Business Intelligence Trends 商業智慧趨勢

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商業智慧導入與趨勢 (Business Intelligence Implementation and Trends). Business Intelligence Trends 商業智慧趨勢. 1012BIT08 MIS MBA Mon 6, 7 (13:10-15:00) Q407. Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept. of Information Management , Tamkang University 淡江大學 資訊管理學系 http://mail. tku.edu.tw/myday/ - PowerPoint PPT Presentation

Text of Business Intelligence Trends 商業智慧趨勢

  • Business Intelligence Trends*1012BIT08MIS MBA Mon 6, 7 (13:10-15:00) Q407 (Business Intelligence Implementation and Trends)Min-Yuh DayAssistant Professor Dept. of Information Management, Tamkang University

    http://mail. tku.edu.tw/myday/2013-05-27

  • Subject/Topics1 102/02/18 (Course Orientation for Business Intelligence Trends)2 102/02/25 (Management Decision Support System and Business Intelligence)3 102/03/04 (Business Performance Management)4 102/03/11 (Data Warehousing)5 102/03/18 (Data Mining for Business Intelligence)6 102/03/25 (Data Mining for Business Intelligence)7 102/04/01 (Off-campus study)8 102/04/08 (SAS EM ) Banking Segmentation (Cluster Analysis KMeans using SAS EM)9 102/04/15 (SAS EM ) Web Site Usage Associations ( Association Analysis using SAS EM)

    (Syllabus)*

  • Subject/Topics10 102/04/22 (Midterm Presentation)11 102/04/29 (SAS EM ) Enrollment Management Case Study (Decision Tree, Model Evaluation using SAS EM)12 102/05/06 (SAS EM ) Credit Risk Case Study (Regression Analysis, Artificial Neural Network using SAS EM)13 102/05/13 (Text and Web Mining)14 102/05/20 (Opinion Mining and Sentiment Analysis)15 102/05/27 (Business Intelligence Implementation and Trends)16 102/06/03 (Business Intelligence Implementation and Trends)17 102/06/10 1 (Term Project Presentation 1)18 102/06/17 2 (Term Project Presentation 2) (Syllabus)*

  • OutlineBusiness Intelligence ImplementationBusiness Intelligence TrendsBig Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses*

  • *Business Intelligence Implementation

  • Business Intelligence ImplementationCSFs Framework for Implementation of BI Systems*Yeoh, W., & Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of computer information systems, 50(3), 23.

  • Critical Success Factors of Business Intelligence ImplementationOrganizational dimensionCommitted management support and sponsorshipClear vision and well-established business caseProcess dimensionBusiness-centric championship and balanced team compositionBusiness-driven and iterative development approachUser-oriented change management.Technological dimensionBusiness-driven, scalable and flexible technical frameworkSustainable data quality and integrity*Yeoh, W., & Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of computer information systems, 50(3), 23.

  • Business Intelligence Trends*

  • Business Intelligence TrendsAgile Information Management (IM)Cloud Business Intelligence (BI)Mobile Business Intelligence (BI)AnalyticsBig Data*Source: http://www.businessspectator.com.au/article/2013/1/22/technology/five-business-intelligence-trends-2013

  • Business Intelligence Trends: Computing and ServiceCloud Computing and ServiceMobile Computing and ServiceSocial Computing and Service *

  • Business Intelligence and AnalyticsBusiness Intelligence 2.0 (BI 2.0)Web IntelligenceWeb AnalyticsWeb 2.0Social Networking and Microblogging sitesData TrendsBig DataPlatform Technology TrendsCloud computing platform*Source: Lim, E. P., Chen, H., & Chen, G. (2013). Business Intelligence and Analytics: Research Directions. ACM Transactions on Management Information Systems (TMIS),3(4), 17

  • Business Intelligence and Analytics: Research Directions1. Big Data AnalyticsData analytics using Hadoop / MapReduce framework2. Text AnalyticsFrom Information Extraction to Question AnsweringFrom Sentiment Analysis to Opinion Mining3. Network AnalysisLink miningCommunity DetectionSocial Recommendation*Source: Lim, E. P., Chen, H., & Chen, G. (2013). Business Intelligence and Analytics: Research Directions. ACM Transactions on Management Information Systems (TMIS),3(4), 17

  • Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses

    *

  • *Source: McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution.Harvard business review.Big Data: The Management Revolution

  • *Source: McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution.Harvard business review.

  • *Source: http://www.amazon.com/Enterprise-Analytics-Performance-Operations-Management/dp/0133039439

  • Business Intelligence and Enterprise AnalyticsPredictive analyticsData miningBusiness analyticsWeb analyticsBig-data analytics*Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012

  • Three Types of Business AnalyticsPrescriptive AnalyticsPredictive AnalyticsDescriptive Analytics*Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012

  • Three Types of Business Analytics*Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012OptimizationStatistical ModelingRandomized TestingPredictive Modeling / ForecastingAlertsQuery / Drill DownAd hoc Reports / ScorecardsStandard ReportWhats the best that can happen?What if we try this?What will happen next?Why is this happening?What actions are needed?What exactly is the problem?How many, how often, where?What happened?Prescriptive AnalyticsPredictive AnalyticsDescriptive Analytics

  • Big-Data AnalysisToo Big, too Unstructured, too many different source to be manageable through traditional databases*

  • The Rise of Big DataToo Big means databases or data flows in petabytes (1,000 terabytes)Google processes about 24 petabytes of data per dayToo unstructured means that the data isnt easily put into the traditional rows and columns of conventional databases*Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012

  • Examples of Big DataOnline informationClickstream data from Web and social media contentTweetsBlogsWall postingsVideo dataRetail and crime/intelligence environmentsRendering of video entertainmentVoice data call centers and intelligence interventionLife sciencesGenomic and proteomic data from biological research and medicine*Source: Thomas H. Davenport, "Enterprise Analytics: Optimize Performance, Process, and Decisions Through Big Data", FT Press, 2012

  • *Source: http://www.amazon.com/Big-Data-Analytics-Intelligence-Businesses/dp/111814760X

  • *Source: http://www.amazon.com/Big-Data-Analytics-Intelligence-Businesses/dp/111814760X

  • Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's BusinessesWhat Big Data is and why it's importantIndustry examples (Financial Services, Healthcare, etc.)Big Data and the New School of MarketingFraud, risk, and Big DataBig Data technologyOld versus new approachesOpen source technology for Big Data analyticsThe Cloud and Big Data*Source: http://www.amazon.com/Big-Data-Analytics-Intelligence-Businesses/dp/111814760X

  • Predictive analyticsCrowdsourcing analyticsComputing platforms, limitations, and emerging technologiesConsumption of analyticsData visualization as a way to take immediate actionMoving from beyond the tools to analytic applicationsCreating a culture that nurtures decision science talentA thorough summary of ethical and privacy issues*Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's BusinessesSource: http://www.amazon.com/Big-Data-Analytics-Intelligence-Businesses/dp/111814760X

  • What is BIG Data?VolumeLarge amount of dataVelocityNeeds to be analyzed quicklyVarietyDifferent types of structured and unstructured data

    *Source: http://visual.ly/what-big-data

  • Big Ideas: How Big is Big Data?*Source: http://www.youtube.com/watch?v=eEpxN0htRKI

  • Big Ideas: Why Big Data Matters*Source: http://www.youtube.com/watch?v=eEpxN0htRKI

  • Key questions enterprises are asking about Big DataHow to store and protect big data?How to backup and restore big data?How to organize and catalog the data that you have backed up?How to keep costs low while ensuring that all the critical data is available when you need it?*Source: http://visual.ly/what-big-data

  • Volumes of DataFacebook30 billion pieces of content were added to Facebook this past month by 600 million plus usersYoutubeMore than 2 billion videos were watch on YouTube yesterdayTwitter32 billion searches were performed last month on Twitter*Source: http://visual.ly/what-big-data

  • *Source: http://www.business2community.com/big-data/big-data-big-insights-for-social-media-with-ibm-0501158

  • *Source: http://2centsapiece.blogspot.tw/Social Media

  • *Source: http://www.forbes.com/sites/davefeinleib/2012/06/19/the-big-data-landscape/

  • *Source: http://mattturck.com/2012/10/15/a-chart-of-the-big-data-ecosystem-take-2/

  • *Big Data Vendors and TechnologiesSource: http://www.capgemini.com/blog/capping-it-off/2012/09/big-data-vendors-and-technologies-the-list

  • Processing Big DataGoogle*Source: http://whatsthebigdata.files.wordpress.com/2013/03/google_datacenter.jpg

  • Processing Big Data, Facebook*http://gigaom.com/2012/08/17/a-rare-look-inside-facebooks-oregon-data-center-photos-video/

  • Data Sci