small devices, big data and decision making
TRANSCRIPT
SMALLDEVICES,BIGDATAANDDECISIONMAKING
BigData,MetroTorontoConven;onCentreJune15,2016
MirceaBaldean
-DanielKahnemanAuthorofThinkingFastandSlow
“
”
Youcanlookatapain;ngallyouwant,butaskingaboutitsprovenienceisusuallyagoodguidewhetherthepain;ngisgenuineornot.
THEMAPAN
DTHETERRITORY
APrimerinDecisionMakingSmallDevices,BigDataUnderstandingData
123
APRIMERINDECISIONMAKING1
APRIM
ERBeforeWeStart
Wetendtobeop;mis;caboutthethingsthatareimportanttous...
APRIM
ERNoisevs.Signal:LawofSmallNumbers
Source:MichaelJ.Mauboussin,“CapitalIdeasRevisited-Part2,”MauboussinonStrategy,2005.
HeadsorTails?Short-termresultsshowmostlynoise:
50/50normalizedresults
APRIM
ERInsideViewvs.OutsideView
TheInsideviewisbasedonintui;on.
TheOutsideviewwill,atleast,givesusaballpark.
Whichonewillyouactupon?
APRIM
ERWhyisChangesoHardtoImplement?
Bigdataimplieschangeswithinmostins;tu;ons.Inthecontextofchangesorreforms,therewillbelosersandtherewillbewinners.Youcanknowaheadof;methatthepoten;alloserswillfightharderthanthewinners.
APRIM
ERWhyisChangesoHardtoImplement?
Losseshurtroughlytwiceasmuchasgainsfeelgood!
Source:A.TverskyandD.Kahneman,“ProspectTheory”,1979.Chart:SSgA,“TheExchange”,2011.
APRIM
ERWhyisChangesoHardtoImplement?
UnlikeGoogle,mostorganiza;onsdonotexperiment.Fewemployeesagreewitheachother,andinfacttheirviewscandifferbyasmuchas45-50%.Andmanyorganiza;onsdon'tevenknowthat!
Source:D.Kahneman,“Bigdata,intuiPonanddecision-makinginfinance”,SantaFeInsPtute,2015.
APRIM
ERInsideViewvs.OutsideView
ExperiencedoesnotbringConvergence,itincreasesConfidence.
Focusonbederinsightsand
fewerblindspots.
Source:D.Kahneman,“Bigdata,intuiPonanddecision-makinginfinance”,SantaFeInsPtute,2015.
APRIM
ERInsideViewvs.OutsideView
Intheknowledgeeconomymakingbederdecisionsiskey.
Therestisincreasinglybecoming
automatedanyways.
SMALLDEVICES,BIGDATA2
SMALLD
EVICES,BIGD
ATASmallDevices,BigData
“BigData”representstechniquesandtechnologiesthatmakehandlingdataat
extremescaleaffordable.
Source:ForresterResearch,Inc.ThePaSernsOfBigData,June2013.
SMALLD
EVICES,BIGD
ATASmallDevices,BigData
Mobileisallaboutopportunity.
Technologyandsocialconnec;vityshouldbeseamless.
SMALLD
EVICES,BIGD
ATAMobileWebvs.Apps
MobileWebistransac;onal.
Appshaveaspecificintent
andamuchricherexperience.
SMALLD
EVICES,BIGD
ATAMobileWebvs.Apps
Mobileismul;-dimensional:
Loca;on,Proximity,Immediacy
SMALLD
EVICES,BIGD
ATAValueofBigData
WebandMobilesta;s;csarebyfarthemostmeasurableandaccuratesourceofmarke;ngdataavailable.
SMALLD
EVICES,BIGD
ATAValueofBigData
However,themostcommonobstacletosuccessfulanaly;csisnottechnology,butrathercross-teamintegra;on.
SMALLD
EVICES,BIGD
ATAHandlingBigData
Datadoesn'tagewell.Don'tmakebaddecisionsusingbaddata.
SMALLD
EVICES,BIGD
ATAHandlingBigData
Investintracking
Leveragethedata
UNDERSTANDINGDATAATMEETVIBE3
UNDERSTAN
DINGD
ATAMeetVibeisaboutconnec;ngpeople
andideas.
Wegivethemaccesstotheworldaroundthem:people,places,rela;onships,interac;onsand
meaningfulconnec;ons.
Moreimportantly,peoplechooseMeetVibebecausetheytrustuswith
theirprivacy.
Tosimplifyscheduling,weprovideaconvenientandsafewayofviewingthe
available;mes.
UNDERSTAN
DINGD
ATA
UNDERSTAN
DINGD
ATAUnderstandingDataatMeetVibe
ProprietaryReal-TimeBIDashboard
AppStoreAnaly;cs
GoogleAnaly;cs
Proximity/Loca;on-basedCMSSocialAnaly;cs
EmailAnaly;cs
SalesForce.com
ProjectManagementAnaly;cs
Hub-and-SpokeModel
UNDERSTAN
DINGD
ATAUnderstandingDataatMeetVibe
ProprietaryReal-TimeBIDashboard
UNDERSTAN
DINGD
ATAUnderstandingDataatMeetVibe
Footprint,AppVersions
UNDERSTAN
DINGD
ATAUnderstandingDataatMeetVibe
ProximityInteracTons
Inanaly;cs,successisdependentuponaskingtherightques;ons.
Some;mesthinkingfastcanleadto
falseposi;ves.
THEEND
THANK#YO
U!
Thank#You!MirceaBaldeanmeetvibe.com/baldean@baldean
Surprise..NoTest!