MakingDataVisualAPracticalGuidetoUsingVisualizationforInsight

DanyelFisherandMiriahMeyer

MakingDataVisualbyDanyelFisherandMiriahMeyer

Copyright©2018MiriahMeyer,Microsoft.Allrightsreserved.

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Preface

Visualizationisavitaltoolforunderstandingandsharinginsightsarounddata.Therightvisualizationcanhelpexpressacoreideaoropenaspacetoexamination;itcangettheworldtalkingaboutadatasetorsharinganinsightFigureP-1.

FigureP-1.Visualizationscantakemanyforms,fromviewsthatsupportexploratoryanalysis(topleft),tothosethatprovidequickoverviewsinadashboard(bottom),toaninfographicaboutpopulartopics(top

right).

Visualizationsprovideadirectandtangiblerepresentationofdata.Theyallowpeopletoconfirmhypothesesandgaininsights.Whenincorporatedintothedataanalysisprocessearlyandoften,visualizationscanevenfundamentallyalterthequestionsthatsomeoneisasking.

Creatingeffectivevisualizationsishard.Notbecauseadatasetrequiresanexotic

andbespokevisualrepresentation—formanyproblems,standardstatisticalchartswillsuffice.Andnotbecausecreatingavisualizationrequirescodingexpertiseinanunfamiliarprogramminglanguage—off-the-shelftoolslikeExcel,Tableau,andRareampleenoughtosuffice.

Rather,creatingeffectivevisualizationsisdifficultbecausetheproblemsthatarebestaddressedbyvisualizationareoftencomplexandill-formed.Thetaskoffiguringoutwhatattributesofadatasetareimportantisoftenconflatedwithfiguringoutwhattypeofvisualizationtouse.Pickingacharttypetorepresentspecificattributesinadatasetiscomparativelyeasy.Decidingonwhichdataattributeswillhelpansweraquestion,however,isacomplex,poorlydefined,anduser-drivenprocessthatcanrequireseveralroundsofvisualizationandexplorationtoresolve.Inthisbook,wefocusontheprocessofgoingfromhigh-levelquestionstowell-defineddataanalysistasks,andonhowtoincorporatevisualizationsalongthewaytoclarifyunderstandingandgaininsights.

WhoIsThisBookFor?Thisbookisforpeoplewhohaveaccesstodataand,perhaps,asuiteofcomputationaltoolsbutwhoarelessthansurehowtoturnthatdataintovisualinsights.Wefindthatmanydatasciencebooksassumethatyoucanfigureouthowtovisualizethedataoncecollected,andvisualizationbooksassumethatyoualreadyhaveawell-definedquestion,readytobevisualized.If,likeus,youwouldliketoaddresstheseassumptions,thenthisbookisforyou.

Thisbookdoesnotcoverhowtocleanandmanagedataindetailorhowtowritevisualizationcode.Therearealreadygreatbooksonthesetopics(and,whenrelevant,wepointtosomeofthem).Rather,thisbookspeakstowhythoseprocessesareimportant.Similarly,thisbookdoesnotaddresshowtochooseabeautifulcolormaporselectatypeface.Instead,welayoutaframeworkforhowtothinkaboutdatagiventhepossibilitiesandconstraintsofvisualexploration.Ourgoalistoshowhowtoeffectivelyusevisualizationstomakesenseofdata.

WhoAreWe?Theauthorsofthisbookhaveacombinedthreedecadesofexperienceinmakingsenseofdatathroughdesigningandusingvisualizations.Wehaveworkedwithdatafromabroadrangeoffields:biologyandurbantransportation,businessintelligenceandscientificvisualization,debuggingcodeandbuildingmaps.Wehaveworkedwithanalystsfromavarietyoforganizations,fromsmall,academicsciencelabstoteamsofdataanalystsembeddedinlargecompanies.Someoftheprojectswehaveworkedonhaveresultedinsophisticated,bespokevisualizationsystemsdesignedcollaborativelywithdomainspecialists,andatothertimeswehavepointedpeopletooff-the-shelfvisualizationtoolsafterafewconversations.Wehavetaughtuniversityclassesinvisualizationandhavegivenlecturesandtutorials.Allinall,wehavevisualizedhundredsofdatasets.

Wehavefoundthatourknowledgeaboutvisualizationtechniques,solutions,andsystemsshapesthewaythatwethinkandreasonaboutdata.Visualizationisfundamentallyaboutpresentingdatainawaythatelicitshumanreasoning,makesroomforindividualinterpretations,andsupportsexploration.Wehelpour

collaboratorsmaketheirquestionsanddatareflectthesevalues.Theprocesswelayoutinthisbookdescribesourmethodfordoingthis.

OverviewofChaptersChapter1illustratestheprocessofmakingsensewithvisualizationsthroughaquickexample,exposingtherolethatavisualrepresentationcanplayindatadiscovery.

Chapter2startstogetintodetails.Itdiscussesamechanismtohelpnarrowaquestionfromabroadtaskintosomethingthatcanbeaddressedwithaniterativevisualizationprocess.Forexample,thebroadquestion“Whoarethebestmoviedirectors?”doesnotnecessarilysuggestaspecificvisualization—but“Findmoviedirectorswhodirectedtop-grossingmoviesusinganIMDBdataset”canleadmoredirectlytoananswerbywayofavisualizationortwo.Thisprocesscreatesanoperationalizedquestion,onethatconsistsofparticulartasksthatcanbedirectlyaddressedwithdata.

Thisprocessofnarrowingaquestiondowntoactionabletasksrequiresinputfrommultiplestakeholders.Chapter3laysoutaniterativesetofstepsforgettingtotheoperationalization,whichwecalldatacounseling.Thesestepsincludefindingtherightpeopletotalkto,askingeffectivequestions,andrapidlyexploringthedatathroughincreasinglysophisticatedprototypes.

Thenumericalnitty-grittyofthebookfollows.Chapter4discussestypesandrelationsofdata,anddefinestermslikedimensions,measures,categorical,andquantitative.Chapter5thenorganizescommonvisualizationtypesbythetaskstheyfulfillandthedatatheyuse.Then,Chapter6explorespowerfulvisualizationtechniquesthatusemultipleviewsandinteractiontosupportanalysisoflarge,complexdatasets.Thesethreechaptersaremeanttoprovideanoverviewofsomeofthemosteffectiveandcommonlyusedideasforsupportingsensemakingwithvisualizations,andareframedusingtheoperationalizationanddatacounselingprocesstohelpguidedecision-makingaboutwhichvisualizationstochoose.

Withthisunderstandingofgettingtoinsight—fromquestionstodatatovisualizations—theremainderofthebookillustratestwoexamplesofcarryingoutthesesteps.ThecasestudyinChapter7describesthecreationofabusiness

intelligencedashboardincollaborationwithateamofdevelopersandanalystsatMicrosoft.TheoneinChapter8drawsfromscience,presentinganexamplewithateamofscientistswhoworkwithbiologicaldata.Thesecasestudiesillustratetheflexibilityoftheprocesslaidoutinthisbook,aswellasthediversetypesofoutcomesthatarepossible.

Thisbookisaccompaniedbyacompanionwebsite.FromthissiteyoucandownloadthecodeandinteractiveversionsofthevisualizationspresentedinChapters5and6,aswellasothercodeandsupplementarymaterial.

AcknowledgmentsDanyelandMiriahwouldliketothankDanyel’scolleaguesatMicrosoft,includingStevenDrucker,MaryCzerwinski,andSueDumais,fortheirenthusiasmandencouragement.WealsothankMiriah’sresearchgroup,theVisualizationDesignLabattheUniversityofUtah,includingAlexLex,forhelpingtheprojecttoevolveandprovidingfeedbackonideas.Wearebothdeeplyappreciativeofourworkorganizationsforsupportingthetimeandenergyrequiredbyprojectslikethisone,andforseeingthevalueincommunicatingourresearchbroadly.AtO’ReillyMedia,wethankMikeLoukidesforencouragingustostartthiswork,andShannonCuttandRachelRoumeliotisforguidingitfromstarttofinish.

PortionsofthisworkwerepresentedattheIPAMWorkshoponCultureAnalyticsof2016,MicrosoftDataInsightsSummitof2016,UniversityofIllinoisUrbana-ChampaignHCISeminarSeriesof2016,UniversityofBritishColumbiaHCISeminarSeriesof2016,WomeninDataScienceConferenceatStanfordUniversityin2017,andO’ReillyVelocityConferencein2017.Ourthankstotheorganizersofthoseevents,andtoparticipantswhogaveuscriticalfeedbackandhelpedclarifyourthoughts.

EarlyfeedbackontheoperationalizationprocesscamefromChristianCantonofMicrosoft.MichaelTwidaleandAndreaThomer,bothofUIUC,helpedinformthediscussionofdatacounselingwiththeirinsightsonhowreferencelibrariansdotheirwork.

WearegratefultoDominikMortizandKanit“Ham”WongsuphasawatforputtingtogethertheexamplesusedinChapters5and6.Theirwork,aswellas

thatoftherestoftheVega-Liteteam,ishelpingshapethefutureofdatavisualization.WealsothankAlexBigelowforsupplyingtheskateboadingvisualizationfigureinthePreface.

WethankJacquelineRichardsforherreviewanddiscussionofthecasestudyinChapter7.Similarly,thecollaborationwithAngelaDePaceandhergroupattheHarvardMedicalSchoolforthecasestudyinChapter8providedvaluableandrichinsightsintotheprocessofdesigningvisualizationsfordomainexperts.Theprojectsdescribedinbothofthesechaptersweredeeplyinfluentialinourworkpractices.

Ourtechnicalreviewers,MichaelFreeman,JeffHeer,andJerryOverton,helpedclarifyandstrengthentheargumentswemake.

Finally,MiriahthanksBrianPriceforhisendlesssupportandencouragement,withoutwhichshecouldneverdothethingsshedoes.

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Chapter1.GettingtoanEffectiveVisualization

Choosingordesigningagoodvisualizationisrarelyastraightforwardprocess.Itistemptingtobelievethatthereisonebeautifulvisualizationthatwillshowallthecriticalaspectsofadataset.Thattherightvisualrepresentationwillrevealhiddeninsights.Thataperfect,simple,andelegantvisualization—perhapsjustalinechartorawell-chosenscatterplot—willshowpreciselywhattheimportantvariablewasandhowitvariedinpreciselythewaytoillustrateacriticallesson.

Thisisoftentheimpressionthatwe,atleast,areleftwithafterreadingdatasciencecasestudies.Butinourexperience,thisdoesnotmatchtherealityofvisualdataanalysis.Ittakeshardwork,andtrialanderror,togettoaninsightfulvisualization.Westartbythinkingaboutwhatwewanttoknow,andwerefinefuzzyquestionsintoactionable,concretetasks.Weclean,reshape,andrestructurethedataintoformsthatwecanputintoavisualization.Weworkaroundlimitationsinthedata,andwetrytounderstandwhattheuserwantstolearn.Wehavetoconsiderwhichvisualrepresentationstouseandwhatinteractionmechanismstosupport.Alongtheway,wefindothervariablesthattellusmoreaboutthedatasetandthathelpclarifyourthinking.Andnosinglevisualizationiseverquiteabletoshowalloftheimportantaspectsofourdataatonce—therejustarenotenoughvisualencodingchannels.

Designingeffectivevisualizationspresentsaparadox.Ontheonehand,visualizationsareintendedtohelpuserslearnaboutpartsoftheirdatathattheydon’tknowabout.Ontheotherhand,themoreweknowabouttheusers’needsandthecontextoftheirdata,thebetterwecandesignavisualizationtoservethem.Theprocessdescribedinthisbookembracesthisparadox:itleveragestheknowledgeusershaveoftheirdatasets,thecontextthedatalivesin,andthewaysitwascollected—includingitslikelyflaws,challenges,anderrors—inordertofigureouttheaspectsofitthatmatter.

Putanotherway,thisbookisaboutthepathfrom“Ihavesomedata…”to“Weknowthisbecauseoftheseclear,concise,andinsightfulvisualizations.”We

believethatcreatingeffectivevisualizationsisitselfaprocessofexplorationanddiscovery.Agoodvisualizationdesignrequiresadeepunderstandingoftheproblem,data,andusers.

GettingtoInsightWemostoftenworkwithotherpeoplethathaveadatasettheyaretryingtomakesenseof.Theprocessofdesigningavisualizationusuallystartswhenpeoplewalkintoouroffice.

CLIENT:IhavesomedatathatI’dliketovisualize.HowshouldIdrawit?

Theclientseemstoexpectustopullavisualizationofftheshelf,tosculptthatperfectvisualization.Wealmostalwaysfrustratethembyaskingwhattheyhopetosee.

Q:Whatisitaboutthedatathatyouwouldliketovisualize?

CLIENT:Iwanttoseehowprofitableourstoresare.

Q:Whatinyourdataindicatesastorebeingprofitable?

CLIENT:Itmeansthatthestorehaslotsofsalesofhigh-profititems.

Q:Howdoesprofitvarybystore?

Andsoon.

Bytheendofthisprocess,weoftenfindthattheclientsdonothaveavisualizationproblem,butanoperationalizationone.Theirstrugglestochooseavisualizationstemfromalackofclarityaboutwhichattributesofthedataaremostimportantandhowthoseattributesrelatetooneanother.Oncetheycandescribehowthedataattributesrelatetothequestiontheyaretryingtoanswer,findinganappropriatevisualizationbecomesmucheasier.

Wehavelearnedovertheyearsthatdesigningeffectivevisualizationstomakesenseofdataisnotanart—itisasystematicandrepeatableprocess.Wehavesystematizedthisprocessintowhatwebelievearereproducibleandclearsteps.

Thisprocesstracksourunderstandingoffourcomponents:

Data

Whatdataisavailable,andwhatdoesitmean?Whatdoesthedatalooklike,andwhatareitsimportantaspects?Wherediditcomefrom,andwhywasitoriginallycollected?

Tasks

Whatneedstohappenwiththedata?Whatarethelow-levelquestionsandtasksthatwillsupporthigh-levelgoals?

Stakeholders

Whoisinvolvedwiththedata,theproblem,andthegoals?Whatcantheysayabouttheproblemtohelpdesignaneffectivevisualization?Whowillviewthefinalvisualization,andwhatsortsofthingsdoweexpectthemtolearnfromit?Whatdomainknowledgedotheybringtothetable?Whatanswerswouldtheyfindsatisfying?

Visualization

Howdoestheunderstandingofdata,tasks,andstakeholderscometogether?Whatrepresentationsofthisdatawillfulfillthetasksfortheusers?

Regardlessofthevisualizationoutcome,thisprocesswillalmostcertainlyleadtonewdiscoveriesandinsights.Thesediscoverieshelptoinformtheoperationalization,buttheywillalsolikelysteertheprocessdownnewandunexpectedpaths.Theguidanceandframeworkinthisbookaremeanttohelpidentifyopportunitiesfordiscoveringnewknowledgeandtomakeanotherwisemessyprocessabitmorestructured.

Hotmap:MakingDecisionswithDataAsanexampleofhowvisualizationscanhelpyoutobetterunderstandaproblem,andhelpanorganizationmakedecisions,wecanlookbackto2006.Microsoftwasrollingoutitsnewmappingtool,VirtualEarth,azoomableworldmap.TheteambehindVirtualEarthhadlotsofquestionsabouthowtheiruserswereusingthisnewtool,sotheycollectedusagedata.

Theusagedatawasbasedontraditionaltelemetry:ithadgreatinformationon

whatcitiesweremostviewed,howmanyviewerswerein“street”modeversus“photograph”mode,andeveninformationaboutviewers’displays.Theyinstrumentedsearchandnavigation,andtheycollectedcountsforthenumberoftimesthatuserslookedatcertainsentinelregions.AndbecauseVirtualEarthwasbuiltontopofasetofprogressivelyhigher-resolutionimagetiles,theteamwasalsoarchivingserverlogsthattrackedhowoftenindividualtilesweredownloaded.

Interviewswithteammemberssuggestedthattheydidnothaveanintuitivenotionofhowtheirtoolwasbeingused.Inconversation,oneteammemberarguedthatpeoplewerelikelytolookattheirownhomes;anotherthoughtthattheoverheadphotographywouldmostlybeusedovermountains.Thegoalswerevaried:theyincludedseeingwhethertheuserexperiencewaswellbalancedacrossuserneedsanddecidinghowandwheretoinvestinfutureroundsofphotography.

WeaddressedthesequestionswithavisualizationtoolcalledHotmap.Figure1-1showsascreencapturefromthevisualizationtool,focusingonthecentralUnitedStates.Hotmapusesaheatmapencodingofthetileaccessvalues.Thisisavisualizationtechniquethatusesacolormaptoencodetheaccessvaluesatthegeospatiallocationsofthetiles.Coloredspotsonthemapareplaceswheremoreusershaveaccessedimagetiles.Thecolormapisalogarithmiccolorscale,sobrightspotshavemanymoreaccessesthandimones.

Figure1-1.Hotmap,lookingatthecentralUnitedStates.ThewhiteboxsurroundsananomalyinSouthDakota.

Someofthebrightestareascorrespondtomajorpopulationcenters—ChicagoandMinneapolisontheright,DenverandSaltLakeCityinthemiddle,andWestCoastcitiesontheleft.Nearthecenter,though,isananomalousshape:abrightspotwherenobigcityexists.Thereisastarshapearoundthebrightspot,andanarcofbrightcolorsnearby.ThespotisinasparselypopulatedbitofSouthDakota—therewasnoobviousreasontotheteamwhyusersmightzoominthere.

Thatpointis,however,veryclosetothecenterofamapofthecontinentalUS.Infact,theteamlearnedthatthecenterofthestarcorrespondstothecenterofthedefaultplacementofthemapinmanybrowsers.Thebrightspotwiththestarmostlikelycorrespondstousersslidingaroundafterinadvertentlyzoomingin,tryingtofigureoutwherethey’velanded;thearcseemstocorrespondtovariationsinmonitorproportions.

Asaresultofthisusabilitychallenge,manymappingtools—includingBingMaps(thesuccessorproducttoVirtualEarth)—nolongerofferazoomslider,whichkeepsusersfromaccidentallyzoomingallthewayinonasingleclick.

Asecondscreencapture,showninFigure1-2,revealsabrightspotoffthecoastofGhana.Thisspotexhibitsthesamestarpatterncreatedbyusersscrollingaroundtotrytofigureoutwhatpartofthemaptheyareviewing.Thisspotislikelyonlybrightbecauseitisat0degreeslatitude,0degreeslongitude,apointthatGIStoolsrunintooften.Whilecomputersmightfind(0,0)appealing,itisunlikelythatthereismuchthereforthetypicalVirtualEarthusertofindinteresting.1

Figure1-2.Hotmap,lookingatthemaporigin(0,0).

Thissecondbrightspotinspiredahuntforbugs.TheteamrapidlylearnedthatVirtualEarth’ssearchfacilitywouldsometimesfail,andinsteadofreturninganerrormessage,typosanderroneoussearcheswouldsometimesredirecttheuserto(0,0).Interestingly,thebughadbeenonthebacklogforsometimebecausetheteamhaddecidedthatitwasnotlikelytosurfaceoften.Seeingthisimagemadeitclearthatsomeusersreallywerebeingconfusedbytheerror,sotheteamprioritizedthebug.

AlthoughtheVirtualEarthteamstartedoutusingtheHotmapvisualizationexpectingtofindoutabouthowusersinteractedwithmaps,theygleanedmuchmorethanjustacharacterizationofusagepatterns.Likemany—darewesaymost?—newvisualizations,themostinterestinginsightswerethosethattheviewerswerenotanticipatingtofind.2

WhereVisualizationIsUsefulIsvisualizationthesilverbullettohelpusmakesenseofdata?Notalways.Therearetwoquestionstoconsidertohelpyoudecideifyourdataanalysisproblemisagoodcandidateforavisualizationsolution.

First,couldtheanalysistasksbesupportedwithanalgorithm?Acrisptasksuchas“IwanttoknowthetotalnumberofuserswholookedatSeattle”suggeststhatanalgorithm,statisticaltest,orevenatableofnumbersmightbethebestwaytoanswerthequestion.Ontheotherhand,“Howdousersexplorethemap?”ismuchfuzzier.Fuzzytasksaregreatcandidatesforavisualizationsolutionbecausetheyrequireyoutolookatthedatafromdifferentanglesandperspectives,andtobeabletomakedecisionsandinferencesbasedonyourownknowledgeandunderstanding.

Thesecondquestiontoconsideris“Isallthenecessaryinformationcontainedinthedataset?”Ifthereisinformationabouttheproblemthatisnotinthedatasetwhichrequiresanexperttointerpretthedatathatisthere,thenvisualizationisagreatsolution.Goingbacktoourfuzzyquestionaboutexploringamap,wecanimaginethatitisunlikelythattherewillbeanexplicitattributeinthedatathatclassifiesauser’sexplorationstyle.Instead,answeringthisquestionrequiressomeonetointerpretotheraspectsofthedatatobringknowledgetobearaboutwhataspectsofthedataimplyanexplorationstyle.Again,visualizationenablesthissortofflexibleanduser-centricanalysis.

Forallbutthecrispestquestionsaboutexplicitlymeasuredphenomena,visualizationisprobablyagoodtooltothrowataproblem.Inourexperience,wehavealmostnevercomeupagainstaproblemthatcannotbenefitfromsomeamountofvisualization.

FurtherReadingTheHotmapprojectisdiscussedin:

Fisher,Danyel."Hotmap:LookingatGeographicAttention.”IEEETransactionsonVisualizationandComputerGraphics13(2007):1184–1191.

Fisher,Danyel."TheImpactofHotmap.”TheInfovis2009DiscoveryExhibition.Redmond,WA:Microsoft,2009.

Somanydatasetshavereferencesto(0,0)thatGISpractitionersrefertothatlocationas“nullisland.”

See“FurtherReading”forotherstoriesofhowHotmaphasbeenused.

1

2

Chapter2.FromQuestionstoTasks

Allvisualizationbeginswithaquestionaboutdata.Ananalystwantstoknowsomethingaboutaphenomenonintheworld,orwantstosharetheirknowledgeaboutitwithsomeoneelse.Shebelievesthephenomenontheywishtoexamineisrepresentedsomehowinthedata.

Thechallengeinthisprocessisthatthequestiontheanalystwishestoaddresscanseemfarfromthedata.Theanalystmightbeworkingonabroadgoal:say,“Arehigh-salaryemployeesmoreproductivethanlesswell-paidones?”Thisleadstoaprocessofmakingthequestionmeasurable.Whatdoestheanalystmeanbyhigh-salary,andproductive?Whatvisualizationorsetofvisualizationswoulddemonstratetherelationshipbetweenthesevariables?

Theprocessofbreakingdownthesequestionsintosomethingthatcanactuallybecomputedfromthedataisiterative,exploratory,andsometimessurprising.Thischapterdescribeshowtorefinehigh-levelquestionsintospecific,data-driventasks.Theoutcomeofthatprocessisasetofconcisedesignrequirementsforavisualizationtoolthatsupportsfindinganswerstothosequestions.

Thegeneralconceptofrefiningquestionsintotasksappearsacrossallofthesciences.Inmanyfields,theprocessiscalledoperationalization,andreferstotheprocessofreducingacomplexsetoffactorstoasinglemetric.Thefieldofvisualizationtakesonthatgoalmorebroadly:ratherthanattemptingtoidentifyasinglemetric,theanalystinsteadtriestolookmoreholisticallyacrossthedatatogetausable,actionableanswer.Arrivingatthatanswermightinvolveexploringmultipleattributes,andusinganumberofviewsthatallowtheideastocometogether.Thus,operationalizationinthecontextofvisualizationistheprocessofidentifyingtaskstobeperformedoverthedatasetthatareareasonableapproximationofthehigh-levelquestionofinterest.

Avisualizationisnottheinevitableoutcomeofoperationalization.Exploringthedatamightshowthatthegoalisbestachievedwithastatisticalanalysisorwithmachinelearning.Similarly,theoutcomeoftheprocessmightshowthata

clusteranalysisacrossmultipleattributesismoreusefulthanaplot.Wefindthatmoreoftenthannot,visualizationisavitalcomponentofgettingtoasuccessfuloperationalization.

Thischapteremphasizesthedataaspectsofthisprocess.Thenextchaptermovestothehumansideoftheprocess:howtogettheinformationnecessarytoeffectivelyoperationalizethehigh-levelquestions.Laterchaptersthenlookathowtotranslatetheoperationalizedquestionsintospecificvisualizations.

Example:IdentifyingGoodMovieDirectorsToguidetheprocessthroughoperationalization,thischapterexaminesanexemplarquestion:“Whoarethebestmoviedirectors?”

Nonspecificquestionslikethisarehowmanydataexplorationsstart.Answeringaquestionlikethisrequiresamuchmorespecifictaskthatcanbepreciselyaddressedwithadataset.Beforewecanbemorespecific,wefirstneedtotakeastepback:whoneedstoknowtheanswertothisquestion?Theusecasemightbeafilmstudenttryingtoassertthathisdissertationisaboutoneofthemostinfluentialdirectors,orahiringmanagerlookingtohireadirectorforanupcomingproject,orajournalistputtingtogetherasplashyarticlethatwillfeatureatoplist.

Eachoftheseusersneedssuggestsdifferentinterpretationsforthenotionofbestdirector.Thefilmstudentislookingforawaytoquantifyanddefendanotionofinfluence,whereasthehiringmanagermightwanttolimitthemselftopeopleworkingtodaywhoarelessaccomplishedandthusmoreaffordable.Forthisexample,though,theuserwillbeajournalistwhoisputtingtogetheranarticleaboutanewmovieandwantstoincludealistofthebestdirectors.

Thegoalofoperationalizationistorefineandclarifythequestionuntiltheanalystcanforgeanexplicitlinkbetweenthedatathattheycanfindandthequestionstheywouldliketoanswer.Forthisexample,thedatasetathandcontainsalistofmoviesratedbythefilm-aficionadocommunity.Eachmovieisassociatedwithadirector,anumberofraters,andanaverageratingscore.

ANOTEONTHEDATA

ThedatasetusedforthischapteriscomprisedoftwoofIMDB’sdownloadablelists,directors.listandratings.list.ThereisacopyoftheJupyternotebookthatparsesthemintocleanerCSVsonthebook’scompanionwebsite.Thescriptcleansthedatatoremoveentriesthatthedatabasereferstoasnotbeingmovies,suchasvideogamesandTVshows.TheanalysisandvisualizationsinthischapterarecarriedoutinPythonandrecordedinasecondJupyternotebookavailableatthesamesite.

Withbothdataandahigh-levelquestioninhand,thevisualizationworkcanbegin.Dataaloneisnotenoughtodictateasetofdesignrequirementsforconstructingavisualization.Whatismissinghereisatranslationofthehigh-levelquestion“Whoarethebestmoviedirectors?”intoasetofconcretetasksoverthedata.

Thechoiceofdatasetandoperationalizationisfundamentallyaspecificperspectiveonaproblem;theystandinforwhattheanalystwishestounderstand.Inthisexample,thereareotherwaystoframetheinquiryandothertypesofdatathatcouldbecollected.Thisisalargepartofwhyvisualizationissoimportantforansweringquestionslikethese:itallowsananalyst’sexperienceandknowledgetolayerdirectlyontopofthedatathatisultimatelyshown.Theanalyst’sskillsandexperienceallowthemtomakeinferencesaboutthemoreabstractquestionstheyarereallyinterestedin.

MakingaQuestionConcreteTheprocessofoperationalizationwindsitswayfromageneralgoalorabroadquestiontospecifictasks,andthentovisualizationsthatsupportthosespecifictasksbasedonconcretedata.

Toachievethis,theanalystsearchesforproxies.Proxiesarepartialandimperfectrepresentationsoftheabstractthingthattheanalystisreallyinterestedin.Forexample,highmovieratingsmaybeareasonableproxyforbestinourmovieexample.Selectingandinterpretingproxiesrequiresjudgmentandexpertisetoassesshowwell,andwithwhatsortsoflimitations,theyrepresenttheabstractconcept.

Inoperationalization,therearetwoimportanttypesofproxies:

Aproxytaskisalower-leveltaskthatstandsinfortheoriginal.Theresultofaproxytaskreflectsontheanswertotheoriginalquestion,buttheproxytaskitselfismorecloselyrelatedtothedata;itcanbeaccomplishedwithquantitativetools,suchasavisualizationorastatisticalanalysis.

Aproxyvalueisanattributeinthedatathatstandsinforamoreabstractconcept.Thiscanbeanexistingattribute,oritcanbederivedfromthedata.

Operationalizingaquestionoftenresultsinmorequestions,whichrequirefurtherarticulationofproxies.Onestepinthisprocessistofindplaceswhereaquestionisunderspecifiedordoesnotdirectlyreferencethedataonhand,inordertoidentifywhereproxiesarenecessary.

Collaborationwithstakeholderscruciallyinformstheprocessofoperationalizingquestions.Ithelpstolearnwhatdataisavailableandhowtheresultswillbeused.Interviewshelptoidentifythequestionsandgoalsofthestakeholderswithrespecttothedataandtofurtherunderstandwhatdataisavailableorcanbemadeavailable.Throughouttheprocess,ananalysttranslatesquestionsandgoalsintoadescriptionoftheproblemthatisamenabletoadatasolution.InterviewtechniquesandprototypingarediscussedinmoredetailinChapter3.

Inthisbookweadvocateanapproachofsystematicoperationalizationinordertobolsterexplicitacknowledgment,validation,andsupportoftherangeofpossibleproxydecisionsforaquestion.Thissystematicapproachleavesopenfuturepossibilitiesandprovidesguidanceformakingdownstreamdecisions.Thestarttothisprocessisgettingtounderstandthequestionandwhatisavailableinthedata—andappreciatingthegapsbetweenthem.

Thischapterbothdescribesandillustratestheoperationalizationprocess.Itusesthemoviedirectorexampletoshowhowtorefineaquestionintodetailed,specifictasks.Itdiscussesthefourcomponentsthatweusetodescribeanoperationalizedtask.

AConcreteMovieQuestionTheexamplestartedwiththehigh-levelquestion“Whoarethebestdirectors?”Thedatasetisalistofdirectorsandalistofmovies.Thefirsttaskisto

operationalizebestdirector.Asaroughdefinition,agooddirectorhasdirectedmanygoodmovies.Butmanygoodmoviesisalsoill-defined,andthusaproxyforgoodmoviemightinturnbebasedonitsratingonIMDB.

Thesedecisionsreplacedonebitofambiguitywiththreemore.Howmanyofthesebestdirectorsneedtoappearintheresults?WhatcountsasgoodIMDBratings,andwhataremanyofthem?Forthatmatter,aquickglanceattheIMDBdatarevealsthatthereareshortfilms,TVepisodes,videogames,andsoon—sowhatcountsasamovie?

Itispossibletochoosethemeasuresarbitrarily:“MorethanfivemovieswithIMDBratingsgreaterthan9.8,”or“averagemovieratinghigherthan8.2,”or“nomovieswitharatinglessthan5.”Whileitisnotuncommontomakethesesortsofdecisionsbasedonroughknowledgeofthedata,orevenbasedonchoosingnice,roundnumbers,lookingattheactualdataisimportant.Thetop-rateditemsonIMDBmightturnouttohaveveryhighratingsbutonlyonereview.Greatdirectorsmightdirectafewstinkers,sojustlookingattheaverageratingmightturnouttobeapoorchoice.Theonlywaytolearnwhatthedatasaysistostartdiggingintoit.

Choosingaproxyallowstheanalysttosanity-checktheirdecisions;itcanbevaluabletodothisiterativelyateachstep,checkingboththequalityofthedataandoftheproxy.

Aquickglanceatthefirstfivedataitemsinthedatasetrevealsnon-mainstreammovies(Table2-1).Thealphabeticalfirstmovieinthedatasetiscalled#1,withatotalof12raters;thesecondisthesimilarlyobscure#1SerialKiller.Sincethescenariotargetsageneralaudience,itshouldprobablyfocusonmoviesthatmostpeoplearelikelytoknow.Adifferentscenariocouldsuggestverydifferentproxies.

Table2-1.Aquickglanceatthefirstdataitemsinthemoviedataset(whichissortedalphabetically)revealsthattherecouldbemovieswithpositiveratingsthathaveveryfewraters,implyinganobscure(butdecent)movie.

ID Raters Score Title Director

0 12 6.4 #1(2005) Breen,James(V)

1 35 6.0 #1SerialKiller(2013) Yung,Stanley(I)

2 5 5.8 #137(2011) Elliott,Frances

3 11 7.4 #140Characters:ADocumentaryAboutTwitter(2… Beasley,Bryan(I)

4 23 6.7 #30(2013) Wilde,Timothy

… … … … …

Thedecisiontostickwithmainstreammoviessuggestsaneedforaproxyforpopularity.Onechoicecouldbethenumberofratingsforeachmovie.Byplottingthedistributionofthenumberofratingsbymovies(Figure2-1),weseethatthevastmajorityofmoviesinthedatasetactuallyhaveveryfewratings.

Figure2-1.Distributionofratings.Thishistogramshowsthecountofnumberofratingsperfilm.Almostallthefilmshavefewratings,withaverylongtail.

Thisfirstplotshowsthatthenumberofratingsisheavilyskewed.Onewaytomakethisdistributionmoreinterpretableistoplotitonalogarithmicscale.InFigure2-2,thedatahasbeenbucketed;afilmwith1,000ratingsnowappearsinthebucketforlog (1000)=3.Takingthelogofthenumberofratingssmoothsthedistribution,moreeffectivelyshowingitsshape.

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Figure2-2.Distributionofthelogarithm(base10)ofthenumberofratings.Thepeakisunder2:mostfilmshaveunder100ratings.

Wecanalsocomputesomebasicsummarizingstatisticsaboutthenumberofratings:themedianmovieinthedatasethasjust26ratingswhilethe75thpercentileisat132ratings. Bylookingupthenumberofratingsforasampleofblockbusters,wenotethatmoviesthatanyonecannameoffhandhavetensofthousandsofratings.Theseareusefulobservations;perhapsitwouldbevaluabletotrimtoaslimmersetofmoviestoensurethatmostareonesthatareasonablenumberofpeoplehaveseen.

Wewanttochooseanumber,though,that’sfairtogoodmovies,eveniftheyarenotverypopular—inthiscase,wepick,somewhatarbitrarily,themost-rated25%ofmovies.Thisamountstoaround70,000filmswithmorethan132ratings.

Wenextpivotandlookatthedistributionofratingsfortheslimmed-downsetofmovies,showninFigure2-3.Thisdistributionshowsadistinctcurvewithaclearpeakandnoticeabledrop-off:ratingsabove7.5seemdifferentfromlowerratings.(Thisdistributionhasamedianscoreof6.6,anda75thpercentileof7.4.)

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Figure2-3.Distributionofscore.Thishistogramshowsthecountofratings,bybucket.Almostallratingsareextremelylow,withaverygradualtail.

Steppingbackfromourdiveintothedata,wecanobservethatwehaveproceededsomedistancealongtheoperationalization.Wehavedefinedagooddirectoranddecidedthatitisbasedontheirmovies;wehavefocusedonmoviesandchosenasetthatarepopularenoughtobepartoftheanalysis.Buttherearestillunansweredquestions:Howwillwerankdirectorsagainsteachother?Whatmakesfora“best”director?

Asystematicapproachtooperationalizationallowsananalysttoseethefullrangeofdecisionsandhelpsinpullingtogetherthesetofproxiesthatcaninformafinalanswer.Ultimately,aninteractivevisualizationtoolcanenableexplorationofmultipleproxiestoallowforasetofjustified,andvalidated,answers.Forourrunningexample,wewillcontinuewiththeoperationalizationafterdescribingaframeworkformakingdecisionsexplicitthroughouttheprocess.

BreakingDownaTask

Throughouttheoperationalization,weneedtoidentifywhereinaquestionortaskthereisaneedforamorerefinedproxy.Doingsosystematicallycanmakeiteasiertovalidatethosedecisions,aswellastoproducearoadmapoftheprocess.Thisallowstheanalysttoeffectivelyrevisitdecisionsonceabetterunderstandingoftheproblemisgained.

Ananalystcanrefineataskbyfirstbreakingitdownintofourspecificcomponents.Identifyingthesecomponentsandhowtheydoordonotdirectlyreferencethedatabecomesatemplateforchoosingmorespecifictasks.Thecomponentsare:

Objects

Thingsoreventsthatexistintheworld:inourexample,adirectorandamoviearebothobjects.Inothercontexts,objectsmightbeauserorasaleofasingleitem.Whenataskisspecificenough,eachobjectwillbesomethingthatcanberepresentedinorcomputedfrom,thedata.Fairlyoften,whenthetaskisatitsmostspecific,anobjectwillcorrespondtoasinglerowinadatabase.

Measures

Theoutcomevariablesthatwillbemeasuredfortheobjects.Qualityofadirector,happinessofauser,andsalesofastoreareallmeasures.Inasufficientlyspecifictask,themeasureiseitheranexistingattributeinthedatasetoronethatcanbedirectlycomputedfromthedata.Ameasureissometimesaggregatedacrossmanyitemsofdata.Inourexample,anumberofmoviesareaggregatedtogethertogetascoreforasingledirector.

Groupings(orpartitions)

Attributesorcharacteristicsofthedatathatseparatethedataitemsintogroups.Forexample,groupingsmightincludestoreregion(westernversuseastern),startdateofplayers,whetherusershavepurchasedanupgrade,orsalesbyyear.Inaspecifictask,partitionsareattributesoftheobjectsorcanbecalculateddirectlyfromthoseattributes.Whenthevisualizationiscreated,partitionswilloftenmanifestasgroupings,separationsacrosscharts,orfilters.

Actions

Wordsthatarticulatethespecificthingbeingdonewiththedata,suchascompare,identify,characterize,etc.Actionsguidetheprocessofchoosingappropriatevisualizations.

Theactionisusefulforidentifyingtheothercomponents.Takethistask:Comparetheamountofmoneyspentin-gamebyplayerswhoplaymorehoursversusthosewhoplayfewerhours.Theactioniscompare.Whatiscompared?Theplayers(theobject).Whatisitaboutplayersthatwewanttocompare?Themoneyspent(themeasure).Finally,thereisaspecificpartitionontheobjects.Theywillbebrokenintotwogroups:thosethatplaymanyhoursandthosethatplayfewhours.

Thefollowingcomponentsaretheheartofaniterativeprocess:

1. Refinethequestionintooneormoretasksthat,individuallyortogether,addressthegeneralquestion.

2. Foreachtask:

a. Identifythecomponentsofthetask.

b. Lookforambiguouscomponents—namely,componentsthatarenotdirectlyaddressablebythedataset.

c. Foreachambiguouscomponent,defineaproxybycreatinganewquestionthataddressesthecomponent,andreturntostep1withthosequestions.

d. Iftherearenoambiguouscomponentsthenthetaskisdeemedactionable,andthuscanbeaddressedwithavisualizationorothercomputationaltechnique.

Next,we’llexplicatesomeofthequestionsfromthemovieexampletoillustratehowthecomponentsworkinpractice,beginningwithExample2-1.

Example2-1.Breakingdownthetasktofindgooddirectors

Task:Identifythetopdirectorswhohavedirectedmanygood,popularmovies

Action:Identify

Object:Director

Measure:Numberofgood,popularmovies

Grouping:Filteroutnon-movies

Identifyingtopdirectorsimpliesthatthere’sameaningfulsortonthedirectorssothatthetopcanbefound(Example2-2).Thus,wecanfurtherrefinetheactiontospecifyanordering.Also,ourfirstlookatthedatashowedthatmanymoviesareunpopular,whichimpliedagroupingtofilteroutunpopularmovies.

Example2-2.Refinedtaskforgooddirectors

Task:Rankorderofdirectorsbythosewhohavedirectedmanygood,popularmovies

Action:Rankorder

Object:Director

Measure:Numberofgoodmovies

Grouping:Filteroutnon-moviesandunpopularmovies

Filteringoutunpopularmoviesisasubtask(Example2-3),whichweaddressedwithahistogramofthenumberofratingsformovies.Thevisualizationofthedistributionallowedustodetermineagoodcutpointforpopularversusunpopular—namely,popularmovieswerethoseinthetop25%ofmovieswiththehighestnumberofratings.

Example2-3.Subtaskforfilteringunpopularmovies

Task:Filteroutmovieswithsignificantlyfewerratings

Action:Filter

Object:Movie

Measure:Numberofratings

Grouping:Separateintomostpopularandleastpopularmovies

ThissubtaskcanbebroughtbackintoExample2-2asaproxyforunpopularmovies.

However,westillhavesomeworktodoonExample2-2:themeasurenumberofgoodmoviesisill-definedwithrespecttothedata.Weneedtorefinethiscomponentbydevelopingaproxyforagoodmovie.Oncewedothat,wecanthenexaminewhatitmeanstohavedirectedmanyofthem.

Theseproxiesrequirefurtherelaboration.Howmanyhigh-scoringmoviesarerequiredfromdirectors?Dolow-scoringmoviescountagainstthem?Thisprocessofidentifyingreasonableproxiesisofteniterative.Forexample,inexploringandvalidatingaproxywiththedata,itmightbecomeobviousthattheeffectsoffilteringbythenumberofratingswasamistakenapproach.

Atthispoint,wecanrecognizethatweneedaproxymeasureforgood(Example2-4).Thereareavarietyofproxiesthatwecantryhere,withvariousvisualizations.Theprocesscontinuesonward.

Example2-4.Subtaskforgoodmovies

Task:Quantify“agoodmovie”

Action:Quantify

Object:Movie

Measure:Goodness

Grouping:None

Breakingdownataskintocomponentshelpsinguidingrefinementofataskintoonethatcanbeaddressedwiththedata.Themostdirectwaytodosoistoconsiderthequestion“Aretheobject,measure,andgroupingeachdirectlydescribedinthedata?”Foreachofthesethreecomponents,isitclearwhichaspectsofthedataareimportantorhowtoderivewhatweneedfromthedata?Ifnot,repeattheprocessofformulatingasubquestioninordertoderiveamorespecificanswer.

Let’stakealookataverydifferentexample—thistime,fromagameplaymetricsscenario(Example2-5).

Example2-5.Exemplartaskforanalyzingagame

Task:Comparetheamountofmoneyspentin-gamebyplayerswhoplaymorehoursversusthosewhoplayfewerhours.

Action:Compare

Object:Players

Measure:Moneyspent

Grouping:Playerswhoplaymanyhours;playerswhoplayfewhours

InExample2-5,thepartitiondividesbetweenmanyandfewhours.Thiscomponentneedstoberefinedfurther,whichleadstoanewquestion:“Inthegame,howmanyis‘many’hoursforaplayer?”Theanalystmighttakeaseriesofsteps.Theymightlookatthedistributionofhoursplayed,ortheymightchoosetofilteroutplayerswhohaveplayedzerohourorthosewhohaven’tmadeitpastthetutorial,ortheymightlookatothermetricsthatareimportanttothegame.Thesestepswouldhelptheanalystfigureoutgoodproxiesformanyandfewhours.

WhenTasksLeadtoNewQuestionsTherearefourbroadcategoriesofnewlinesofinquirythatcanemergefrom

refiningaquestion.First,asinthemovieexample,therefinementprocessoftenrevealsthatanewanalysisisneededtoanswerthesequestions.

Second,operationalizingcanalsoleadinnewdirections.Intheprocessofexploringwhothebestdirectorsare,theanalystmightnoticethatsomedirectorssticktoasinglegenre;theymightdecidethatthisanalysismightbeinterestingdividedacrossmultiplegenres.TheymightalsonoticethatbothIMDBandRottenTomatoeshavescoresonmovies,andwanttoseehowtheseresultsvarybasedonRottenTomatoesscoresinsteadofIMDB.

Third,thedataitselfcanleadtonewquestionstoo.Inexploratorydataanalysis(EDA),forexample,thedataanalystdiscoversnewquestionsbasedonthedata.Theprocessoflookingatthedatatoaddresssomeofthesequestionsgeneratesincidentalvisualizations—oddpatterns,outliers,orsurprisingcorrelationsthatareworthlookingintofurther.

Finally,doingsomeanalysisoftenleadstodoingaroundofdatacleaning.Whiledatacleaningislargelyoutofthescopeofthisbook,oddoutliersandsurprisingtrendsare,asoftenasnot,theresultofdirtydata.

ReturningtotheExample:ExploringDifferentDefinitionsThereareseveraldifferentpossibledefinitionsofbestdirector.

Hereisone:thebestdirectorhasthemostmovieswithmorethan134ratings.Table2-2showsthetopscorers.ThemostprolificdirectorsinourdatasetareChuckJonesandFritzFeleng(whodirectedclassicLooneyTunesanimations),WilliamHanna(whodirectedTomandJerryandotherclassicHanna-Barberacartoons),andGeorgeMéliès(anearlyinventorofspecialeffectsandshorts).

Table2-2.Topfivedirectorsbynumberoffilmsoverthreshold

Director Avg.raters Avg.score Count Totalraters

Jones,Chuck(I) 719 7.4 148 106,397

Freleng,Fritz 402 7.2 141 56,730

Hanna,William(I) 591 7.5 119 70,315

Méliès,Georges 717 6.1 114 81,769

White,Jules(I) 235 7.1 102 23,969

GeorgesMélièshas526filmsonhisIMDBpage;only114,however,madeitoverthethresholdofraters.Thehugenumberoffilmsisexplainedbythefactthatthefilmsareshorts—morefamiliarontelevisionnow,butoncealsoshownintheaters.Thisshouldbeanopportunitytodomoredatacleaningtojoininanothertablethatwilltelluswhetherafilmisashortornot,andfilterthoseout.IMDBhasafilmdurationdatatable;inatypicalanalysisprocess,thenextstepwouldbetomergeinthistable,addinganewproxyforwhatmakesforashortfilm.

Wemightexploreotherdefinitionsofbestdirectors.Forexample,thebestdirectorsmightmakethemoviesthatpeoplewanttoratethemost.Table2-3isalistofthedirectorswhosemovieshave,intotal,themostratings.

Table2-3.Topfivedirectorsbytotalnumberofratingsacrossallmovies

Director Avg.raters Avg.score Count Totalraters

Spielberg,Steven 245,717 7.2 36 8,845,795

Nolan,Christopher(I) 778,737 8.2 11 8,566,104

Tarantino,Quentin 526,689 7.8 13 6,846,955

Jackson,Peter(I) 371,219 7.6 16 5,939,505

Scorsese,Martin(I) 144,823 7.5 41 5,937,725

Thislistmakessense.Theseareveryfamousnameswhohavedirectedveryfamiliarmovies.

Differentproxiesyielddifferentresults.Orderingbytheaveragescoreforallmoviesbyasingledirectormightbeonewaytofindtheverybestdirectors.AsseeninTable2-4,thefirstonthislistisadirectorwhohasonlyonemovieover

thethreshold:aMongolianmoviefrom2016with624ratersandanaveragescoreof9.7.Thismeasureofpopularityreturnsaverydifferentsetofresultsthanthepreviousmeasure:tenthousandtimesmorepeopleratedQuentinTarantino’smoviesthanUranchimegUrtnasan’swork.

Table2-4.Topfivedirectorsbyaveragescore

Director Avg.raters Avg.score Count Totalraters

Urtnasan,Uranchimeg 624 9.7 1 624

Miller,George(XXXVII) 394 9.6 2 787

Chowdhury,AmitabhReza 14,628 96 1 14,628

Biebert,Aaron 12,040 9.6 1 1,204

Arsyn,Ken 619 9.5 6 3,712

Butcanthequalityofadirectorbemeasuredbasedonjustoneortwomovies?Eachstepofdataexplorationleadstoanotherstepofrefiningthequestion.Isitmoreimportanttohavemanyraters,ahighaveragescore,orahighminimumscore?

Thechoiceofmetricsleadstoverydifferentoutcomes.Aslighttweakdetermineswhetheryoufinddirectorsofanimatedcartoons,blockbusterdirectors,oraverydiversesetofinternationaldirectors.

HowSpecificDoestheProcessGet?Thisprocessofrefinementleadstoascaryscenario.InDisney’sFantasia,intheSorcerer’sApprenticesequence,MickeyMouseattemptstostopanenchantedbroombychoppingitinhalfandinsteadproducestwohalf-sizeenchantedbrooms.Willouranalysissubtasksforevermultiply?

Theoperationalizationprocessisaniterativeoneandtheendpointisnotpreciselydefined.Theanswertothequestionofhowfartogois,simply,farenough.Theprocessisdonewhenthetaskisdirectlyactionable,usingthedataathand.Theanalystknowshowtodescribetheobjects,measures,andgroupingsintermsofthedata—wheretofindit,howtocompute,andhowtoaggregateit.Atthispoint,theyknowwhatthequestionwilllooklikeandtheyknowwhattheycandotogettheanswer.

Anactionabletaskmeansthatitispossibletoactonitsresult.Thatactionmight

betopresentausefulresulttoadecisionmakerortoproceedtoanextstepinadifferentresult.Ananswerisactionablewhenitnolongerneedsfurtherworktomakesenseofit.

Low-levelobjectsarereadytobeinterpretedfromthedata.Sometimestheycanbereaddirectlyoffthedatatable,butmoreoftenitismoreindirect;theanalystmayneedtocarryouttransformationsonthedata,whethermathematicaltransformationsordatabasejoins.Forinstance,inthemovieexample,theobjectisthedirector;theproxyforthedirectoristheresultofaggregatingmultiplemoviestogether.Partitionsandmeasuresatthelowestlevelwillresolvetoconcretemanipulationsoftheobjects.

Theprocessendswhenallthetoolsneededtoansweraquestionareinplace—whetherasanumber,avisualization,orevenasaninteractionacrossmultiplevisualizationsrepresentingmultipleproxies.Theanalystmightdecidethattherightcutoffformanyhoursofgameplayissixhours—anumber—orthehoursplayedbythetop10%ofplayers—aformula—orabovethelogicalbreakpoint,whichmightberepresentedbyadistribution.Theseresultsgetpropagatedbackintoanyothertasksthatdependonthem.

MakingUseofResultsThisprocessofpropagatingresultsbackintohigher-levelquestionsisflexibile.Sometimesthelow-levelquestiondoesnothaveanexactanswerbutinsteadresolvesinitsownvisualizationorinteraction.Thatvisualizationmighthelpananalystinmakingadecision,butitmightalsoimplyparametersonthedata.Forexample,thejournalistmightrealizethatthereareseveralpossiblecutoffsfordefiningwhatitmeanstobeagoodmovie.Ratherthansimplypickingaspecificthreshold,ananalystmightinsteadpropagateamechanismfordynamicallydeterminingcutoffsintohigher-leveltasks.Seeingavariablepropagatedlikethiscanbeacuethataninteractivevisualization—ratherthanastaticimage—mightbehelpful.

Visualizationisalsoimportantforsupportingtheoperationalizationprocess,eveniftheendresultisnotaninteractivevisualization.Inthemovieexample,visualizationhelpedustounderstandthenatureanddistributionofthedata.Visualizationcanbemoreprominentwithmorecomplexanalysistasks.Ifthe

analystwantedtocompareratingsagainstpopularity,itwouldbedifficulttodisplaythatonalist;iftheywantedtoexplorehypothesesabouthowthepopularityofdirectorschangesovertime,morevisualrepresentationswouldhelpthemexplorethedata.

Conclusion:AWell-OperationalizedTaskAwell-operationalizedtask,relativetotheunderlyingdata,fulfillsthefollowingcriteria:

Canbecomputedbasedonthedata

Makesspecificreferencetotheattributesofthedata

Hasatraceablepathfromthehigh-levelabstractquestionstoasetofconcrete,actionabletasks

Awell-operationalizedtaskisafirststeptowardcreatingavisualization.Chapter4beginstodescribethewaysinwhichtheobjects,measures,andpartitionscanbeshapedintoaspectsofavisualization.Chapters5and6constructvisualizationsbasedonthem.

Writtenoutindetail,thisprocesscanseemtedious,butinpractice,itisabbreviatedandsimplified.Therearetwoimportantusesforthissystematization.First,theprocessofexplicitlylookingatcomponentscanhelpuntangleknottyproblems,decomposingplaceswheretheanalysthasmadeassumptionsaboutthedata.ExplainingpreciselywhythenumberofIMDBratingsisaproxyforpopularityforcestheanalysttoexplorewhetheritisagoodchoice—and,perhaps,torevisethatchoicelater.

Theprocessalsohelpsguidequestionsandinterviews.Chapter3explainshowtocarryoutoperationalizationwithdomainexperts.Recognizingtheneedtomakedecisionsaboutproxieshelpsguidetheseconversations.Everydatasethassubtleties;itcanbefartooeasytoslipdownrabbitholesofcomplications.Beingsystematicabouttheoperationalizationcanhelpfocusourconversationswithexperts,onlyintroducingcomplicationswhenneeded.

FurtherReadingTheprocessoutlinedhereissimilar—andinmanywaysparallel—totheGoal,Question,Metric(GQM)processfoundinthesoftwareengineeringspace.GQMrefinesfromageneralgoaltoaspecificmetric,usuallyorientedaroundprocessimprovementsothattheconsumercanhaveasinglenumberthathelpsthemknowwhethertheyaresucceedinginimprovingthatprocess.

Ourprocessismoreexploratoryandoftencomesearlierinthecycle.AGQManalysismightchooseagoallike“improveuserretention.”Incontrast,exploratoryoperationalizationmightstartwithaquestionlike“Douserscomebacktooursite?”withtheawarenessthattheproblemismultifacetedandcomplex,andmightrequireavarietyofdifferentmetricstodescribe.FormoreonGQM,see:

Basili,Victor,GianluigiCaldiera,andDieterRombach.“TheGoalQuestionMetricApproach.”EncyclopediaofSoftwareEngineering.NewYork:Wiley,1994.

Thedatavisualizationfieldhasspentagreatdealofefforttryingtounderstandthetasksthatcanbeaccomplishedinavisualization.AmarandStasko,forexample,explorealow-levelanalysisoftaskscarriedoutonaspecificvisualization.Attheotherendofthespectrum,BrehmerandMunznerexplorehigh-leveltasksforvisualization,startingwithcomparingpresentationandexploration:

Amar,RobertandJohnStasko.“AKnowledgeTask-BasedFrameworkfortheDesignandEvaluationofInformationVisualizations.”ProceedingsoftheIEEESymposiumonInformationVisualization(2004):143–150.

Brehmer,MathewandTamaraMunzner.“AMulti-LevelTypologyofAbstractVisualizationTasks.”IEEETransactionsonVisualizationandComputerGraphics19(2013):2376–2385.

Medianandpercentilearewaysofcharacterizingadistributionofnumbers.Ifoneweretosortthenumbers,the75thpercentilewouldbe75%ofthewaydown1

inthelist.Themedianwouldbeatthehalfwaypoint.