Small Devices, Big Data and Decision Making

SMALLDEVICES,BIGDATAANDDECISIONMAKING

BigData,MetroTorontoConven;onCentreJune15,2016

MirceaBaldean

-DanielKahnemanAuthorofThinkingFastandSlow

“

”

Youcanlookatapain;ngallyouwant,butaskingaboutitsprovenienceisusuallyagoodguidewhetherthepain;ngisgenuineornot.

THEMAPAN

DTHETERRITORY

APrimerinDecisionMakingSmallDevices,BigDataUnderstandingData

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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!