Different Success Metrics- IT Led MDG with DQ and Business Led DQ with MDG

In my previous post I attempted to differentiate among the different ways DQ and MDG projects get started and how both the DQ and MDG processes are prioritized.
As a quick follow up to that, I want to share that each approach – whether an IT lead MDG with DQ as a required process, or a business process for DQ improvement “Get it Clean” supported by an MDG “Keep it Clean” component, each has different measures of success.
To refresh, we see IT led efforts related to ERP/EAM or MRP consolidations starting with MDG, with DQ providing the required role for legacy data and a supporting role for real-time DQ improvement as part of an MDG roll out.
Business led efforts see ongoing DQ as well as legacy DQ at the forefront with the technology for MDG frameworks being a secondary concern. The accuracy and consistency of data is the key to business analytics.
For IT led initiatives we see success criteria such as the % of completeness, reduction in duplicates, and tightening of approval process times.
For business led initiatives we see criteria such as reduction in inventory (either a % or an actual hard currency reduction), cost savings (again either a % or a target figure), increased spend under management, reduction in maverick spending, increase in number of POs before vendor invoices, and reduction in expedited deliveries.
The core difference, as I look at the measures above, reflects the business challenges and success criteria monetization. While IT generally looks at platform compatibility, ongoing maintenance and net purchase costs of DQ and MDG solutions, the business is looking for cash-flow and balance sheet improvements.
What are some reasonable goals for the business criteria?
Let’s look at the numbers next time.
Blog: Guiding Principles That Guarantee Success In An Enterprise Data Management Strategy

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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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