Transforming Big Data Sets into Business Assets by Prof. Ravi Vatapu, CBS
Transcript of Transforming Big Data Sets into Business Assets by Prof. Ravi Vatapu, CBS
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Ravi VatrapuDirector, Centre for Business Data Analytics (bda.cbs.dk)
Professor, Department of IT ManagementCopenhagen Business School, Denmark
Email: [email protected]: http://www.cbs.dk/en/staff/rvitm
Centre: http://bda.cbs.dk
Transforming Big Data Sets into Business Assets
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• Phenomena• Internet, Social Media & Society• Challenges & Opportunties: In-House Data + Big Data• Business Value: Big Data Sets à Business Assets
• Centre for Business Data Analytics (bda.cbs.dk)• Meaningful Facts• Actionable Insights• Valuable Outcomes• Sustainable Impacts
• Case Projects• Predictive Models• Prescriptive Analytics• Visual Analytics
• Our Product and Service Portfolio
Outline
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About Me: Global Nomad
Vizag,India Blacksburg,USA
Honolulu,USACopenhagen,Denmark
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Part I:Phenomena
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Internet, Social Media & Society
https://en.wikipedia.org/wiki/On_the_Internet,_nobody_knows_you're_a_dogRamu:“OntheFacebook,everybodyknowsIamadog"
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Challenges: How to Combine House Data with Big Data
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Opportunities: Business Value = In-House Data + Big Data
Porta,M.,House,B.,Buckley,L.&Blitz,A.(2008)
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Business Value = In-House Data + Big Data
Wollan,R.,Smith,N.&Zhou,C(2011)SonyPS4Controller:“Share”Button
ImagefromKotaku
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Big Data Sets à Business AssetsCase: Product: Baby-Monitors
MasterThesis:AdeleIndianeGurrich Kristensen&StineSofieBragdø
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Part II: CSSL ApproachSet-Theoretical Big Social Data Analytics
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CentreforBusinessDataAnalytics(cbsBDA)locatedattheDept.ofITManagement,CopenhagenBusinessSchool.cbsBDA conductstransdisciplinarybasicresearchonsocio-technicalinteractions withspecificapplicationstomanagersincompanies,teachersinschoolsandresidentsincities.
1Director&Professor2AssistantProfessors10PhDStudents4ResearchAssociates+11FacultyCollaboratorsatCBS,KUandbeyond
(IEEEEDOC2014)
cbsBDA (bda.cbs.dk)
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cbsBDA’s Naïve Model for Applied Research
• Symptoms
• Diagnosis
• Therapy• Prescription• Proscription
• Prognosis• Positive/Negative
MARCELLUS:
SomethingisrotteninthestateofDenmark
http://shakespeare.mit.edu/hamlet/full.html
https://en.wikipedia.org/wiki/File:Helsing%C3%B8r_Elsinore_from_sea_01.jpg
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Class of Problems: Social Associations (Organisations)
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SocialData
Interactions Conversations
Actors ArtifactsActivitiesActions Topics EmotionsPronounsKeywords
Source:RaviVatrapu
Conceptual Model of Social Data
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Analytical Framework for Set-Theoretical CSS
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McDonaldsDKActors:266,000Noma Actors:4,567McDonaldsDK&Noma Actors:203
CROSS-WALL ANALYSIS:MCDONALDS DKVS.NOMA
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• Whatkindsofsocialtextdothese203 actorscreate,circulate,andinteractwith?
• What,ifany,isthecross-culturalvariationofactorsassociatingwithbothfastfoodandfinedining?
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Part III: Case Projects
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Case Project #1: Loyalty Club Programs
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• Datasets• CRM
• Interviews
Case Project #1: Loyalty Club Programs
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© Temperaturenpådanskeloyalitetsklubberanno2015-II 20
BigSocialDataAnalytics
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Foragivensocialmediaaction,wewanttoanalyse andmodel:
• UserCharacteristics• Emotion• Personality
• User/ConsumerCharacteristics• ConsumerDecision-MakingStage
• Organisational Consequences• BrandSentiment
• SocialMediaConsequences• SocialEngagementPotential
Beyond Social Media-->Towards Social Business
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BeyondSocialMedia-->TowardsSocialBusiness“Heres anidea.Ifyouliketheirfoodeatthere.Ifyoudont liketheirfoodeatsomewhereelseormakeyourownmeal.
Ireallydont understandwhatthebigdealis.”
User Consumer
Organisation
SocialInfluence
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TextClassification:Multi-DimensionalModels
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BasicEmotions
0.00% 20.00% 40.00% 60.00% 80.00% 100.00%
BRmatascoop
ForbrugsforeningenIKEA
imercolOplus
Sportsmaster
BasicEmotions:Proportion
Joy% Sadness% Surprise% Fear% Disgust% Anger%
0 0.2 0.4 0.6 0.8 1
BRmatascoop
ForbrugsforeningenIKEA
imercolOplus
Sportsmaster
BasicEmotions:Intensity
JoyIntensity SadnessIntensity SurpriseIntensity
FearIntensity DisgustIntensity AngerIntensity
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BrandParameters:HistoricalDevelopment:UserEmotionsvs.BrandSentiment:
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PredictingNetPromotorScoreFromBigSocialData
R²=0.95813
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0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70NPS Poly.(NPS)
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Case Project #2: Market & User Segmentation
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CROSS-WALL ANALYSIS:USER/CUSTOMER SEGMENTATION
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DK2011 US2008
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ENGAGEMENT DIMENSIONS &USER SEGMENTS
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SOCIAL NETWORK DIALOG SPACE
Robertson,S.,Vatrapu,R.,&Medina,R.(2010).OfftheWallPoliticalDiscourse:FacebookUseinthe2008U.S.Presidential Election.InformationPolity,15(1-2),11-31.
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BUSINESS VALUE:REAL PROMOTER SCOREProduct Advocates are champions for products in general.
Product Enthusiasts are the users that aspire for the product category.
Brand Loyalists are champions of a particular brand.
Brand Tourists are in the early stages of brand consideration.
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Case Project #3: Sales & Revenue Forecasters
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Business Value: Sales and Revenue Predictive Models
(IEEEEDOC2014)(ICCSS2015)
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H&Msales,billionSEKperQuarter
Sales PredictedSales
Company DataSource TimePeriod SizeofDatasetApple Twitter 2007® October12,
2014500million+tweetscontaining“iPhone”
H&M Facebook January01,2009®October12,2014
~15millionFacebookevents
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Case Project #4: Social Media Crises (“Shitstorms”)
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CSR Crises: Bangladesh Factory Accidents & Volkswagen
IEEEEDOC2015 IEEEBigData2015
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Business Impact: Social Media Crisis
(ACMCABS2014) (IEEEEDOC2015)(IEEEBigData2015)
DuringCrisis:05-19February,2014Artefacts:AllData:WallbeginningtolastcollectedtimeActors:AllFacebookusersonCopenhagenZooPageActions:LIKE
Activity:PositiveAssociationSociologicalImportanceOrganizationalRelevance
Interpretation:ComputationalSocialScience:SetTheoryLIKEswereawayofexpressingculturalsolidarityandin-groupsupporttoaDanishinstitutionperceivedtobeunderundeservedout-groupcriticism
LikesonZoo’sPosts&CommentsUniqueActorsonZoo’sFBWall
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Case Project #5: EU Immigration Crisis
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EU Immigration Crisis
BDACourseProject:Jensen,Brock,Hody,Christensen&AlHumaidan
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• Big Social Data• Complete Corpus for Facebook• Multi-Channel, Multi-Language & Multi-Domain
• Analytics Software• Social Data Analytic Tool (SODATO)• Social Set Visualiser (SOSEVI)• Multi-Dimensional Text Analytics (MUDITA)• Social Business Predictor (SB-PRE)• Social Business Integrator (SB-INT)
• Research & Consultancy Reports
• Analytics Time Horizons• Fixed• Incremental• Continuous
• Analytics Mode• Historic• Near Real-Time• Real-Time
• Projects• Research
• Research Projects• Industrial PhD Projects
• Consultancy• Real Promoter Score• Strategic Management
Our Product Portfolio
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