Business Intelligence System: Concept and Framework. International conference on Computer and Network Technology. Pp. 334-338. 3. Milena Tvrdikova (2007), “Support of Decision Making by Business Intelligence Tools”. International Conference on Computer Information Systems and Industrial Management Applications. Pp. 364-368. 4. Sara Reese Hedberg (1996), “AI tools for Business-Process Modeling”. IEEE InBusiness Intelligence Systems. Pp.13-15 5. Liya Wu, Gilad Barash, Claudio Bartolini (2007). “A Service-Oriented architecture for Business Intelligence”. IEEE International conference on Service-oriented Computing and Applications. Pp. 279-285 6. Sixto Ortiz Jr. (2010), “Taking Business Intelligence to the Masses”. Computer. Vol 43, No. 7, Pp. 12-15. 7. David King, Daniel O’Leary (1996). “Intelligent Executive Information System”. IEEE Intelligent Systems, Pp. 30-35 8. Hugh J. Watson, Barbara H. Wixom (2007), “The Current State of Business Intelligence”. Computer. Pp.96-99 9. Umeshwar Dayal, Malu Castellanos, Alkis Simitsis and Kevin Wilkinson (2009), “Data Integration Flows for Business Intelligence. EDBT09, March 24-26, Saint Petersburg, Russia. 10. “Building information out of data: Executive information system at Pennstate” (1998), The Pennsylvania State University, Coordinating Committee. 11. Dan Power , Are Executive Information Systems (EIS) , Editor, 12. Lungu Ion and Vatuiu Teodora , “Executive Information Systems : Development Life cycle and building by using the Business Intelligence Tools”. 6
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME 13. Bharathi M A, Vijaya Kumar B P and Manjaiah D.H, “Power Efficient Data Aggregation Based On Swarm Intelligence And Game Theoretic Approach In Wireless Sensor Network, International journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 184 - 199, Published by IAEME 14. N. Sivakumar, Dr. P. Sivaraman, N. Tamilselvan and Dr. R. Sevukan, “Digital Content Management System: A Conceptual Framework, International journal of Computer Engineering & Technology (IJCET). Volume 2, Issue 2, 2011, pp. 16 - 24, Published by IAEME 15. 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