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Information Management Framework Data Quality
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30 Jan 20031Information Management Framework Data Quality2What is qualityQuality is dynamic concept that is continuously changing to respond to changing customer requirements Defined in 3 ways:Conformance to specifications (DQA)Fitness for use (Surveys)3Quality issuesProblems can result from:Human errorMachine errorProcess error4PurposeStorageAccess andUse Archive andCollectionInformation lifecycle phasesDisposalData QualityAssessmentData EntryVerificationInternal qualitychecksData StoreData CollectionData AccessHistoric dataUser FeedbackAnalysis andFitness for useApproval topublish (vetting)30 Jan 20035Conformance to specifications: Quality Plan Data Quality Assessments6Data Quality AssessmentsQuality Investigation Module Update MetadataData QualityAssessmentprocessesPrior auditsStandardsUse/analyse dataDefine BusinessRequirementsDefineBusiness/datarulesQuality Plan(Benchmarks)System SpecData Quality management cycleObtain FeedbackStartUpdate DataManagement PlanOriginal useDefine Purposeof DataUpdateBenchmarks7Data StoreData CollectionData AccessHistoric dataStorageAccess &UseArchive/DisposalCollectionDQ Assessment and Remediation ProcessAudit RecommendationsData Quality AssessmentApproval /Priority ProcessData RemediationPart 3 Data Management Plan Information lifecycle phases8Recording quality -ANZLICTotal Input ErrorsLinageVertical accuracyHorizonal accuracyPositional accuracyAttribute accuracyAttribute consistancyLogical consistancySpatial CoverageTemporal CoverageClassificationCompletenessQuality9Business rulesEach business rule should have an expected outcome (benchmark)Business rules need to align to quality ANZLIC elements 10Findings -DQ ProcessesThe processes and guidelines are good!The Data Management Plan is importantNeeds to be completed by all data sets prior to AssessmentBenchmarks for quality established with Data Managers before DQA11Soil ProfileVery large and varied data set (millions of soil properties)Where Data exists -is mostly goodMany missing valuesData Transformation ErrorsData on forms different to values in databaseMissing values set to default values in load program.12Data Analysis –Soil PropertiesExamples of problems:Location Accuracy -Invalid grid references for a grid zoneMandatory Fields missing dataNature of Exposure-1269 records missing valueLogical InconsistenciesIf Horizon Code begins with 'B' And ACS Order is 'SO' (Sodosol)Then pH >= 5.5238 records in error.13Data Analysis –Ground WaterMinimal spatial data (point locations only)Data where present is mostly goodMany missing values14Examples of problemsInvalid Key fieldsWork Number of non standard format Location AccuracyInvalid grid references for a grid zoneLogical InconsistenciesJobs completed before they startedHole depth of 36kmMandatory Fields missing dataWork Type Code -1503 records missing value.15Data Analysis –Ground WaterDatabase Issues:No Load or creation date in database (only update date)Impossible to apply date based business rulesGW licenses mandatory from 2001 onwards.Logical Inconsistencies:License Form A received and no GDS record (1000’s)Needs investigationRegion CodeRegion NameGW Licenses in LASGW Licenses in GDSGW Licenses not in GDSPercentage Missing10Sydney -South Coast362234202025%20Hunter2000128072036%30North Coast32013162391%40Murrumbidgee191218071055%50Murray2350911143961%60Lower Murray /Darling84424250%70Lachlan1913137154228%80Macquarie -Western2345200234314%90Barwon45264445812%16Data AnalysisAction ListsGenerated for each data setScope of RemediesImproving data quality goes beyond the identifying, measuring and fixing the data in the IT systems. Improve data capture–Train entry staff–Replace entry processes–Provide meaningful feedback–Change motivations to encourage qualityAdd defensive checkers, Periodic DQ asssessments, Data cleansing17Data Quality ReportingData Quality PortalGeneral DQ informationStatistical Reporting and MonitoringData Quality Exception ReportingManagement of Data Quality issues30 Jan 200318Fitness for use -User needs covered later in day30 Jan 200319Improving quality20Ways of improving qualityTackle quality at source, not downstream in the lifecycleTraining data collectors in importance on getting it rightContinual improvement with quality method 21Planning ProcessControlling process (check)Initiating processClosing processExecuting process (do)Links among Process Groups in a Phase(Arrows represent flow of information)( PMBOK 2000 Fig 3-1 p31)2223
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