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A Guide to Measuring Data Quality for Purchased Product Master – Part 2

This is Part 2 of a 2 part blog post. Read Part 1 on A Guide to Measuring Data Quality for Purchased Product Master – Part 1
 
In my previous post I outlined some metrics for the purchased product / material master – both objective measures (like completeness, accuracy, and timeliness) and qualitative measures – attributes and attribute values that add practical business value to material management, engineering and strategic sourcing teams.
 
I left off stating the emerging challenge for the Chief Data Officer – or the executive champion responsible for the Purchased Product / Material Master – is to collaborate with the data consumers to define acceptable measurements and levels.
 
Data Consumers for the PP or Material Master vary consistently (this is not an oxymoron) based on the business of the company. Different types of consumers will have different requirements and the responsibility for success changes with each data type / consumer type pairing.
 
For simplicity’s sake let’s break the world into 4 types of companies:

  1. Discrete Manufacturing
  2. Process Manufacturing
  3. Distribution / Wholesale
  4. Field Services

And divide the broad definition of Purchased Products / Material Masters into 4 sub-types

  1. Off the shelf OBCD Parts for Finished Products
  2. Engineered to Specification parts for Finished Products
  3. Off the shelf Maintenance, Repair and Overhaul parts (and spares)
  4. Fabricated / Machined components for Operations and Maintenance

And group Data Consumers into 3 categories of stakeholders

  1. Engineering
  2. Operations
  3. Procurement / Strategic Sourcing

Each Consumer will have a different sense of ownership for successful data management practices. Noticeably, Engineering and Operations will have the most interest in Engineered or Fabricated components. They will also have the greatest vested interest in attribute mapping, attribute value completion and of course completeness. The challenge is identifying those key individuals who may have access to tribal knowledge in cases where the technical attributes and characteristics are not currently captured in maintenance (operations) or PDM/PLM systems (Engineering).
 
The table below shows the primary Data Consumer
 

A Guide to Measuring Data Quality for Purchased Product Master – Part 2

A Guide to Measuring Data Quality for Purchased Product Master – Part 2

In my next post we will cover the key measures and levels acceptable to each consumer across these areas.
 
Recommended Reads:
 
White Paper – Empower end-users with Master Data Management

White paper – MRO Master Data Management for Enhanced Maintenance Performance

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Arthur Raguette

Arthur Raguette

Arthur Raguette is the Executive Vice President at Verdantis. Arthur is very passionate about the application of innovative technologies to solve real-world business problems with a strong emphasis on large enterprise solutions. He has more than a decade of experience of working with Software for Master Data Management and Data Governance for multiple domains and across industries. Arthur’s prior technology passions included high performance B2B middleware and hybridized SaaS applications for HR, Employee and Education related domains.

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