This week, I received an email from TealBook regarding “the first iteration of their Supplier Data Platform (SDP), helping customers enrich and improve their supplier data across all of their systems and tools.”
Here is what they sent. What are your top takeaways from reading this?
By the way, I share my thoughts at the end and hope you will share yours in the comment section.
Hello jon,
Welcome to Foreword, a monthly newsletter to keep you informed about all things TealBook.
It’s been an exciting time around here; in February we launched the first iteration of our Supplier Data Platform (SDP), helping customers enrich and improve their supplier data across all of their systems and tools.
We have also been continuously learning from our co-innovation customers and early adopters. We know the problem we’re solving resonates with procurement teams because we keep hearing that they’re keen to get their hands on quality supplier data so they can spend less time on manual processes.
Dive in to learn more about what SDP has to offer.
What’s new?
Ability to ingest vendor master data in real-time
With SDP, we’re able to ingest an organization’s vendor master data in real-time and enrich it using machine learning, artificial intelligence, and human-in-the-loop analytics to improve the completeness, breadth, and accuracy of the organization’s supplier data.
TrustScoresTM
TrustScores are confidence scores ranking from “Very High” to “Low” assigned to every attribute gathered by TealBook’s Supplier Data Platform. They aren’t determined in a one-size-fits-all way; they vary depending on the attribute type. For example, if multiple data sources confirm the “Year Founded” as the same, it gets a ‘High’ TrustScore. On the flip side, certain attributes like diversity certificates, which come from a single certifying body, get their TrustScore based on how confident TealBook is in that specific source. As a result, the TrustScore stays consistent across all certificate-level attributes.
Business Rules Engine
The Business Rules Engine works in conjunction with TrustScores to empower Supplier Data Platform users in controlling attribute inclusion in enrichment requests.
These rules are fully customizable, providing users the flexibility to determine whether data should always be included, never included, or included only if the attribute meets or exceeds a designated TrustScore value. Importantly, all modifications made to these rules are meticulously tracked and documented, ensuring transparency throughout the entire process.
We’re continuously learning from our customers and prospects and making changes to SDP based on what we hear from the market. I’ll be sure to update you on an ongoing basis, but if you ever have any questions or would like to learn more, I would love to connect.
Cheers,
Stephany Lapierre, Founder & CEO, TealBook
When I built my self-learning algorithm, nascent AI platform, it was based on my theory of strand commonality. Simply put, within seemingly disparate streams of data events, there are related attributes that collectively determine outcomes.
By identifying these shared attributes and utilizing self-learning algorithms to assign weighted values based on historical and real-time events, here was the following result:
“In August 2003, the new technology successfully went live in a production environment for the DND. In this test case, the public sector organization realized a year-over-year 23% cost of goods savings for seven consecutive years while simultaneously reducing the number of buyers required to manage the contract to 3 from an original 23. Delivery performance and product quality also improved dramatically.”
Looking back, I should have never sold my company and patent for $12 million during the dot.com boom – but hey, after all these years, it’s great to be actively involved in an industry I love.
By the way, I will have to look through my old archives as I still have the original patent application outlining the solution architecture. When I find it, I will share it with you.
In the meantime, based on what I read above, e.g., “using machine learning, artificial intelligence, and human-in-the-loop analytics to improve the completeness, breadth, and accuracy of the organization’s supplier data,” you should be able to achieve the same outcome I had so many years ago—regardless of the solution provider.
30
Procurement Insights
TealBook’s Stephany Lapierre’s BIG News
Posted on April 12, 2024
0
This week, I received an email from TealBook regarding “the first iteration of their Supplier Data Platform (SDP), helping customers enrich and improve their supplier data across all of their systems and tools.”
Here is what they sent. What are your top takeaways from reading this?
By the way, I share my thoughts at the end and hope you will share yours in the comment section.
Hello jon,
Welcome to Foreword, a monthly newsletter to keep you informed about all things TealBook.
It’s been an exciting time around here; in February we launched the first iteration of our Supplier Data Platform (SDP), helping customers enrich and improve their supplier data across all of their systems and tools.
We have also been continuously learning from our co-innovation customers and early adopters. We know the problem we’re solving resonates with procurement teams because we keep hearing that they’re keen to get their hands on quality supplier data so they can spend less time on manual processes.
Dive in to learn more about what SDP has to offer.
What’s new?
Ability to ingest vendor master data in real-time
With SDP, we’re able to ingest an organization’s vendor master data in real-time and enrich it using machine learning, artificial intelligence, and human-in-the-loop analytics to improve the completeness, breadth, and accuracy of the organization’s supplier data.
TrustScoresTM
TrustScores are confidence scores ranking from “Very High” to “Low” assigned to every attribute gathered by TealBook’s Supplier Data Platform. They aren’t determined in a one-size-fits-all way; they vary depending on the attribute type. For example, if multiple data sources confirm the “Year Founded” as the same, it gets a ‘High’ TrustScore. On the flip side, certain attributes like diversity certificates, which come from a single certifying body, get their TrustScore based on how confident TealBook is in that specific source. As a result, the TrustScore stays consistent across all certificate-level attributes.
Business Rules Engine
The Business Rules Engine works in conjunction with TrustScores to empower Supplier Data Platform users in controlling attribute inclusion in enrichment requests.
These rules are fully customizable, providing users the flexibility to determine whether data should always be included, never included, or included only if the attribute meets or exceeds a designated TrustScore value. Importantly, all modifications made to these rules are meticulously tracked and documented, ensuring transparency throughout the entire process.
We’re continuously learning from our customers and prospects and making changes to SDP based on what we hear from the market. I’ll be sure to update you on an ongoing basis, but if you ever have any questions or would like to learn more, I would love to connect.
Cheers,
Stephany Lapierre, Founder & CEO, TealBook
When I built my self-learning algorithm, nascent AI platform, it was based on my theory of strand commonality. Simply put, within seemingly disparate streams of data events, there are related attributes that collectively determine outcomes.
By identifying these shared attributes and utilizing self-learning algorithms to assign weighted values based on historical and real-time events, here was the following result:
“In August 2003, the new technology successfully went live in a production environment for the DND. In this test case, the public sector organization realized a year-over-year 23% cost of goods savings for seven consecutive years while simultaneously reducing the number of buyers required to manage the contract to 3 from an original 23. Delivery performance and product quality also improved dramatically.”
Looking back, I should have never sold my company and patent for $12 million during the dot.com boom – but hey, after all these years, it’s great to be actively involved in an industry I love.
By the way, I will have to look through my old archives as I still have the original patent application outlining the solution architecture. When I find it, I will share it with you.
In the meantime, based on what I read above, e.g., “using machine learning, artificial intelligence, and human-in-the-loop analytics to improve the completeness, breadth, and accuracy of the organization’s supplier data,” you should be able to achieve the same outcome I had so many years ago—regardless of the solution provider.
30
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