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Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
Data Protection - Daragh O Brien
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Data Protection - Daragh O Brien

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  • 1. DATA PROTECTIONPART OF A QUALITY MANAGEMENTSYSTEM FOR INFORMATION
  • 2. SOME FORMALDEFINITIONSData (plural of datum) •Facts about things RED
  • 3. Red
  • 4. RED
  • 5. RED
  • 6. SOME FORMALDEFINITIONSInformation •Facts about things in a context •Facts with attached meaning
  • 7. SOME FORMALDEFINITIONSInformation •Facts about things in a context •Facts with attached meaning
  • 8. SOME FORMALDEFINITIONSKnowledge •Information that can be used to trigger action • Right place • Right time • Right format • Right context
  • 9. Peanut allergyCaucasian Ate birthday cakefemale, early 40s within last hour(red hair) collapsedat work Treat for anaphylactic shock
  • 10. Value Context Knowledge Information Information Data Data Data
  • 11. Data ProtectionInformation Quality Information Security
  • 12. SYLLOGISM PART 1Data Protection is a QualityManagement System applied to thecollection, management, use, anddisposal of personal data.(e.g. BS10012:2009)
  • 13. SYLLOGISM PART 2Information/Data Quality is theapplication of proven QualityManagement systems to theInformation Product
  • 14. SYLLOGISM PART 3Data Protection isInformation Quality
  • 15. LINKING TO DATA QUALITYEU Directive 95/46/EC defines “DataProtection” principles as“Data Quality Principles”.
  • 16. WHAT IS “INFORMATION QUALITY”?The degree to which information and data can bea trusted source for any or all required uses.The degree to which data and information meetsthe specific needs of specific customers.Consistently meeting or exceeding knowledgeworker/end customer expectations.
  • 17. DAVID LOSHIN
  • 18. Data Information Data Data Model Data Values Presentation Policy Domains Accuracy Enterprise Agreement Clarity of Definition Appropriateness Accessibility of UsageComprehensiveness Correct Interpretation Null Values Metadata Stewardship Flexibility Flexibility Completeness Privacy Ubiquity Robustness Format Precision Consistency Redundancy Essentialness Portability Currency Security Representation Attribute Granularity Timeliness Unit Cost Consistency Representation of NullPrecision of Domains Values Homogenity Use of Storage Naturalness Identifiability Obtainability Relevance SimplicitySemantic Consistency Structural Consistency Dimensions of Data Quality © D
  • 19. DANETTEMCGILVRAY
  • 20. Ease of Use &Data Specification Maintainability Data Integrity Data Coverage Fundamentals Duplication Presentation Quality Perception, Relevan Accuracy ce, Trust Consistency & Data Decay Synchronisation Timeliness & Transactability Availability Danette McGilvray’s Data Quality Dimensions.
  • 21. LARRY ENGLISH
  • 22. DATA QUALITY CHARACTERISTICS Data Protection
  • 23. HIQA’S DEFINITION OF DATA QUALITYData Quality refers to data that isaccurate, valid, reliable, relevant, legible, complete and available in a timely manner todecision makers for healthcare delivery andplanning purposes.
  • 24. DATA QUALITY CHARACTERISTICS HIQA
  • 25. W. EDWARDS DEMING
  • 26. SYSTEM OF PROFOUND KNOWLEDGE Theory of Optimisation Theory of Theory of Psychology Knowledge Theory of Variation(c) Castlebridge Associates
  • 27. THEORY OF KNOWLEDGEKnowledge cannot exist with out a theoryExperience is not the same as theoryTheory shows cause and effectTheory allows for prediction (c) Castlebridge Associates
  • 28. THEORY OF KNOWLEDGE “Best Efforts? Imagine the chaos if everyone ran around trying their best without a theory of knowledge to inform their actions. Disaster”.(c) Castlebridge Associates
  • 29. Seek first tounderstand… Stephen R. Covey
  • 30. THEORY OF KNOWLEDGEI could copy my maths homeworkI’d get THAT problem right But would I understand the principles to apply to a different problem?
  • 31. KEY LESSONEffective implementation of Quality Systemsrequires an understanding of the “Theory ofKnowledge” and the fundamental principles ofthat Quality system.Blind adoption of tools, techniques, andtemplates without the Theory of Knowledgetells you “WHAT” but not “WHY”.
  • 32. NON-LINEAR LIFE CYCLE Apply Plan Obtain Store/Share Dispose MaintainBased on English 1999 and McGilvray 2008
  • 33. MAPPING THE LIFE CYCLE TO DATAPROTECTION
  • 34. INFORMATION CHAINS – THE FOCUSAn information chain is effectively a chain of processes through whichinformation flows to achieve an objective in the organisation.Only by understanding how information flows can you understand howthe quality of the information • Affects the organisation • Is affected by the Organisation
  • 35. If you cant describe whatyou are doing as aprocess...... You don’t know what youare doing. W. Edwards Deming
  • 36. THIS IS NOT A PROCESS MAP ORINFO CHAIN DESCRIPTION• We do this.• Then Martin in Accounts does that.• Then Betty in Receivables does this other thing• Then it comes back to us• Then something else happens.• 4th Thursday of month the Jaberwock audits.
  • 37. If I had wanted toknow what you did on your holidays,I’d have asked. Process Improvement Lead, Telco industry
  • 38. INFORMATION CHAINS Information Flow Some Output Some OutputSome Some Some Action Some Action Some ActionInput Output By someone By someone By someone That becomes That becomes an Input an Input
  • 39. DATABASES ARE LIKE LAKES
  • 40. DAVID LOSHIN
  • 41. THE VIRTUOUS CYCLE
  • 42. THE VIRTUOUS CYCLE(c) Castlebridge Associates
  • 43. DANETTE MCGILVRAY10 STEPS TO TRUSTED INFORMATION
  • 44. SOME INTRODUCTORY COMMENTSDanette’s view on Information • Information must be consciously managed as a resource (a source of help to the business) and • As an asset (a source drawn on by the business to make a profit) • Information is a product of processes and activities in organisations.Danette’s Definition of Information Quality • the degree to which information and data can be a trusted source for any/all required uses
  • 45. ASSESSMENT-AWARENESS-ACTION
  • 46. FRAMEWORK FOR INFORMATION QUALITY(c) Castlebridge Associates
  • 47. THE 10 STEPS METHOD™
  • 48. LARRY P. ENGLISHTIQM™
  • 49. THE TIQM PROCESSES
  • 50. COMMON COREELEMENTS
  • 51. INFORMATION IS…1.An Asset2.A Product
  • 52. INFORMATION QUALITY PROGRAMS1. Should be based on proven Quality Management Principles2. Make use of objective statistical measurement of quality3. Emphasise elimination of process defects to remove root causes of errors4. Should be cyclical and based on philosophy of continuous improvement5. Should emphasise the development of a Quality Culture that pervades the organisation.6. The focus must be on improving the system of production, eliminating common causes of defect, and preventing errors7. Scrap and rework is not Information Quality Management
  • 53. BUT BACK TO DATAPROTECTION…
  • 54. SUMMARY (OF THEORY) Data Protection & Information Quality are closely linked disciplines Understanding your Processes is key Quality has to be built in Inspecting defects out is not Quality POSMAD Information Life Cycle gives context You can measure Quality of Information (across many characteristics)
  • 55. From Toothpastefordinner.comProcess & Context =Meaning & Purpose
  • 56. Information has attributes you can measure...Measurement can support Controls andPolicies Metrics can support Change Management goals
  • 57. What ismeasured gets done.
  • 58. How can you feed the GREED motive?
  • 59. What is your Data Protection Scorecard?How does it translate to your bottom line?
  • 60. GETTING HELP
  • 61. THE IAIDQ• International Association for Information & Data Quality• Founded in 2004• Leading Professional body for Information/Data Quality practitioners.• 500+ members in 15 countries• Active in Ireland through collaboration with the Irish Computer Society (the “IQ NETWORK”)
  • 62. D2: Information Quality D1: Environment Information and Culture Quality Strategy and D3: Governance Information Quality Value and Business Impact D6: SustainingInformation Quality D4: Information Architecture D5: Quality Information Quality Measurement and Improvement

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