数据质量良好实践指南 Data Quality Good Practice Guide

NPLReportNumberMS63

DataQualityGoodPracticeGuide

JoãoGregórioandPaulDuncanwithcontributionsfrom

MoulhamAlsuleman,MichaelChrubasik,Bartlomiej

Cieszynski,VivekDesai,Jean‑LaurentHippolyte,PadminiKrishnadas,KeithLines,RussellMiller,NinaPerić,and

HarveyWhelan

March2026

NPLManagementLtd

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NationalPhysicalLaboratory(NPL)

DataQualityGoodPracticeGuide

byJoãoGregórioandPaulDuncan

withcontributionsfrom(alphabetically):

MoulhamAlsuleman,MichaelChrubasik,BartlomiejCieszynski,VivekDesai,

Jean‑LaurentHippolyte,PadminiKrishnadas,KeithLines,RussellMiller,NinaPerić,andHarveyWhelan

WearetheUK’sNationalMetrologyInstitute(NMI),

aworld-leadingcentreofexcellencethatprovidescutting-edgemeasurementscience,engineeringandtechnologythat

underpinsprosperityandqualityoflifeintheUK.

NPLManagementLtd

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©NPLManagementLimited,2026

ISSN1754-2960

/10.47120/npl.MS63

NationalPhysicalLaboratory

HamptonRoad,Teddington,Middlesex,TW110LW

ThisworkwasfundedbytheUKGovernment’sDepartmentforScience,Innovation&TechnologythroughtheUK’sNationalMeasurementSystemprogrammes.

Extractsfromthisreportmaybereproducedprovidedthesourceisacknowledgedandtheextractisnottakenoutofcontext.

NPLManagementLtdExecutiveSummary

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ExecutiveSummary

Dataqualityunderpinseffectivedecision-makingacrossmultipledomainsofapplication.Asorganisationsincreasinglyrelyondigitalsystems,fromanalyticalpipelinesanddigitaltwinstoregulatoryreportingplatforms,theconsequencesofpoor-qualitydatabecomemorepronounced.Thisguideprovidesgoodpracticeguidanceforunderstandingand

assessingdataquality,groundedinestablishedinternationalstandardsandinformedbypracticalapplicationsacrossdiversedomains.

TheguideadoptstheISO25012dataqualitymodelasitsfoundation,whichdefines15dataqualitydimensionsorganisedalongaspectrumfrominherenttosystem-dependent.Thisframeworkisextendedwithfouradditionaldimensions(governance,usefulness,

quantity,andsemantics)identifiedthroughrecentresearchasincreasinglyrelevanttomoderndataapplications.Theresultingsetof19dimensionsprovidescomprehensivecoverageofthecontemporarydataqualitylandscapewhilemaintainingthestructuredclassificationthatsupportsitssystematicassessment.

Beyonddimensiondefinitions,theguidesurveysexistingdataqualityframeworks,

comparinggeneralisablestandardssuchasISO25012andISO8000withdomain-specificframeworkssuchasALCOA+forlifesciencesandBCBS239forfinancialservices.A

comparativeanalysisshowsthatcoredimensions,suchasaccuracy,completeness,consistency,andcurrentness,aresteadilyrepresentedacrossframeworks,while

dimensionssuchassemantics,quantity,andgovernancearelessrepresented.Thisanalysisprovidesguidanceonframeworkselection,helpingorganisationsidentifyapproachessuitedtotheirregulatorycontextsandoperationalrequirements.

Acentralthemeofthisguideisthatdataqualityisinherentlycontextual.The19-dimensionstructureoffersacommonvocabularyfordiscussingdataquality,butthepractical

interpretationofeachdimensionvariesdependingontheapplicationenvironment.Six

casestudiesdrawnfrommanufacturing,healthcare,maritime,andenvironmental

monitoringdomainsarepresentedinthisguidetoillustratethisprinciple.Thedataqualitydimensionoftraceability,forinstance,takesondifferentmeaningsacrossthesecase

studies:intheassessmentofsimulationcredibilityitisrelatedtotheprovenanceof

documentation;insupplychainintegrationitrelatestotheintegrityoftransferredfiles;and

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inenvironmentalmonitoringitisattachedtoregulatorycompliance.Recognisingthesecontextualdependenciesisessentialforimplementingdataqualitypracticesthatarealignedwithestablishedframeworksandtailoredtospecificoperationalneeds.

Futuredevelopmentswillexploretheformalisationofdimensionoperationalisationthroughstructuredrepresentationsofhowdimensionsareinterpretedacrossdifferentdomainsofapplication.Thelong-termvisionisalivingframework,basedonsemanticwebstandards,thatcapturesvalidatedinterpretationscontributedbypractitioners,creatingashared

resourcethatbridgesabstractdefinitionsandconcreteassessmentpractices.

Thisguideisintendedfordatapractitioners,qualitymanagers,researchers,andanyoneresponsibleforensuringthatdataarefitforpurpose.Itprovidesbothconceptual

groundworkandpracticalexamplesthatsupportthedesignandimplementationofeffectivedataqualityprogrammes.

NPLManagementLtdContents

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Contents

ExecutiveSummary 3

Contents 5

PurposeandScopeofthisGuide 6

AnIntroductiontoDataQuality 8

UncertaintyinMeasurementData 10

DataQualityDimensions 13

DataQualityFrameworks 25

CaseStudies 32

Conclusions 47

FutureWork 49

Acknowledgements 51

References 52

NPLManagementLtdPurposeandScopeofthisGuide

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PurposeandScopeofthisGuide

ThisGoodPracticeGuideprovidescomprehensiveguidanceforassessingandmanagingdataqualityacrossdiverseapplicationsanddomains.Itisintendedfordataprofessionals,qualityassurancepersonnel,managers,andanyoneresponsibleforensuringdataqualityintheirorganisations.

Trustworthydata-drivendecision-making,regulatorycompliance,andoperational

effectivenessalldependonsystematicapproachestodataquality.AlthoughtheguidancepresentedinthisdocumentwasdevelopedwithNPL'sroleastheUK'sNational

MeasurementInstituteinmind,theprinciplesandapproachespresentedareapplicable

acrossvariousindustriesandorganisationalcontexts.Theframeworkbuildsupon

establishedinternationalstandards,particularlyISO25012duetoitsgeneralisablenature,whileincorporatingadditionaldimensionsandcontemporarybestpracticesrelevantto

moderndigitalenvironments.

Asbusinessesacrossallsectorsevolvetowardsdata-drivenoperations,thevolume,

velocity,andcomplexityofdatacontinuetogrowexponentially.Theincreasing

dependenceondigitalsystems,particularlyartificialintelligence(AI)andmachinelearning(ML)forinformeddecision-making,makeshigh-qualitydataessentialnotonlyfor

operationalefficiencybutalsoforincreasingtrustinthesesystems.

Systematicdataqualitymanagementoffersdistinctbenefitsacrossdifferentstakeholdergroups.Datacreatorsgainenhancedconfidenceintheiroutputs,reducedriskoferrorsandrework,andimprovedefficiencyincollectionandprocessingworkflows.Datausersbenefitfromgreatertrustindata-drivendecisionsthroughtransparentqualityindicators,improvedabilitytoassessfitness-for-purpose,andenhancedinteroperabilitysupportingFAIRdataprinciples.Fororganisationsmorebroadly,comprehensivedataquality

practicesstrengthenreputationandcredibility,improveoperationalefficiency,andensurecompliancewithinternationalstandardsandregulatoryrequirements.

Againstthisbackdrop,theprimaryobjectivesofthisguidearetopresentastandardisedframeworkofdataqualitydimensionsorganisedintodistinctcategories,toprovide

practicalguidanceforimplementingdataqualityassessmentprogrammes,todemonstrate

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theirapplicationsthroughreal-worldexamples,andtofacilitateeffectivecommunicationaboutdataqualityacrossdifferentdomainsandstakeholders.

Coveragespansthecompletedatalifecycle,fromcollectionandprocessingthrough

analysis,storage,anddissemination.Theframeworkappliestoallformsofdata,includingstructuredandunstructureddatasets,measurementandexperimentaldata,businessandoperationalrecords,researchandscientificdata,anddigitalassetsandmetadata.

Technicalscopeextendstobothtraditionalandemergingtechnologies,includingcloud-basedsystems,artificialintelligenceapplications,andInternetofThingsdeployments.

Ratherthanprescribingspecifictechnicalimplementationsorsoftwaresolutions,thisguidefocusesonassessmentandmanagementpractices.Organisationsshouldadaptthe

frameworktotheirspecificoperationalcontexts,regulatoryrequirements,andstakeholderneedswhilemaintainingalignmentwiththefundamentalprinciplespresented.

NPLManagementLtdAnIntroductiontoDataQuality

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AnIntroductiontoDataQuality

Theincreasingdependenceondataforinformingdecision-makingprocessesbringswithitagrowingneedtoensurethatdataarefitforpurpose[

1

].Thisreliancespansvirtually

everysector,frompharmaceuticalmanufacturingandhealthcaretoengineeringand

education[

2

].Asorganisationsintegrateincreasinglycomplexdigitalsystemsintotheiroperations,theconsequencesofpoordataqualitybecomemorepronounced[

3

].For

instance,modernanalyticalpipelines,digitaltwins,andpredictivemaintenancesystemsrelyheavilyonaccurate,timely,andwell-structureddatatofunctionasintended[

4

][

5

].

EvencontemporaryAI-enabledtools–althoughfarfromtheonlyexample–highlightthesameunderlyingprinciple:theperformanceandreliabilityofthesesystemsare

fundamentallyconstrainedbythequalityofthedatatheyconsume[

6

].Thismakesitan

opportunemomenttoemphasisethebroaderpointthatrobustdatagovernanceand

qualitymanagementareessential,regardlessofwhetherthesystemisamachinelearningpipeline,aprocesscontrolsystem,oraregulatorydecisionsupportplatform[

7

].

Addressingthesechallengesrequiresaclearunderstandingofwhatdataqualitymeansandhowitcanbeevaluated.ThisguideadoptsthedefinitionestablishedbyISO25012,wheredataqualityrepresentsthedegreetowhichdatapossessthecharacteristics

necessarytosatisfystatedandimpliedneedswhenusedunderspecifiedconditions[

8

].Itisdefinedbyasetofdimensionsthatcanbequalitativelyorquantitativelyassessedto

describethequalityofdatasetsandotherdatastructures.Commondimensionsincludeaccuracy,completeness,traceability,andtimeliness,thoughtheterminologyusedto

describethemiscomplex,leadingtoongoingstandardisationefforts[

9

].Inmeasurementscience,dataqualityencompassesnotonlytheaccuracyandprecisionofnumerical

valuesbutalsothecompletenessofmetadata,theconsistencyofformats,andthereliabilityofdataprovenance[

10

][

11

].

Dataqualityisalsoinherentlycontextual,meaningthatdatasuitableforoneapplication

maybeinadequateforanother[

12

].Thiscontext-dependencyisparticularlyimportantinmetrology,wherethesamemeasurementdatamightbeusedforroutinequalitycontrol,

regulatorycompliance,scientificresearch,andcommercialtransactionseachwithdifferentqualityrequirements.Despiteeffortstoestablishstandardisedmetricsandassessment

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procedures,differentindustrieshaveuniquedataneeds,andinconsistenciesindefiningandevaluatingqualitypersistacrosssectors.Thislackofstandardisationhasresultedinconsiderablediversityinexistingterminology,whichcreatescommunicationchallenges

whenaddressingdataqualityacrossdomains.Thisguidedrawsuponrecentworkthat

mapsthediverseterminologyusedacrosssectorsintoaunifiedframework,enablingdatapractitionerstocommunicateaboutdataqualityusingacommonvocabulary[

1

].

Implementingsuchaunifiedunderstandinginpracticetypicallyreliesondataquality

frameworks.Thesearestructuredmethodologiesusedtoassess,manage,andimprovethequalityofdataTheycanbebuiltuponexistingstandardsandregulationsorspecificallydesignedformoretailoredapplications[

2

].Theseframeworksareessentialfor

organisationstomanagetheirdataanddemonstrateevidenceofgooddataquality

practiceswhencommunicatingwithstakeholders.Frameworksrangefromgeneralised

approachesapplicableacrossmultipledomainsandusecasestospecialisedframeworksaddressingspecificregulatoryandoperationalrequirementswithinsectors[

7

].While

specialisedframeworkstypicallycoverfeweroveralldimensionsthangeneralisable

frameworks,theyoftenincludedimensionsabsentfrombroaderframeworks,providing

targetedinsightscrucialfortheirspecificapplications[

13

].Whiletheseframeworksprovidevaluablestructureforassessingdataqualitybroadly,measurementdatapossessuniquecharacteristicsthatwarrantspecificconsideration.

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UncertaintyinMeasurementData

Assessingandquantifyingthequalityofdatarequiresanestimationofthe"confidence"intheprocessthatgeneratedthatdata.Inmetrology,thescienceofmeasurement,this

practiceisalreadywellestablishedandprovidesausefulfoundationfordataquality

assessment.Metrologyfollowsmatureprinciples,includingtheGuidetotheExpressionofUncertaintyinMeasurement(GUM)andtheInternationalVocabularyofMetrology(VIM),whichofferstructuredframeworksforunderstandingandexpressingmeasurementquality[

10

][

11

].Thesedistinctivecharacteristicsmeanthatdataqualityassessmentin

measurementcontextsmustconsidermetrologicalprinciplesalongsidestandarddataqualitydimensions.

Allmeasurementresultsincludeassociateduncertaintyinformationthatquantifiesthe

reliabilityofthereportedvalue,andthisuncertaintymustbeclearlydocumentedand

propagatedthroughdataprocessingworkflows.

Figure1

illustratestherelationship

betweenthemeasuredvalue,themeasurand(the"true"valuebeingmeasured),theerror,andtheuncertainty.Measurementresultsmustalsodemonstratetraceabilitytorecognised

Figure1:Distinctionbetweenerroranduncertainty.Erroristhedifferencebetweenthemeasuredvalueandthemeasurand,whereasuncertaintyquantifiestheconfidenceinthemeasurementresult.

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nationalmeasurementstandardsthroughanunbrokenchainofcalibrations,andthistraceabilityinformationformsessentialmetadatafordataqualityassessment.

Measurementresultsarealsocriticallyinfluencedbyenvironmentalconditionssuchas

temperature,humidity,andpressure,makingcompletedocumentationoftheseconditionsnecessaryforcorrectinterpretation.Thecalibrationstatusofequipmentintroducesan

additionaltemporalaspect,intheformofacalibrationtimestamp,thatcanaffect

measurementquality.

Figure2

showshowthesemetadataelementsmovethroughatypicalmeasurementsystemandhighlightstheconsequencesofincomplete

documentation.

Understandinghowuncertaintypropagatesthroughameasurementsystemprovides

insightintohowconfidenceaccumulates,ordiminishes,acrossconnectedprocesses.Inmetrology,everymeasuredquantityistreatedashavinganassociateduncertainty,and

mathematicalmodelsdescribehowtheseuncertaintiescombinetoinfluenceoutputs.

WithintheGUM,themodelfunction,sensitivitycoefficientsandcovariancestructuremakeexplicithoweachinput’sdistributioninfluencestheoutput,providingastructuredbasisforassessingthe"fitnessforpurpose"ofdataqualitydimensions.Inthisway,uncertainty

informationfunctionsasstructuredmetadata,comparabletootherdataqualitydimensionssuchasaccuracyandcompleteness.Uncertaintybudgetsthereforeencompassnotonlyexpandeduncertaintiesbutalsotheunderlyingstandarduncertainties,correlation

assumptionsandmodelspecificationsinmachine-readableform.

Asmeasurementsystemsincreasinglybecomepartofautomatedworkflows,digitaltwins,andAIprocesses,metrologicalprinciplescanbeembeddedwithinthesesystemsto

ensurethatuncertaintyisnotonlycalculatedbutalsopropagatedthroughcomplexdigitalstructures.Conceptssuchasuncertaintyquantificationanduncertaintypropagation

thereforeprovideafoundationforexpressingconfidenceacrossdataqualitydimensions,extendingtherigourofmetrologytomodern,data-centric,andinterconnected

measurementenvironments.

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Figure2:Measurementdatalifecycleshowingtheimpactofmetadatapreservationondataqualityinameasurementcontext.Ashaft

diametermeasurementshowshowuncertaintyisevaluatedfromTypeA(statisticalanalysis

ofrepeatedmeasurements)andTypeB

(calibrationcertificate)components.Atthe

datarecordingstage,measurementmetadatamustbecaptured:theuncertaintystatement

withcoveragefactor,traceabilityreference,

environmentalconditions,measurement

procedure,andoperatordetails.Thegood

practicepath(left)preservesthismetadata,

enablingconfidentconformancedecisionswithapplieddecisionrulesandmaintaininga

completeaudittrail.Thepoorpracticepath(right)recordsonlythemeasuredvalue,

renderingthedataunabletosupportreliablequalitydecisionsordemonstrate

measurementvalidity.

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DataQualityDimensions

Dataqualitydimensionsarespecific,quantifiablecharacteristicsusedtoassessand

describethequalityofdatawithinagivencontext.Eachdimensionrepresentsadistinctaspectofdataqualitythatcanbeevaluatedindependently,yetcollectivelytheyprovideaviewofoveralldatafitnessforpurpose.The19dimensionspresentedinthisguideare

organisedintothreecategories–Inherent,contextual,andsystem-dependent–basedontheirnatureandtheirrelationshiptodatausagecontexts[

1

].Thiscategoricalstructure

reflectshowqualitycharacteristicsmanifestdifferentlydependingonwhethertheyarisefromthedataitself,itscontextofuse,orthesystemsthatmanageit,helpingpractitionersidentifywhichdimensionsaremostrelevanttotheirspecificassessmentneeds.

Figure3

showsthishierarchicalstructure,withtheouterringdisplayingthediverseterminology

usedacrossdifferentframeworksanddomainsthatmaptoeachstandardiseddimension.Thesedimensionshavealsobeenformalisedintoastructuredtaxonomy,availableasan

Figure3:Dataqualitydimensionsclassifiedintothreecategoriesbasedontheir

characteristics:inherentdimensions(red);contextualdimensions(blue);andsystem-dependentdimensions(green).

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interactiveresource

1

wherepractitionerscanexploredefinitions,examples,and

relationshipsbetweencategories.Thissectionprovidesdetaileddefinitionsandguidanceforeachdimension,enablingpractitionerstounderstand,assess,andimprovespecificaspectsoftheirdataqualitysystematically.

Inherentdimensions

Inherentdataqualityrepresentsthedegreetowhichthedata'squalitycharacteristicshavetheintrinsicpotentialtomeetstatedandimpliedneedswhenusedunderspecified

conditions.Errorsinthedatacanpropagatethroughworkflows,causingsignificant

problemswhendataareusedforreporting,analytics,anddecision-making.Addressing

inherentdataqualityrequiresrobustvalidationagainstestablishedrules,appropriate

governanceoversight,andthoroughdocumentation.Byexaminingdimensionssuchas

accuracy,completeness,consistency,credibility,andcurrentness,organisationscanverifythattheirdataintegritymeasuresenabledatatomeetqualityexpectations.

Accuracy

Accuracyisthedegreetowhichdatahaveattributesthatcorrectlyrepresentthetruevalueoftheintendedattributeofaconceptoreventinaspecificcontextofuse[

1

][

8

].In

measurementscience,accuracyreflectshowcloselyameasuredorrecordedvalue

correspondstothetrueorreferencevalue.Thisdimensionisfundamentaltodataquality,asinaccuratedatacanleadtoflawedanalyses,incorrectconclusions,andpoordecisions.

Example(s)andpossibleinterpretation(s):

Accuracy:Atemperaturesensorusedinanenvironmentalchambercanbe

assessedforaccuracybycomparingitsreadingsagainstacalibratedreferencethermometertraceabletonationalstandards.Ifthesensorreads23.2°Cwhilethereferencethermometer(withknownuncertainty)reads23.0°C±0.1°C,the

accuracyofthesensorcanbequantified.

1InteractiveDataQualityTaxonomy:

https://dq-taxonomy-7aa56b.gitlab-docs.npl.co.uk/

.

Thisisaliveresourceunderactivedevelopment;ifinaccessible,pleasecontactthecorrespondingauthor.

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Completeness

Completenessisthedegreetowhichsubjectdataassociatedwithanentityhavevaluesforallexpectedattributesandrelatedentityinstancesinaspecificcontextofuse[

1

][

8

].Thisdimensionaddresseswhetherallrequireddataelementsarepresentandpopulated.Incompletedatacanresultfromcollectionfailures,systemerrors,ordeliberateomissions.Inmeasurementcontexts,completenessensuresthatallnecessaryparameters,

metadata,andsupportinginformationarecapturedalongsideprimarymeasurements.

Consistency

Consistencyisthedegreetowhichdatahaveattributesthatarefreefromcontradiction

andarecoherentwithotherdatainaspecificcontextofuse[

1

][

8

].Itcanapplyboth

amongstdataregardingoneentityandacrosssimilardataforcomparableentities.

Consistentdatamaintainlogicalrelationshipsandfollowestablishedrulesacrossdatasets,timeperiods,andsystems.Inmeasurementapplications,consistencyensuresthatrelatedmeasurements,units,andformatsaligncorrectlythroughoutdatalifecycles.

Example(s)andpossibleinterpretation(s):

Completeness:Amaterialstestingdatabasecanbecheckedforcompletenessbyidentifyingrecordswithmissingmandatoryfields.Forexample,tensilestrengthmeasurementsthatlackassociatedspecimendimensions,testtemperature,or

humidityconditions.Thecompletenesscanbequantifiedasthepercentageofrecordswithallrequiredfieldspopulated.

Consistency:Inachemicalanalysisdataset,thesumofconstituentpercentagesforacompoundmustequal100%.Forexample,asamplelistingconstituentsat

45%,30%,and25%isinternallyconsistentbecausethevaluesaddupexactlyto100%.Incontrast,arecordshowingconstituentsthattotal110%indicatesaconsistencyerror,suggestingproblemssuchasincorrectmeasurements,

roundingmistakes,orduplicatedentries.

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Credibility

Credibilityisthedegreetowhichdatahaveattributesthatareregardedastrueand

believablebyusersinaspecificcontextofuse[

1

][

8

].Credibilityincludestheconceptofauthenticity,encompassingthetruthfulnessoforigins,attributions,andcommitments.Thisdimensionreflectsusertrustindatasourcesandprocesses.Inmetrology,credibilityis

closelylinkedtotraceabilitytonationalstandards,calibrationrecords,andthereputationofmeasurementfacilitiesandpersonnel.

Currentness

Currentnessisthedegreetowhichdatahaveattributesthatareoftherightageina

specificcontextofuse[

1

][

8

].Alsoreferredtoastimelinessortemporality,thisdimensionassesseswhetherdataaresufficientlyup-to-datefortheirintendedpurpose.The

acceptableageofdatavariessignificantlybyapplication.Forexample,real-time

monitoringrequiresimmediatedata,whilehistoricalanalysesmayusedecades-oldrecords.Inmeasurementcontexts,currentnessincludesconsiderationofcalibrationvalidityperiodsandmeasurementtimestamps.

Example(s)andpossibleinterpretation(s):

Credibility:Measurementdatafromanaccreditedcalibrationlaboratorycarrieshighercredibilitythandatafromanunknownsource.Credibilitycanbeassessedbyevaluatinglaboratoryaccreditationstatus,participationinproficiencytestingprogrammes,andmeasurementandcalibrationcapabilitylistingsindatabases.

Currentness:Amassbalanceusedforhigh-precisionmeasurementsrequiresannualcalibration.Itscurrentnesscanbeassessedbycheckingthecalibrationduedate:ifthecurrentdateis15March2025andthelastcalibrationwas

performedon20March2024witha12-monthvalidityperiod,thecalibrationremainscurrentuntil20March2025.Measurementsfromequipmentwhosecalibrationhasexpiredrepresentsafailurethatcompromisestraceabil