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Tony O Brien MIT Information Quality Industry Symposium 2010 V1

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Sustaining Data Quality : Lessons from the Field Tony O’Brien The 4 th Massachusetts Institute of Technology Information Quality Industry Symposium 14 th July 2010
Setting the scene ,[object Object],[object Object],[object Object],[object Object],[object Object]
Sustainability ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Organisation: Remploy ,[object Object],[object Object],[object Object],[object Object],[object Object]
Quality Data ,[object Object],[object Object],[object Object],[object Object]
Data Quality Improvement ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conceptual Process Model ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conceptual Framework
What Levels of Data Accuracy are Necessary? Source: Goodfellow, (1994: 18) Inventory Balances 95% Production/Purchase Orders (Housekeeping) 98% Stock Allocations 97% Bills of Materials & Routings 98% Customer Orders 98% Source: Wallace and Kremzar (2001: 195-217) 1 in 12 3 in 20 1 in 3 24 in 25 Potential Error Rate 91% 84% 68% 4% Overall Accuracy 97% 95% 90% 50% Order Accuracy 98% 95% 90% 50% Stock Accuracy 99% 99% 95% 90% BOMs & Routings 97% 95% 90% 30% Planning Parameters 100% 100% 100% 60% Demand Data MRP etc
Data Quality Improvement Project ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Measurement- Key Performance Indicators ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Measurement- Roll Out ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Extension of the Initiative ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Guidelines and Issues ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overall Index Improvement
Overall Progress ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Quantitative Study ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Quantitative Study ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Culture Change ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Motivational Factors for Improvement ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Lessons Learnt ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Deciding Factors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outcomes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Final thoughts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Tony O Brien MIT Information Quality Industry Symposium 2010 V1

  • 1. Sustaining Data Quality : Lessons from the Field Tony O’Brien The 4 th Massachusetts Institute of Technology Information Quality Industry Symposium 14 th July 2010
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
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  • 9. What Levels of Data Accuracy are Necessary? Source: Goodfellow, (1994: 18) Inventory Balances 95% Production/Purchase Orders (Housekeeping) 98% Stock Allocations 97% Bills of Materials & Routings 98% Customer Orders 98% Source: Wallace and Kremzar (2001: 195-217) 1 in 12 3 in 20 1 in 3 24 in 25 Potential Error Rate 91% 84% 68% 4% Overall Accuracy 97% 95% 90% 50% Order Accuracy 98% 95% 90% 50% Stock Accuracy 99% 99% 95% 90% BOMs & Routings 97% 95% 90% 30% Planning Parameters 100% 100% 100% 60% Demand Data MRP etc
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