Today's social media and cloud computing in business environment

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Today's social media and cloud computing in business environment lecture in Management Information Systems (MIS) 2014 course



2. OUTLINE Personal experience on information systems Theory and models on how to develop and acquire social media andcloud computing applications from systems perspective How social media and cloud computing applications are changingmanagement information systems in todays business environmentNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH3 3. INFORMATION SYSTEMS BACKGROUND 1996 Technical documentation specialist, Healy Chemicals Ltd. (UK) 1997 Data base programmer and administrator, Healy Chemicals Ltd. (UK) 1998 Computer sales and support, Puhetta Ltd. 1998 System administrator, Kuljetusliike K. Vierikko Ltd. & Kuljetus Veli Nikkil Ltd. 2001 Trainee / technical documentation, Steri Ltd. 2003- Entrepreneur / IS consultant, Ins. tst. Jari Jussila 2002- Project engineer, HAMK University of Applied Sciences,2004 e.g. PLC, SCADA, MMS, ERP, BI 2007- Researcher, Tampere University of Technology2009 e.g. Web 2.0, Scrum, Agile and Lean software development 2009 CEO / IS & KM & M consultant, Yoso Services Ltd.2011 e.g. Cloud computing (SaaS and IaaS) 2009 Project planner, Technology Centre Innoparke.g. Social media 2010- Project manager, Tampere University of TechnologyNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH4 4. WHAT IS A MANAGEMENT INFORMATIONSYSTEM?Goal: to inspire double loop learningNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH5 5. SINGLE-LOOP AND DOUBLE-LOOPLEARNINGGoverningvariablesAction strategiesResults /consequencesSINGLE-LOOP LEARNINGDOUBLE-LOOP LEARNINGAssumptions / beliefsRef. Argyris & Schn 1978; Argyris 1990NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH6 6. HOW TO DEVELOP OR ACQUIRE (I.E. MAKE OR BUY)INFORMATION SYSTEMS IN TODAYS BUSINESS ENVIRONMENTRef. Lyytinen 1987e.g. Waterfall e.g. Agilee.g. Lean Startupe.g. VanguardNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH7 7. WORK SYSTEM THEORY AND WORKSYSTEM METHODRef. Alter 2013NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH8 8. ACTIVITY THEORY, ACTIVITY SYSTEMSAND EXPANSIVE LEARNINGRef. Engestrm 2001; Engestrm 1987NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH9 9. INTERACTING ACTIVITY SYSTEMS EXPANSIVELEARNING IN INTERACTING ACTIVITY SYSTEMSInstrumentsRulesDivision oflabourSubjectCommunityTransformationObject ->OutcomeMotivationDivision oflabourSubjectInstrumentsCommunity Rulese.g. IaaS provider e.g. customerRef. Vartiainen, Aramo-Immonen, Jussila et al. 2011NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH10 10. ACTIVITY SYSTEM CYCLE OFEXPANSIVE LEARNING1. Questioning2. Historical analysis & actual-empiricalanalysis3. Modeling the new solution4. New model5. Implementing the new model6. Reflecting on the process7. Consolidating the new practiceWORK SYSTEM LIFE CYCLE1. Initiation2. Development3. Implementation4. Operation and maintenanceRef. Vartiainen, Aramo-Immonen, Jussila et al. 2011NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH11 11. COMMAND & CONTROL VS SYSTEMS THINKING VIEWCommand and Control Thinking Systems ThinkingTop-down, hierarchy Perspective Outside-in, systemFunctional Design Demand, value and flowSeparated from work Decision-making Integrated with workOutput, targets, standards: related tobudgetMeasurementCapability, variation: related topurposeContractual Attitude to customers What matters?Contractual Attitude to suppliers CooperativeManage people and budgets Role of management Act on the systemControl Ethos LearningReactive, projects Change Adaptive, integralExtrinsic Motivation IntrinsicRef. Seddon 2011NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH12 12. 13LEAN STARTUP 13. LITERATURE ON HOW TO CHANGE MANAGEMENT THINKING INTODAYS BUSINESS ENVIRONMENTNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH14 14. EXAMPLES OF SOCIAL MEDIA ANDCLOUD COMPUTING ENVIRONMENTSPublic social media and cloudapplicationsBusiness oriented public socialmedia and cloud applicationsPrivate business oriented socialmedia and cloud applicationse.g. Google Documents e.g. Yammer e.g. phpBBe.g. WordPress e.g. ZenDesk e.g. Confluencee.g. Facebook e.g. SharePoint Online e.g. SharePoint ServerNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH15 15. Ref. Schubert & Jeffery 2012NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH16 16. PRIMARY CHARACTERISTICS OF CLOUDSYSTEMS Utility computing allow easy outsourcing of IT resources infrastructure, by moving the in-houseapplications to a dedicated (public) CLOUD provider Elasticity allow the environment to ideally automatically assign a dynamic number ofresources to a task Availability and Reliability CLOUD systems build up on the internet of service principle to expose the servicesin a highly accessible fashion, i.e. with minimal configuration and devicerequirements (generally through a browser). CLOUDs enhance this aspect furtherby virtualising the service / resource access, basically allowing access anywhere,anytime Ease of use The fact is that CLOUDs can reduce the overhead for managing and administeringresources through automation and outsourcing, and should reduce the overheadfor creating highly available and reliable servicesRef. Schubert & Jeffery 2012NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH17 17. SOCIAL MEDIA GENRESRef. Lietsala & SirkkunenNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH18 18. More on social mediatools and processes: Lietsala & SirkkunenRef. Lietsala & Sirkkunen 2008NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH19 19. ADOPTION OF CORPORATETECHNOLOGIESAutomating transactions Enabling collaboration and participationAdoption of ERP, CRM, SCM- Users assigned bymanagement- Users must comply withrules- Often complex technologyinvestmentAdoption of Web 2.0 tools- User groups can formunexpectedly- Users engage in highdegree participation- Technology investmentoften lightweight overlay toexisting infrastructureTraditional ITWeb 2.0 toolsTime1990s 2009ProductivityRef. McKinsey 2009NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH20 20. 21Ref. Pirttil 2014NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH 21. Changed service systems enabled by corporate technologiesRef. Pirttil 2014NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH22 22. A DATA PERSPECTIVE ON INFORMATION SYSTEMSWEBBIG DATAWeb logsOffer history A / B testingERPCRMostotiedotmaksutiedotsegmentointitarjoustiedotasiakaskohtaamisettukikontaktitSentimentDynamic PricingAffiliate NetworksSearch marketingBehavioral targetingDynamic FunnelsSocial NetworkExternal DemographicsBusiness Data FeedsPictures & VideoSpeech to TextSensor DataProduct/Service LogsSMS/MMSUser Generated ContentMobile WebUser Click StreamLocation DataRef. Yli-Pietil & Backman 2013; Valli & Ahlgren 2013NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH23 23. EXAMPLE OF PREDICTION MARKETSRef. Wolfers & Zitzewitz 2004NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH24 24. EXAMPLE OF NEW APPLICATION INSALES FUNCTION Concern over the taste of a new coffee Social media was followed Taste was fine, but price was too high Price was reduced the same dayRef. Kaisler et al. 2013 Introduction to Big DataNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH25 25. EXAMPLE OF NEW APPLICATION INSERVICE FUNCTION Business challenge Needed to gain deeper insights into the causes and combinations ofcircumstances which led to warranty issues Needed to increase customer satisfaction through increased product qualityand reduced warranty issues Solution implemented Implemented a data mining capability to gain actionable insights across awide range of warranty issues Feedback of issue findings into product design process for improvementsand modified service patterns where these were demonstrated to havecontributed to warranty issues Demonstrated business value Reduced warranty cases from 1.1 to 0.85 per vehicle 5% reduction in warranty cases Annual savings of 30m approximatelyRef. IBM 2013 Improving Operational and Financial Results through Predictive MaintenanceNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH26 26. BIG DATA DRIVEN AGENT-BASED MODEL INPREDICTION BUSINESS DECISIONSThe average cumulative sales of PS3 and Xbox 360 predicted by the modelagainst the real cumulative sales of the two consoles.Ref. Huotari et al. 2014 Winter Simulation Conference*NOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH27Huotari, Jrvi, Kortelainen et al. 2014 27. From reporting to operational analytics OPERATIONAL INTELLIGENCEREPORTINGWHAThappened?Batch reportsANALYZINGWHYdid it happen?Ad Hoc,BI toolsPREDICTINGWHAT WILLhappen?PredictivemodelsOPERATIONALIZINGWHAT IShappening now?Link toOperationalSystemsACTIVATINGMAKEit happenAutomaticLinkagesSTRATEGIC INTELLIGENCENOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH28Yli-Pietil & Backman 2013 28. NEW TRENDS IN MANAGEMENTINFORMATION SYSTEMS Using collective intelligence and wisdom of crowds in decisionmaking e.g. Prediction markets Using real-time or near real-time data in decision making e.g. Starbucks product price optimization e.g. Telefonica customer support Using increased computing power in predicting results / options inmanagement decision making Automated decision makingNOVI RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH29 29. Slideshare: CONTENT ON KNOWLEDGEMANAGEMENT:Twitter: @Noviresearch ja @tietojohtaminenYouTube: research centers website RESEARCH CENTERTUT.FI/NOVITWITTER: @NOVIRESEARCH