DSCInsights in Action: Unlocking Supply Chain Value with Data Trading

Welcome to Digital Supply Chain Institute insights in action. As always, we try to keep the topics actual. We try to have people who can build the value and bring the benefits to global digital supply chain community. And today I'm very happy that we will be talking about the topic which is becoming more and more interesting but on the sidelines of supply chain and actually it implicates the overall value of the biggest trend and hottest topic and that's AI nothing can happen with AI with AI in the aspect of not understanding and having the right data so today I'm very pleased to have with me Ross Novak who is the CEO and president of the association of professional social compliance auditors. Welcome Ross. >> Thank you. >> We also have with us Craig Moss who is the digital supply chain institute director of member members project but he also holds a very interesting link to ABSA as the board member and in that capacity you know Craig will help us understand how we broker this relationship in a special approach related to data. Craig welcome >> great to be here. So let's deep dive into the topic and let me give the context before we go into questions and the context is around that the work in digital supply chain institute brought us to data even before the AI hype and what we developed during that time understanding that global supply chains especially in digital era are uh aggregating more data than anybody else we developed a framework which is a data trading framework where we wanted to understand and help companies how they can define their strategic data and then potentially trade for mutual benefits. So what we are seeing now especially with the AI component and that the data is massively generated and on the other side there are so many critical gaps. So filling the gaps now is even more important than ever in order to utilize and use uh generative AI and AI agents in the particular way that can create a tangible return on investment on one side but also on the other side the value for the customers, partners, suppliers or anybody in the value chain who needs to benefit from it. And this is where uh Craig led development of the data trading framework and talks with ABSKA about how that can be implemented from an idea and a concept into real value thing which actually creates the value on the companies of the market. So Craig I'll start with you and just let's briefly explain uh to our audience what data trading is and how did the framework get developed. >> Great Marco. So you know what we saw in the in the beginning even before the big boom in AI was that companies were starting to use more and more algorithms to do datadriven decision-m and we saw that there were critical data gaps and so they would have maybe they had 90 of the hundred pieces of data they needed but there were 10 critical missing missing pieces. So we started to explore where would those exist and instead of just going to a supplier or a customer and saying hey I need this from you instead of trying to buy it from them we said why don't you look inside to see what data you have that would be of value to them and then that was the whole thought behind data trading and we developed a framework around that and it's really we've used it in commercial context with companies and now we're really excited to be piloting it in this case with APSA in part of their broader ecosystem. >> So this is a great introduction and thank you very much uh for it Craig and going now to Abska. I think what's what's interesting for our audience also is to get a little bit more about whoa is Ross and then on the other side link that afterwards with you know a very interesting pilot we are developing together and then rounding it up with why you see a data trading concept attractive to APSka and its members. >> Well first of all Marco again thank you for having me. You said it well at the beginning. It's a mouthful, but the Association of Professional Social Compliance Auditors is voluntary membership organization that regulates the social compliance auditing industry. So, how doctors and lawyers and accountants are regulated, they have certain skills they have to show, they have ethics they have to follow. We're modeled the same way for the social compliance auditing professions. We have 85 member firms that conduct this work around the world. They do that through 5,400 member auditors. And between those 85 and those 5,400, they conduct almost 200,000 social compliance audits a year globally. Regardless of industry, regardless of country, that's what we do. And so we regulate the firms, we regulate the auditors and the process as well. So that's what we do. And the the reason this came along, not just because Craig is on the board, but our company is growing. The industry is growing. So the number of audits that we conducted, our members conducted, grew 9% year-over-year. When it comes to measuring the exposure of workers to human rights issues on the ground, there is a huge need to do it. For the most part, brands and retailers want to do it because they want to source their goods and services in a way that the consumer can respect it and know that they're not inadvertently adding to the harm. Increasingly, that volume is because we're looking at legal requirements to do that. Whether that's through import and customs or laws and regulations that are applying to corporations across entire continents, regulation and governance is coming from a legal perspective and not just a voluntary one. So the timing is perfect in that because we are becoming more sophisticated and we realized that we have a ton of data here but we still aren't 100% sure how we want to use it. So data training Craig focused on the outcome of the data training. I'm going to start with saying data training is a small experiment to learn how we handle data internally. Who owns the data? Who updates the data? Who uses the data? How do we access the data? How easy is it? Before we start experimenting and giving tons of data on 200,000 audits worldwide, we have to build that muscle, we have to before we start becoming a bodybuilder, we have to hit the gym. Data trading is us hitting the gym. >> One of the things that from my point of view that was so intriguing about ABSKA is that those 80 member firms, they have certain data on what happens in the field. When I looked at it with Ross, Ross and ABSA doesn't get all that data. They have limited data from that. But what Ross has and ABSKA, they have tremendous data on the exam process that an auditor goes through. So what we wanted to look at, one of the things Ross and I have been talking about is how do we correlate exam performance with field performance. So there's a great way where we can start to correlate data from different pieces where right now it's siloed in different types of organizations. Thank you Craig and and this is a great segue towards the the next question in a way and I want to reflect first on something which Ross mentioned and that's like you know we we are cutting a large problem into smaller pieces which are actionable steps rather than you know boiling the ocean taking a small segment in an area where we want to define and have a case study and then scale you know it's it's the best way and it's the right approach because we can test try and pivot and you know we won't say that we'll do it from uh the firsthand and that brings me to you know I I understand Craig that you are working with Ross on defining the problem addressed right and then the missing data and uh begin the discussion with data trading partners so having in mind that it's chuckled in a small let's say chunk very focused that it will be driven by a case study how is that going >> it's going really well um what we have found in this case and it's really applicable beyond ABSA also but is that in each case we can identify certainly what what Ross has and what APSA has and what would help them but then when we start the discussion with each data trading partner that's like starting a whole new discussion what data do they have what problem do they want to solve and they might not want to solve the exact same problem that APSA does so part of that is kind of really understanding the need of each group understanding the data that's available also to understand the relative value of data and this is a key part of the data trading mindset is that some of the things that Ross has are of relatively low value to APSA, but could be really valuable to a big brand or retailer or really valuable to a big audit firm or one of what we call the collaborative programs. These large groups that share audits. That relative value of data is really a key part of this kind of data trading mindset. that getting away from the idea we need to trade large amounts of data to the idea let's get really focused and I'll trade you this for that and what I give you might be relatively low value to me but really high value to you. So that's one of the things that kind of accelerates it. So in each organization that I'm talking to and we have advanced conversations I I can't mention the names yet but advanced conversations with a large audit firm and a very large collaborative program or audit sharing program and in both cases there's a lot of discussions about what problem do they want to solve and it's like I said before it's not exactly the same problem but they realize that together they can solve each other's problem through strategic data trading >> and related to that too everything is that is exactly right. One of the things that we found out when we try to do this on our own one-on-one with some of these partners is data training as I'm learning is helping translate data. The way that we store data or the data we have in the format or the things we're trying to measure are often there's not a an easy correlation to the trading partner. They may keep it may be the same topic but they record it completely differently. And what we found out was we were kind of sliding past each other. We wanted the same goal but we weren't talking the same data language. And one of the reasons this pilot project is so exciting is is you help us to translate that data so we don't have to do that. I'm not the data expert. The trading partner is the data expert. But how do we take disperate data and marry it together in a way that's useful? So that's an aspect of it that's exciting for me as well. And that's part Marco that's part of the DSCI member Lanico the member that has they have the data escrow technology which enables each trading partner to basically I'll give you the non-technical my non-technical description allows each partner to put their data into the escrow account in whatever form it is. They will do the magic internally to exchange it and make it usable to the other party when certain rules are met. I like the notion of uh what uh both of you Craig and Ross you just shared actually a complex problem has been defined in simple steps to try to operationalize the concept and on the other side prove the value which is great right usually where these kind of things fail is that it's a complex problem complex software solution too many let's say cooks in one kitchen and you you connect things only vertically while here we are really connecting them horizontally ally and this brings me to a question for you Ross which is related to something you already mentioned upfront and that's uh the transparency about social compliance audits right the transparency now plays bigger and bigger role in today's world especially at companies and customers willingness to buy a product or a service and then align with the brand looking into ethical procedures right so how do you see data trading helping in that aspect of your work because I think this can help everybody who is looking into this understand where the value is in your concrete example. >> Yeah, that's a good question. You know, our industry is not unlike other industries, other professions where people want to hire the best people and want to have full reliability on the output. In this case, a social compliance audit. And there are occasions where people question the quality of it. I'm not getting what I want. It's not done on time. there are errors in the report. And so this is an opportunity to marry two different kinds of data together. And Craig hit on it earlier. ABSA has a three-part exam. In order in order to become a certified social compliance auditor, you have to take a written exam, another written exam, and then you do a live interview. You pretend like you're interviewing a worker, and you're looking for forced labor issues. That's the biggest thing that people want to find out. And people want to be able to buy a product that doesn't that wasn't made in whole or in part with uh with forced labor. So what we're able to do is we have the exam data as auditors commended the industry. Our trading partners data trading partners have data on performance on the ground. Let's marry the two together and see what correlation connections predictive abilities that data will yield. And that's only good for social compliance auditing but to my knowledge that doesn't happen in a lot of other professions. I'm a lawyer by trade and the consumer doesn't know how well I do on a particular job. There's no public rating. No one's ever asked me, "Well, are you a really good lawyer? Well, how did you do on the bar exam?" You know, and so this is something not only good for the social compliance auditing industry, but if we can measure performance of licensed professionals coming into the industry and while they're already there, a it's a unique development among any profession, but b it increases the reliability of the audit and especially when you're talking about human rights and the well-being of some of the most vulnerable populations in the world, the increased reliability on social compliance audit is more important than ever. And data trading is going to help us do that. Thank you very much uh Ross the thank you Craig. I think uh with this we can round up the conversation on a on a high note. These things are definitely happening. Uh the overall approach is in uh the application mode. I'm sure that in next few months we will be having the first case study and after that I look forward doing another round so we can show our community what the results were what we learned and you know are we on the right track or we need to pivot and I hope you agree with the approach. Thank you once again for being with us and please stay tuned for more.