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system informacji zarządczej - efektywna integracja i analiza danych

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Warianty tytułu
EN
business intelligence - effective integration and analysis of data
Języki publikacji
PL
Abstrakty
EN
Today's fast-paced, competitive marketplace means increasing pressure to raise profits, do more with less and operates more efficiently. Achieving these goals requires faster and improved decision-making ability across organizations. Unfortunately in many situations, departments continue to operate in information silos. Existing enterprise systems often are not linked and software packages are not integrated. Decision makers have difficulty getting the accurate information they need and IT professionals spend more time responding to ad-hoc report requests than focusing on strategic initiatives. Business intelligence integrates data from across your enterprise, and provides self-service reporting and analysis at everyone's "fingertips". Decision makers spend less time looking for answers and more time driving strategic decisions. BI decision-support initiatives are also very expensive endeavors. Disparate business data must be extracted and merged from online transaction processing (OLTP) systems, from batch systems, and from externally syndicated data sources. BI decision-support initiatives also call for new technology to be considered, additional tasks to be performed, roles and responsibilities to be shifted, and analysis and decision-support applications to be delivered quickly while maintaining acceptable quality. A staggering 60 percent of BI projects end in abandonment or failure because of inadequate planning, missed tasks, missed deadlines, poor project management, undelivered business requirements, or poor quality deliverables. An effective methodology of introducing the BI decision-support initiatives can lower this high investment risk. BI projects can be organized according to the six stages and 16 development steps within these stages as outlined in this article.
Rocznik
Strony
37--48
Opis fizyczny
Bibliogr. 15 poz.
Twórcy
autor
  • Instytut Organizacji Systemów Produkcyjnych Wydział Inżynierii Produkcji Politechnika Warszawska ul. Narbutta 85, 02-524 Warszawa tel. (022) 234-85-10, ks.rostek@neostrada.pl
Bibliografia
  • [1] Amarowicz A.: Business Intelligence - od wiedzy do trafnych decyzji biznesowych, "Strategie Biznesu", SAP Polska 2006.
  • [2] Biere M.: Business Intelligence for the Enterprise, Prentice Hali PTR, lipiec 2003.
  • [3] Jaworska E.: Wycisnąć z danych co się da, Manufacturing Systems Information Polska, grudzień 2005.
  • [4] Kuhn M., Łopata D., Todd G. B.: Mnóstwo danych, wniosków brak. Business Intelligence, "Outlook" 2005, nr 1, str. 35-41.
  • [5] Miller G.: The Business Intelligence Competency Center: A SAS Approach, SAS Publishing, grudzień 2005.
  • [6] Moss L. T., Atre Shaku: Business Intelligence Road-map: The Complete Project Lifecycle for Decision-Support Application, Addison Wesley, luty 2003.
  • [7] Rasmussen N., Goldy P. S., Solli P. O.: Financial Business Intelligence, John Wiley & Sons Inc., New York 2002.
  • [8] Sid A., Moss L. T.: Data Warehouse Project Management, Addison-Wesley, 2000.
  • [9] Steensboe Ch., Andsbjerg R.: SASBIEnables Optimi-zed Controland Steering at UKA, IDCAnalyze the Future, IDC 2006.
  • [10] Voges K. E., Pope N.: Business Applications and Computational Intelligence, Idea Group Publishing 2006.
  • [11] www.oracle.com, stan na 10 stycznia 2007.
  • [12] www.microsoft.com/poland/, stan na 20 stycznia 2007.
  • [13] www.pmslabs.com.pl, stan na 8 stycznia 2007.
  • [14] www.sas.com, stan na 10 stycznia 2007.
  • [15] www.sybase.pl, stan na 10 stycznia 2007.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD5-0001-0004
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