book

Practical Data Quality

Name: Practical Data Quality
Author: Robert Hawker
ISBN: 9781804610787

by Robert Hawker

September 2023

Intermediate to advanced

318 pages

9h 33m

English

Packt Publishing

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Practical Data Quality
ForewordContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookTemplates and diagramsGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1 – Getting Started
Chapter 1: The Impact of Data Quality on Organizations
The value of this bookImportance of executive supportDetailed definition of bad dataBad data versus perfect dataImpact of bad data qualityQuantification of the impact of bad dataImpacts of bad data in depthProcess and efficiency impactsReporting and analytics impactsCompliance impactsData differentiation impactsCauses of bad dataLack of a data culturePrioritizing process speed over data governanceMergers and acquisitionsSummaryReferences
Chapter 2: The Principles of Data Quality
Data quality in the wider context of data governanceData governance as a disciplineData governance tools and MDMHow data quality fits into data governance and MDMGenerally accepted principles and terminology of data qualityThe basic terms of data quality definedData quality dimensionsStakeholders in data quality initiativesDifferent stakeholder types and their rolesThe data quality improvement cycleBusiness caseData discoveryRule developmentMonitoringRemediationEmbedding into BAUSummaryReferences
Chapter 3: The Business Case for Data Quality
Activities, components, and costsActivities in a data quality initiativeEarly phasesPlanning and business case phaseDeveloping quantitative benefit estimatesExample – the difficulty of calculating quantitative benefitsStrategies for quantificationDeveloping qualitative benefitsSurveys and focus groupsOutlining data quality qualitative risks in depthAnticipating leadership challengesThe “Excel will do the job” challengeOwnership of ongoing costs challengeThe excessive cost challengeThe “Why do we need a data quality tool?” challengeSummary
Chapter 4: Getting Started with a Data Quality Initiative
The first few weeks after budget approvalKey activities in those early weeksUnderstanding data quality workstreamsWorkstreams required early onIdentifying the right people for your teamMapping resources to the workstreamsSummary
Part 2 – Understanding and Monitoring the Data That Matters
Chapter 5: Data Discovery
An overview of the data discovery processUnderstanding business strategy, objectives, and challengesApproaches to stakeholder identificationContent of stakeholder conversationsThe hierarchy of strategy, objectives, processes, analytics, and dataPrioritizing using strategyLinking challenges to processes, data, and reportingBasics of data profilingTypical tool data profiling capabilitiesUsing these capabilitiesConnecting to dataSummary
Chapter 6: Data Quality Rules
An introduction to data quality rulesRule scopeThe key features of data quality rulesRule weightingsRule dimensionsRule prioritiesRule thresholdsCost per failureImplementing data quality rulesDesigning rulesBuilding data quality rulesTesting data quality rulesSummary

Chapter 7: Monitoring Data Against Rules
Introduction to data quality reportingDifferent levels of reportingData security considerationsDesigning a high-level data quality dashboardDimensions and filtersDesigning a Rule Results ReportTypical features of the Rule Results ReportDesigning Failed Data ReportsTypical features of the Failed Data ReportsRe-using Failed Data ReportsMultiple Failed Data ReportsExporting Failed Data ReportsManaging inactive and duplicate dataManaging inactive dataManaging duplicate dataDetecting duplicatesPresenting findings to stakeholdersLaunching data quality reporting successfullyEmbedding reports into governanceSummary
Part 3 – Improving Data Quality for the Long Term
Chapter 8: Data Quality Remediation
Overall remediation processPrioritizing remediation activitiesRevisiting benefitsApproach to determining prioritiesIdentifying the approach to remediationTypical remediation approachesMatching issues to the correct approachMoving remediation to business as usualUnderstanding the effort and costTypes of cost in remediationGoverning remediation activitiesKey governance activitiesTracking benefitsQuantitative exampleQualitative benefit trackingSummary
Chapter 9: Embedding Data Quality in Organizations
Preventing issue re-occurrenceMethods to prevent re-occurrenceThe ongoing impact of human errorShort-horizon reportingOngoing data quality rule improvementStrategies to identify rule changesUpdating data quality rulesTransitioning to day-to-day remediationRequirements for successPlanning for a successful transitionIndications that the transition has been successfulContinuing the data quality journeyRoadmap of data quality initiativesIdentifying the next initiativeObtaining supportWhat if no further initiative is sanctioned?Summary
Chapter 10: Best Practices and Common Mistakes
Best practicesSelecting the best practicesManage data quality primarily at the sourceImplementing supporting governance meetingsIncluding data quality in an organization-wide education programLeveraging the data steward and producer relationshipBest practices throughout this bookCommon mistakesFailure to implement best practicesA lack of practicalityTechnically driven data quality rulesOne-off remediation activityRestricting access to data quality resultsAvoid silos in data quality workThe future of data quality workLLMsGreater emphasis on high-quality data in organizationsSummary
Index
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Content preview from Practical Data Quality

7 Monitoring Data Against Rules

After all the hard work prioritizing and collecting data quality rules, monitoring starts to provide the desired payoff.

Monitoring is about organizing the rules you have developed into a set of reports and dashboards that help an organization take action.

Until this point, you have probably only seen your data quality rules in action against test data. This is the point where you will finally judge your data against the rules that you have established and you will see for the first time where the gaps are.

This can bring up conflicted feelings. As a data quality professional, you are hoping that there will be gaps in the data identified by the rules. If you do all this hard work and then find there are few or ...

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Practical Data Quality

by Robert Hawker

7

Monitoring Data Against Rules

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.