Episode Transcript
[00:00:00] Speaker A: Hi everyone. Today we're going to learn about data trading and how it can dramatically advance your supply chain risk management efforts. I'm your host, Bill Coffin, and this is the.
Supply chain risk has steadily grown in its importance to ethics and compliance programs, especially as organizations around the world no longer engage in supply chains, but rather supply webs that can involve thousands of third, fourth and even fifth parties across our increasingly interconnected world.
For E and C leaders, running programs on fixed resources, supply chain due diligence and risk management has become a daunting task, especially as global regulations require businesses to take a much deeper dive into their supply chain due diligence.
This is where generative AI is making a huge difference. By using Genai and its large language models, ENC programs have been able to extrapolate their total universe of inherent and residual risk, magnifying their ability to monitor their supply chains by an order of magnitude. And now the vast amounts of supply chain data that E and C teams can successfully manage thanks to the smart application of AI are enabling ways to use that data strategically to to manage risk and build value.
With us today to talk about this is Craig Moss, Executive Vice President of Measurement at Ethisphere and a director at the Digital Supply Chain Institute and the Cyber Readiness Institute. He is also on the board for the association of Professional Social Compliance Auditors. Craig is a prolific thought leader, public speaker and author on the subjects of value chain holistic risk assessment and AI.
His most recent byline with the Dow Jones Risk Journal is is Stop Hoarding, Start Trading the New Data Strategy for Gen AI Success, which explains how data trades can help organizations fill specific gaps in their supply chain risk management programs. Craig, welcome back to the Ethicast. It's great to have you on the program once again, Bill.
[00:02:06] Speaker B: Great to be here. I always enjoy it.
[00:02:09] Speaker A: Can you explain what data trading is and why it's a natural extension of the role that generative AI plays in advancing supply chain risk and due diligence?
[00:02:19] Speaker B: Yeah, I mean just fundamentally data trading is the idea that each organization has data and instead of the idea that I just need you to give me your data, Bill, I could go in and start to be much more specific and say, bill, I will give you this in exchange for that.
So it really becomes instead of thinking at a big idea of like large scale data exchanges, I want companies to start thinking much more specifically to have a laser focus on specific data that they need to fill certain data gaps.
But don't just ask other people for it. Tell them what you can give them. So that's where the trading part comes into it.
So I think that that's really a different mindset and we've seen in a lot of cases it's challenging for companies to change the mindset because so many companies think about, oh we got to protect all our data, we need to hoard it, but that's really not the most effective way. And if you're strategic about trading it with other organizations, you actually can create more value for both organizations.
[00:03:27] Speaker A: What might a legal framework for a data trade look like? I mean one imagines you'd have to get all that sorted out before moving on to the particulars of the data trade itself.
[00:03:36] Speaker B: So we were very fortunate. At the Digital Supply Chain Institute, Dave Capos from Crevasse, Wayne and more is on the board of DSCI and his law firm helped us to put together a data trading framework from a legal standpoint and at a simple level. I'm not a lawyer but I can explain it in a non legal way. It's a restricted use license. So when I trade the data with your organization, I'm giving you a restricted use to restricted license to use it. It's in the specific way that we're putting into the trade. So if I say you can use this for internal analytics internally to feed into your gen AI models, that would be the restricted part of it royalty free. But also it doesn't become, you don't own it. I still own it. I'm just giving you that restricted use license that I can terminate if I want to terminate it, but then both sides would terminate it. So it's very much of a bilateral agreement in that way.
So when you think back to your first question about the role of Gen AI in this, what we see with gen AI and in one of my earlier articles I talked about the idea of companies needing to get more data around what's happening inside the companies in their supply chain like program maturity data, performance data, things like that.
That's some of the data that we think is really effective to try to trade for.
[00:05:07] Speaker A: Craig, in your article you explain a five point pilot program for what a data trade might look like. Can you go through those five points for us?
[00:05:16] Speaker B: Sure.
Bill, it really all starts with the idea of defining the goal is the most important part. And the more specific that a company can be in defining that goal in looking at exactly what problem they're trying to solve, the better they'll be able to identify the data gaps that they have.
So if you're starting with a big general problem it's harder to become specific about the data you need. So we really advocate that companies take a very specific look at defining the goal and specifically what problem they're trying to solve with this. So in the case of one of the PILOTS programs that is mentioned in the article for Dow Jones, we're working with apsca, the association of Professional Social Compliance Auditors.
And they want to look at how they can improve auditors ability to uncover conditions of forced labor in the supply chain. So that becomes the goal is. And then you can start to think how do we start to get data to be able to facilitate better understanding of that so that we can work on it? So that's number one is defining the goal.
Number two is to assess the data that you have and the data that you need, what are the gaps that you have.
So in the case of apsca, they have a lot of data on the exam results of an auditor as they go through the exam process.
What they don't have as much of is data on the field performance, the correlating field performance of that auditor.
What the audit companies have is they have of course data on the performance in the field, but they don't actually have as much visibility into the exam.
So by starting to trade that information, we're able to look at a better correlation between exam performance and field performance that then can improve the exam process, the training process for auditors, but also could actually then be used to look at what would be the remediation elements that could take place in the field. So there's a lot of things that can take place here that are beneficial to all the parties. And just by being specific about trading certain pieces of data, once you identify the data gaps, then the next piece of course would be determine the relative value of data.
This is something that was really interesting Bill, is people think all our data is so important, it's also valuable. That's not true.
Some of it is really valuable and some of it is not really valuable.
So if you think about what is not valuable to me, could be really valuable to you?
So you look at the value relative, the relative value gap also and that is really where companies can start to become a little more strategic.
Right. What could I give you, Bill, that is going to be really valuable to you? That I don't really care if it's not that useful to me. We worked on one case and this is not so compliance related, but that was really interesting.
Is a large global shipping and logistics company.
So what data did they want from the big apparel company they wanted to know what are the big port that you're going to be using six months from now for all the goods coming in for Christmas.
Right. So why? Because they wanted to have empty containers there. Because the big shipping company, one of their problems is having empty containers in the right place.
Okay, it makes sense. I'd never thought about that before, but you hear and they say, yeah, that's a big problem.
So if they know that XYZ company is going to be having a major promotion and all the goods are going to be coming out of a certain port, they can just make sure they have the containers there.
So it's that relative value of data that's the next piece. Then from there it really gets into preparation.
And then now you start to think about like preparing for the negotiation. What is the data that we want to trade specifically, how often do I need it? That's another thing, Bill, that, you know, people talk about, oh, we need all this data in real time.
That's baloney. You might need some data in real time, but if you're only going to look at it once a month for some kind of monthly report, you don't need it in real time. What you need it is once a month.
So that's the other thing that we talk about with people is how, how often do you actually need it and what is the use of it going to be with Gen AI? Even with that, a lot of people using Gen AI just to produce like monthly reports or quarterly reports, they then don't need a real time data stream for that. So that's another piece that we look at.
And then you start to get into the actual negotiation. So the final step is really around negotiation and governance.
So that gets into what am I going to give you? But also the relative value. So all the pieces in the preparation come to this point of negotiating the actual trade.
And if the preparation has gone well, we have the legal framework, you have the technology, and we're actually working with an interesting company in Serbia that has called Lanaco and they have data escrow technology.
So that's what I call it. They call it, they use a different terminology, but I would just simply call it data escrow. Just like with banks, you know, you can put money into an escrow account and then the bank releases it when certain conditions are met.
That's what they have this very cool technology to do called Lanico is the name of the company. So the data from each party would go into the data escrow account.
When the conditions are met, it gets released to each party and they can use it and integrate it into their own systems.
[00:11:15] Speaker A: So this all feels like very human facing engagement for something that has its roots originally in AI. Craig, what does this tell you about the role that humans still have to play in putting AI to its best use as a tool for advancing ethics and compliance outcomes?
[00:11:31] Speaker B: Yeah, I think Bill, clearly, clearly going back to the problem definition, I think the problem definition really becomes a critical element of it. And then the ability also to convince other people to change mindset right internally, to go to the other internal department and say, hey, we're going to trade this data with XYZ company.
That takes convincing. It takes influencing. And that's something that humans are good at or some humans are good at.
Then the next piece is influencing the trading partner and negotiating it. Those are all human qualities that are really, really important.
I think that the AI certainly could be used it would be interesting to see if you could use AI to determine the actual relative value of data. I hadn't thought about that before, but that would actually be a really interesting thing is to feed some different data sets into an LLM and say, rank the value of this data to my organization. That would kind of be an interesting exercise. But so much of the other stuff I think is really human centric.
[00:12:41] Speaker A: Well Craig, thank you very much for stopping by the Ethicast once again for this latest look at how AI is transforming the state of the art in ethics and compliance. It's always great to have you back on the program. I always learn something every time we chat and so again, thank you so much for coming back on the show.
[00:12:55] Speaker B: Thank you.
My pleasure. I always love doing it and I look forward to the next time.
[00:12:59] Speaker A: To read Craig's article Stop Hoarding, Start Trading the New Data Strategy for Gen AI success, please visit the Dow Jones Risk journal at dowjones.com it's the latest in the series of articles that he has published and all of them are deeply informative. We will leave a link to Craig's article in the show Notes for this episode to learn how AI is transforming the state of the art in ethics and compliance. Visit ethisphere.com to get your free copy of our report AI in Ethics and Risk to Manage Tool to Leverage, which features an overview of AI regulatory trends, AI governance best practices, and compelling use cases from the field.
For new episodes each week, be sure to subscribe to us on YouTube, Apple Podcasts and Spotify. And if you haven't Already, please follow ETHASphere on LinkedIn to learn more about how we help organizations measure and improve their ethics and compliance programs. Together, we can make the world a better place by advancing business integrity. That's all for now. But until next time, remember, strong ethics is good business.
