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Bookings Quality Score Model

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Data Quality exercises in organizations is usually taken up as programs or as siloed projects.

However, what is observed, is typical , past the project life, the quality continues to erode.

The challenge to ensure data stays relevant to application and business rules and requirement remains .

The attached slide is a road map to sustain the Data /Information Quality throughout the data life cycle.

Again, it is just an actionable road map, based on real implementations and is subject to changes and improvements.

Veröffentlicht in: Technologie
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Bookings Quality Score Model

  1. 1. Bookings Quality Score ModelStrategy Execution<br />Copyright 2009© by Deep Dive Consulting Inc.. All rights reserved<br />
  2. 2. Booking Quality Baseline Highlights<br />Initial Phase - “De-facto” Business “benchmark for “Quality Assurance” – Bookings Audit Document.<br />Booking Audit Process Facts & Results<br />Fact : Manual and Only Select Sample Booked Orders Audited. <br />Result: ♦ High Rate of Quality Issues.<br />Fact: Sample Selection Criteria relies on multiple non-standardized sources.<br />Result: ♦ Inability to capture exactness of Quality. ♦ Downstream reporting inconsistency<br />Fact: Multiple external and disparate sources of Quality benchmark references.<br />Result: ♦ Inability to get 360o visibility into Bookings Quality.<br />Copyright 2009© by Deep Dive Consulting Inc.. All rights reserved<br />2<br />
  3. 3. Multi-Dimensional Quality Watermark Model<br />Data Quality Metrics applied to discreet Booking Attributes<br />. Examples: Percent Valid “Bill-To” names.<br />MeasuredAcross Business-defined Time periods<br />Example: Quarter to Date or “Last Recorded Hour”<br />Scored across a complete Discreet <br />Booking Record.<br />“Watermarked” using Cumulative Scores to Span Time ,Volume and Different Types of Bookings.<br />Graded to build Scores<br />Bookings  Orders flagged as “Booking” in Oracle<br />Booking Record constituents include<br />♦ Purchase Order Components<br />♦ Order Components<br />♦ Professional Services – SOW components<br />♦ Sync Sort Only Customer Support Components(SOCS)<br />♦ Other Components as indicated in Bookings Audit document.<br />Copyright 2009© by Deep Dive Consulting Inc.. All rights reserved<br />3<br />
  4. 4. Bookings Quality “Watermark”<br />Progression of Data Quality Objectives <br />Quote-To-Cash<br />Copyright 2009© by Deep Dive Consulting Inc.. All rights reserved<br />4<br />
  5. 5. Progression of Booking Rules Discovery<br />Bookings<br /> Business Rules<br />BI <br />Rules<br />5<br />Copyright 2009© by Deep Dive Consulting Inc.. All rights reserved<br />
  6. 6. Data Quality – The Big Picture<br />Policy Alignment<br />Cleansing<br />Assessment<br />Profiling<br />Sustaining<br />Pre-Assessment<br /><ul><li>Review businessprocesses
  7. 7. Review data issues
  8. 8. Develop dataquality metrics
  9. 9. Discover and assess: - domains - policies - standards - rules
  10. 10. Create policy
  11. 11. Create standards
  12. 12. Design and build scalable profiling process
  13. 13. Execute process
  14. 14. Document results
  15. 15. Perform and validate
  16. 16. Apply technical corrections
  17. 17. Business validation
  18. 18. Socialize policy
  19. 19. Revisit prior phases for other data areas</li></ul>Focus for today<br />Copyright 2009© by Deep Dive Consulting Inc.. All rights reserved<br />6<br />

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