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Data Quality - Are We There Yet?

Data Quality - Are We There Yet?






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    Data Quality - Are We There Yet? Data Quality - Are We There Yet? Presentation Transcript

    • Data Quality – “Are We There Yet?” August 17, 2011 Presented By Arvind Mattoo, CBIP
    • Data Quality• Data Quality – Explained• Data Quality – CEO’s Concern• Data Quality – CIO’s Nightmare• Data Quality – PM’s Approach• Data Quality – IT’s Deliverable 2
    • Data Quality – Dimensions Process Dimension Business Dimension• Accessible • Relevant• Consistent • Existent• Complete • Reliable• Lineage • Reportable• Controllable • Compliant• Secure • Measurable Data Quality FACT Technical Dimension Time Dimension• Accurate• Integral • Currency• Unique • Timeliness• Valid • Historical• Secure 3
    • Dimension – BusinessRelevant: Does it Map to our Requirements?Existent: Do we Own it?Reliable: Can we Trust it?Reportable: Can we Visualize it?Compliance: Is it Mandated?Measurable: Can we Baseline it? 4
    • Dimension – ProcessAccessible: Can I Get it?Consistent: Can I Standardize it?Complete: Does it Encompass Usability?Lineage: Can we Trace it?Controllable: Can we Discipline it?Secure: Can we Trust it? 5
    • Dimension – TechnicalAccurate: To what Degree does it Jive?Integral: Does it Comply Structurally?Unique: To what extent is it De-Duped?Valid: Does it Conform by the Rules?Secure: To what Level is it Secured? 6
    • Dimension – TimeCurrency: To what Degree is it Current?Timeliness: How Readily is it Available?Historical: How far back can we Audit? 7
    • Data Quality – CEO’s Concern• Lack of Strategic Information Capabilities• Quality of Decision Making• Lack of Visibility• Loss of Opportunities• Increasing IT Expenditures• Diminishing Rate of Return• Lack of Collaboration 8
    • Data Quality – CIO’s Nightmare• How did we get into this mess?• How does it impact our business?• Are we the only one?• How do we get out of this?• How do we sustain it?• Are we there yet? 9
    • Data Quality – As We Speak!• Data Misused: Not Authorized• Data Abused: Not Qualified• Data Confused: Not Clarified• Data Refused: Not Ratified• Data Diffused: Not Archived 10
    • How did we get into this mess? Business  Technical • Mergers • Conversion • Acquisitions • Manual Data Feeds • Expansions • Lack of Automation • Diversification • System Upgrades • Regulatory • Consolidation • Lack of Ownership • Insufficient DQ Rules • Business Process Changes • System Errors • Lack of Executive Awareness • Source System Changes • Lack of Training • Lack of Expertise 11
    • How does it impact our business? CEO CIO• Reputation at Stake • Time to Reconcile Data• Lower Quality of Service • Delay in New System Deployment• Customer dissatisfaction • Poor System Performance• Loss of Motivation • Loss of Credibility• Compliance Issues • Downstream System Data Issues• Expectations not met • No Single Version of Truth Surging Cost 12
    • Are we the only one? 13
    • How Bad is it? 14
    • Who is Controlling Whom? 15
    • How do we get out of this?• Data Quality – PM’s Approach• Data Quality – IT’s Deliverables 16
    • Data Quality – PM’s Approach Methodology • Assess/Profile Data • Define Baseline • Define Metrics and Targets • Define and Build Data Quality Rules • Enforce Data Standards across Board • Monitor Data Quality against Targets • Review Exceptions and Gaps • Cataloguing Errors • Refine Data Quality Rules • Manage Data Quality against Targets • Automate Data Quality Process • Fine Tuning Data Quality Rules 17
    • Data Quality – PM’s Approach Governance Team • Governance Committee • Data Stewards • Business SME • Business Analysts • Technology SME • Process SME 18
    • Data Quality – PM’s Approach Technology • Data Profiler • CRM • Data Warehouse • Master Data Management • ETL/ELT • CASE • Custom Data Integration • Master Data Integration 19
    • Data Quality – IT’s Deliverables Establish Data Quality Rules • Referential Integrity Rules • Attribute Rules • Attribute Domain Rules • Attribute Dependency Rules • Historical Data Rules • State-Dependent Rules Cataloguing Errors • Error Tracking • Error Notifications/Alerts Score carding • Record Level • Domain Level 20
    • How do we Sustain over time?• Follow Data Quality Framework• Profile Data consistently• Update Rule Based Engine Frequently• Exploit Embedded DQ Functions/Solutions• Adopt Proactive Approach• Establish Stewardship• Practice DQ Governance 21
    • Data Quality – Are We There Yet?• Accessible • Accurate• Relevant • Consistent• Reliable • Complete• Reportable • Secured• Compliant • Integral 22
    • Data Quality – Are We There Yet?Not really!Data Quality is an iterative process… 23