Here is an excerpt from Tealbook’s Stephany Lapierre’s recent LinkedIn post that inspired today’s post:
Your team may only be realizing <50% of the ROI from your Ariba or Coupa investment. Why?
Because procurement tools are only as powerful as the data you feed them.
And as most teams have experienced, quality data remains a systemic challenge. S2P platforms excel at workflow automation, user experience, and feature delivery. But they’re not built to be data platforms — that takes a different structure, architecture, skill set, metrics, and mindset.
Here’s what we’re hearing across large enterprise teams that have already invested in S2P solutions:
– Fragmented, inconsistent vendor masters create duplicates across systems
– Duplicates delay payments, erode hygiene, and limit savings opportunities
– Missing information drives delays and manual work
– Onboarding is still slow, creating compliance risk and operational drag
– Incomplete data reduces visibility into supplier risk and opportunity
🚫 These aren’t isolated pain points — they compound and quietly block transformation.
Here are my comments and questions:
I have been thinking about your post for the past few days, Stephany Lapierre, regarding SAP‘s Joule Feedback Loop and Agent-based Metaprise Loopback process.
Here is an excerpt from a 3-Part Series on Procurement Insights:
“Clean data is not a ‘once done’ and static task but requires a continuous learning loopback process for self-learning algorithms. Otherwise, formerly clean data will revert to its prior state of inaccuracy. In an overly simplified example, why must you launder your clothes or wash the dishes repeatedly?” – Jon Hansen, Procurement Insights (1998 to 2025)
In the context of the above excerpt, I haven’t ascertained how TealBook‘s algorithms deal with clean data to keep it clean. For example, do you use SAPJoule-type explicit feedback loops or an Agent-based self-learning loopback process?
Also, and of significant importance, how do you determine outcomes in areas such as ROI, market alignment, and Tariff resilience?
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Procurement Insights
When does data cleansing start and when does it finish?
Posted on April 14, 2025
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Here is an excerpt from Tealbook’s Stephany Lapierre’s recent LinkedIn post that inspired today’s post:
Your team may only be realizing <50% of the ROI from your Ariba or Coupa investment. Why?
Because procurement tools are only as powerful as the data you feed them.
And as most teams have experienced, quality data remains a systemic challenge. S2P platforms excel at workflow automation, user experience, and feature delivery. But they’re not built to be data platforms — that takes a different structure, architecture, skill set, metrics, and mindset.
Here’s what we’re hearing across large enterprise teams that have already invested in S2P solutions:
– Fragmented, inconsistent vendor masters create duplicates across systems
– Duplicates delay payments, erode hygiene, and limit savings opportunities
– Missing information drives delays and manual work
– Onboarding is still slow, creating compliance risk and operational drag
– Incomplete data reduces visibility into supplier risk and opportunity
🚫 These aren’t isolated pain points — they compound and quietly block transformation.
Here are my comments and questions:
I have been thinking about your post for the past few days, Stephany Lapierre, regarding SAP‘s Joule Feedback Loop and Agent-based Metaprise Loopback process.
Here is an excerpt from a 3-Part Series on Procurement Insights:
“Clean data is not a ‘once done’ and static task but requires a continuous learning loopback process for self-learning algorithms. Otherwise, formerly clean data will revert to its prior state of inaccuracy. In an overly simplified example, why must you launder your clothes or wash the dishes repeatedly?” – Jon Hansen, Procurement Insights (1998 to 2025)
In the context of the above excerpt, I haven’t ascertained how TealBook‘s algorithms deal with clean data to keep it clean. For example, do you use SAPJoule-type explicit feedback loops or an Agent-based self-learning loopback process?
Also, and of significant importance, how do you determine outcomes in areas such as ROI, market alignment, and Tariff resilience?
30
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