Stephany Lapierre: 0:03

If you operate globally, it's very, very challenging to keep track of all the different legislation. And access the data that you need to be able to report and be compliant, right? And so, that's a perfect use of AI and automating, you know, those rules, those, requirements, those data attributes that you need to collect, to refresh, to report, versus having to buy software to solve for each of those legislation you know, you're gonna end up with 300 different niche solutions that are gonna address different component of it

Tom Raftery: 0:34

Good morning, good afternoon, or good evening, wherever you are in the world. This is the Digital Supply Chain Podcast, the number one podcast focusing on the digitization of supply chain, and I'm your host, Tom Raftery. Hi everyone and welcome to episode 367 of the Digital Supply Chain Podcast. My name is Tom Raftery and I'm excited to be here with you today sharing the latest insights and trends in supply chain. Before we kick off today's show I want to take a quick moment to express my gratitude to all of our amazing supporters. Your support has been instrumental in keeping this podcast going and I am really grateful for each and every one of you. If you're not already a supporter, I'd like to encourage you to consider joining our community of like minded individuals who are passionate about supply chain. Supporting the podcast is easy and affordable with options starting as low as just three euros or dollars a month. That's less than the cost of your latte and your support will make a huge difference in keeping this show going strong. To become a supporter, you simply click on the support link in the show notes of this or any episode, or visit tinyurl. com slash dscpod. Now, without further ado, I'd like to introduce my special guest today, Stephanie. Stephanie, welcome to the podcast. Would you like to introduce yourself??

Stephany Lapierre: 1:54

Yeah. Thank you so much, Tom, for inviting me. So I'm Stephany LaPierre. I'm the founder and CEO at Tealbook. And so Tealbook is a AI company. We've been focusing on, using the technology to collect information on every B two B company in the world. We create a digital, universal supplier profile that continuously gets more robust or more coverage across more information, more attributes to a company, and also, improving the quality of that information over time. That's allowed us to, ingest the vendor master of large enterprise. Match it to the right entity, enrich it with more attributes, keep the data maintained and updated, and enable that data to be distributed across their systems. Ultimately giving them, better visibility, better data quality, better access, so that they can power their tools, their processes, their people, to drive much better and faster outcomes.

Tom Raftery: 2:54

Okay. Interesting. Before we get into the why you're doing that, tell me, first of all, what kicked off this? What was the kind of Damascene moment that you decided to set up a supplier intelligence organization?

Stephany Lapierre: 3:12

That's a, it's a question that's being asked many times, like, why this? And so the inspiration started about 16, 17 years ago I had started a consulting firm initially focused on finding innovation for large companies to solve business challenges. So the company was called Matchbook. The premise was if you had a business challenge and you wanted to find innovation, my firm would go and find suppliers, third party providers that had solved similar problems for other companies, or came together to brainstorm and, and develop a solution that would drive innovation within the organization. And if, you know, in, in that business I remember the first month or so of starting Matchbook, I was with a client who said, Steph, you need to meet this amazing company. These guys spun off from another larger company. This is a startup, and I've hired them and they've drive, they've drove a lot of value for business. You need to have them in your roster. And she grabbed a two inch binder from under a desk. Slapped it on her desk and start flipping through business cards and pamphlets that were falling everywhere. She, she had this binder for 20 years that she had been, you know, in this role. And you know, it took her about 10 minutes to find the business card that she was looking for. One she couldn't remember the name of the company 'cause it was a funny acronym, but says as soon as I see the card, I will remember. And I remember it took long, a long time because I had a flight to catch and I was like, Kim, just send me their contact information. She says, no, no, no, I'm gonna find it. And once she found the card, she gave it to me and said, write their contact information 'cause I don't wanna lose it. And, and she took the card back and shoved it in the binder. And then the binder was put under a desk. That was like the, a little, you know, a little we say in French. Like the, the first kind of light bulb where I left that client which was Johnson and Johnson, so a big company, and I was thinking, how on earth does Johnson and Johnson capitalize on the knowledge that Kim had about this amazing company who drove value for a business, if it takes her 10 minutes to even remember, you know, to get their contact information. And that's because I was in her office and we're talking about something that sparked for her to think about this company. And at the same time, I knew that there was a big consolidation exercise that was happening with one of the large consulting firm, and they're trying to reduce their supply base by 50%. And so it's like, how can they reduce their supply base by 50%, if they don't have the information about these small companies that may not be a lot of spend right now, but are actually driving value and are making impact in the business? And it's sort of, you know, for me, as I'm driving, I'm like, this makes no sense. Like, how can we have this information digitized in a way that can be shared, that can be accessed, that can be optimized? Because think about the number of suppliers. In this case it was 150,000 suppliers globally. If you have no data, no information on what these suppliers do, how they're being leveraging, leveraged by the organization, by what team, what value are they driving? How can you optimize this massive investment, not just in dollars going to these suppliers, but in the management of like sourcing those suppliers, going through diligence, you know, negotiating, onboarding them, paying them. It made no sense to me to have such a big asset, that could drive so much value if you knew more about it and you could optimize it properly and it just the next nine years just continue to, you know, be in the forefront of everything I did. It's like, this is not a software or people or process problem. This is a data problem. And I just couldn't understand or grasp why data was such a big challenge until I start peeling off sort of the layers to understand that. It's not just a problem, it's a systemic problem across every single enterprise, across every single sector. And there's a lot of reasons why. But that became sort of a bit of a, maybe an obsession. And I think you do have to be obsessed with solving a big problem like this when you decide to launch a tech company. But that became a catalyst. So I waited nine years before doing something about it, 'cause my, my consulting firm was very successful. I still own it. I don't operate it anymore, but and I have three children and so I knew that, you know, building a tech company was gonna require a lot of investment of my time, my energy. It also required capital, which I had never raised capital before, and so it was a lot of reasons why I shouldn't have not done it. Maybe I should, I should have not done it. But I decided to take this on and then made a commitment from the beginning. And, you know, the commitment grew as, you know, I put more effort into it and raised capital and became, you know, employed people and, and start, you know, winning customers then. Then there's a huge responsibility of making that company a success.

Tom Raftery: 8:05

It is your fourth child,

Stephany Lapierre: 8:08

It's not a child, you know, it, it could be seen this way, but it's not, it's a business. And it's a very important distinction because there, there's a, it is emotional, but there's also a very rational side of the relationship with the company, cos you have to make good decisions and you can't let emotions drive those decisions. And so, and I'm very different at home with my kids and I am, you know, when I'm running my business. So I, I don't consider a for child, but it's definitely in terms of investment of my time and energy, you know, it equally or maybe more at times, right? It's taking a lot of, over the last eight, nine years.

Tom Raftery: 8:45

And where did the name Teal Book come from?

Stephany Lapierre: 8:48

So it was the notion of this binder, so it was like your black book, your card book of businesses that's not digitized. So if you can make that digitized, transparent, you know shareable, something that you could optimize, something that connect to your systems and tools and your analytics. So it was sort of the, the notion of a teal book, like your, your, your book of, of really important relationship to the organization.

Tom Raftery: 9:11

Okay. And you alluded to something there the kind of the difficulty of gathering all this data and then selling it on, I mean, it it sounds like people who are potential clients are used to buying software solutions and sure, you have a software solution, but you're not selling functionality per se. You're selling intelligence or data. Is that a distinction that is challenging?

Stephany Lapierre: 9:42

Yeah, that was probably the biggest challenge, in building a company because, you know, when you go shopping for a house, you, you look at the house and you like the, the door, and you like the rooms and the floor and the kitchen. But if there's no foundation, the house is built on, you know, cracky or, or, or weak foundation, it's not gonna sustain itself. And so, and we have, and I say we, because I do feel part of the procurement community, we have this notion that software is more tangible. Right? If I, if I have a workflow, if I'm putting, I'm logging into something, it drives change and it's, you know, I need people to use it and the more people are gonna use it, the more change it's gonna drive. And we've been conditioned to buy software for a really long time. And so in the early days of Teal Book, I call it a bit our split personality.'cause we were trying to explain how we were creating this digital profile and the data itself would enable all these, these use cases, right? All these business outcome. But procurement couldn't understand that. And so we had to build an application with software as a way and focus on some use cases where we could demonstrate what we meant in a more tangible way. Especially we're thinking, you know, 5, 6, 7 years ago. And so, and the use case were very well intentioned in in the way that the first one was a search engine because when we were starting to collect this data, which is all our data in these profiles, although we were grabbing, you know, the name, the website, the location, the goods and services, the, the contact information, all these different attributes, we had no way of knowing if the data was good or bad yet. We were just, you know, the first the first sort of projects were like, can we actually find a B2B company , you know, using the web like we built on GCP, start leveraging some of the the Google machine learning models initially, and could we extract the information in a way that was useful and then, could we extract in a way that, you know, we could expand the coverage, but we didn't know if it was good or bad. So it was very easy for a human to look at a profile and say, oh, I know Mike has been gone for five years, and then discount the whole thing. Right? And if we couldn't expose sort of a trust score or level of confidence in our data, that was very difficult for, for procurement people to, to take it at face value. But what we had is enough context between companies. And so when we launched this, this platform, I think it was 2017, we had 600,000 digital profiles. And we had enough information to understand, well, what makes a supplier similar, not to one another? And so we decided to, our first commercial use case was going to be a search engine with a discovery application to enable sourcing managers to find existing and alternative suppliers 90% faster than if they were doing it through Google or word of mouth. And if it was directionally correct, that was good enough because instead of taking two or three weeks to get back to the business, you know, even if they had to toggle with the search and the advanced search and the keywords, it's still, they would still get back to the business within two to four hours, right? So it was a huge, you know, amount of time saved, but also from a credibility perspective and a value driver for the business was huge. And so that's what we launched with, that was 2017. And as we progress, our customers start asking for other use cases, and we start looking for what is the next use case we can solve for? And then it became very apparent that you know, the, the supplier diversity reporting task is incredibly manual. And so traditionally, you know, you'd get your, you extract your data from your financial systems. Say you have 150,000 suppliers globally. Then you gotta figure out which of those are potentially small and diverse. Then you got figure out who to contact. You gotta contact those suppliers. Encourage them to come and put a certificate. If they're certified, into a portal. You gotta validate that the certificate is legit and not expired, and then you get to classify to the right classification and then match it to the right entity, often even for sustain at the facility level. Right? And so it became, finding and attaching and enriching and refreshing certificate to the right entity was just such a manual task for the amount of, of, of I don't say RoI because there's a lot of RoI attached to it, but the benefit or the perceived benefit of the outcome versus the amount of work didn't sort of match in procurement's mind. And so we decided to tackle this. For us it was a forcing factor to really start looking at small, privately owned businesses, and if we could get richer, better data on these companies, that's where a lot of the change happened. That's where a lot of the innovation comes from. That's where a lot of the risk also is introduced, and there's not a lot of good data on privately, small, privately owned businesses. And so it was sort of a, it was a really good use case for us for many reasons. And then we developed a supplier diversity solution with the reporting, with tier two itself certification embedded into it. So we took, we definitely took a software. We're always, we've always been a data platform, but we take in sort of a software approach to be able to commercialize and sell and gain revenue and get customers on board. And that's been a big shift for us in the past year and as we're launching this new platform in a few months is that this new platform we've really doubled down on the quantity and the quality and the transparency of our data. And our customers now have bought all these different niche solution. Are saying to us, I don't want another analytics dashboard. I don't want another application. I want better data and I want more of it, and I want it to be easily distributed through my system. And so we've, you've, we've made a very hard commitment to not continue to build structured application to deliver on different use case. There's hundreds and now maybe thousands of, of new technology that are out and, you know, to service, these use cases. But what we've found, there's a huge un unserved sort of unmet gap in data quality and the quantity of data that's, you know, available to procurement organization, not just within procurement, but to the digital enterprise. And so that's what we focus on, but that's been, you know, the, the, the puzzle of building this company and figuring out how to commercialize and drive enough growth to be able to raise capital to, you know, so that that's been the journey that we've been on.

Tom Raftery: 16:40

So one of the challenges of this, obviously Stephany, has to be the data quality. How do you ensure that the data that you have is of a) quality and b) maintained up to date because as you mentioned, you know, the, the use case there of somebody having left the company six weeks ago, two months ago yesterday, you know, how do you, how do you keep all that up to date?

Stephany Lapierre: 17:08

Well, that's, you know, there's so much hype around AI right now as like, sort of more consumer facing or, or employee facing way to leverage ai, right? Because it collects a lot of information to give you answers to questions that you have, and, and it's gonna get better and it's gonna get more trusted over time. We've been using the technology to solve this data problem, right? How do you collect information and, and put it into a digital profile knowing that it belongs to that right entity? And then, and then the next stage is like, what attributes are we interested in? And then the next phase is like, well, what attribute is good right? Or maybe not as good. And so, you know, it's a lot of the magic of the technology that we've built, but for us, one, we have, you know, if you're counting websites, hundreds of millions of data sources, if you discount sort of the big web, it's still thousands of data sources in our library. And what we've done is we've attribute a trust score to every source of data that we collect, and that, that weights into some of the calculation. And then we've have another sort of way of calculating, using AI, the trust score on every single attribute that we pull into Tealbook. And so we've worked with our customers to understand if the way that we were calculating a trust score was acceptable to them, would they trust our high trust score? And so, and we're still gonna be something that we're gonna continue to iterate, but what it's done is it's enabled us to show transparently what we have a lot of confidence and an attribute versus other attributes that we're always gonna introduce new attributes. That we may only have one data source that we're unsure of. It doesn't mean it, it's wrong. It could still be right. It just means that we don't have enough signal, enough validation on the data to know that it's trusted. And that, you know, that doesn't exist. That sets a new gold standard in our industry on data quality, because if you buy data from any other sources that it's done in Bradstreet or Moody's or whoever, there's no way of knowing what data is good or bad. And so you have to, when you audit the data, you have to verify the entire file. You get audit everything. As with Tealbook, you're gonna be able to choose either based on spend or the relationship or the size of the company, or the trust score or the type attribute. Where do you wanna spend your time? cause what we're not promising is perfect data across every attribute, but what we're giving you is a trust score to guide where do you need to focus the effort. In some case, we have clients who won't even accept our a high trust score because they're reporting to the SEC and they need a hundred percent accuracy. And so they may still want to do a validation of the data, but once it's validated, there's a feedback loop, it comes back validated and then it meet their highest, you know, threshold to be able to then integrate that data into their general ledger, their master data. But there's a lot of other use cases where they don't need the highest trust score. Like let's take sourcing. You know, they may need 25 to 45 attributes around the company to enable sourcing and have more information. But as long as it's directionally correct in some, like, if there's a ca capability that's not quite right, you know, or the person is gone or, you know, whatever, the, the, the attribute may not be at the highest trust if you, if you don't reporting to your regulators and there's still a process after to be able to do diligence, you may accept more of a medium trust score. In some cases, our clients want all of our data in their BI tools or their data lake because they're manipulating it differently on their end, but it's, they're having data, and data is better than no data, right? So they're, they prefer be able to ingest it, but the concept of being able to match our data to the right entity in milliseconds, which is unheard of. To be able to attach it to the right USP, the Universal Supplier Profile, fill it with attributes and give a level of transparency that customers have never had before is a huge differentiator. And so, you know, yes, it's on us to continuously showcase that we're increasing the trust score across more attributes. So that's the forever investment. That we're introducing more and more attributes over time to be able to, you know, to cover most of the requirements that are needed across the entire supplier life cycle. And then our customers to giving them the control, right? And the governments to choose where, which data, what tolerances do they have for trust and where to direct the data based on the use cases. And they have a lot of that flexibility in our platform. That's a software piece, which is a lot more sort of, you know, how to. How to set sort of your business rules, right? And, and, and your how you're gonna direct the data into your systems and tools.

Tom Raftery: 21:41

Okay. And you started off doing kind of contact information and then supplier diversity. What else? You know, What are your customers? What kind of information are they looking for, and where do you see it going?

Stephany Lapierre: 21:58

Yeah. You know, it's our most interesting clients in some ways are ones who have tried to embark into a data foundation journey themselves, , because they've built the infrastructure, they've, they've implemented the data lake, they've built the connectors to their systems, with the preconceived notion that if there's more data coming from their system into their data lake, they'll be able to then hire data scientists and do something with the data. And I think that the misconception is that the data in your system is not good and there's not enough of it to give you the information that you need to have a full view of your suppliers and be able to use those types of signal to validate the quality of the information. And there's not a lot of logic built to make that more automated over time and more efficient. And so you're left with a gap and the gap will be, well, you need data operations people, you need CDAs. You're gonna need the third party data sources. You're gonna be to connect those data source to, to your data lake. You're still gonna need to export your vendor map right? There's an enormous amount of of time spent, and we had a client coming to speak to our team a couple of weeks ago who described, this is a Fortune 100 company, very sophisticated, who has taken now in a five-year journey to build their own data infrastructure who have chosen to partner with us because it was so difficult to do, and we're talking about very basic, you know, getting your vendor master deduplicating it, matching it to the right entity with the right name, the right tax ID number, the right physical location, like we're talking very basic. Just that alone, they're spending a million and a half a year so that they can get a spend reporting that they trust with confidence. And it's still a recurring event that happens every two to three weeks.

Tom Raftery: 23:47

Wow.

Stephany Lapierre: 23:47

That should happen in real time.

Tom Raftery: 23:49

Hmm.

Stephany Lapierre: 23:50

They should, they should be able to access a spend analytics or spend reporting in their own BI tool that they have confidence to with the logic that doesn't have to be done over and over and over again. And so we're talking very basic information at first. Obviously we we're, we're with this new platform. we've paired down a little bit the number of attributes. So, in our legacy platform, we had about 75 to 80 attributes on average across 6 million companies. We're starting a little bit slower with the new platform 'cause we're re, we're building the logic to then be able to fill the lake faster and expand much quicker. So we're launching with 46 attributes and there, there are 46 attributes that you need across your lifecycle. At baseline. And then we've added, because we're really good with certificates, that it's diversity certificates, we're introducing iso, you know, quality, safety, sustainability certificates as well. And then we go into more complex over time that it's, you know, financial data. Risk is a beast. And so we have elements of re risk, but that's an investment that we're gonna continue making. You know, we're talking about also some really proprietary information that over time we're gonna be able to expose, like benchmark, trends of risk based on spend being lost across suppliers, across different markets, to suppliers that are startup, that are gaining spend across different markets that may be highlighted as a small startup company that's bringing innovation that you should be aware of. There's a lot of and there's a lot of proprietary data that we can release over time, but our mission is how much data can we release as fast as possible with the highest quality? And our customers who have been tremendous partners are waiting in quite heavily in how we prioritize a roadmap and every attributes of project. Right. Is it, you know, are we building the attribute by deduction? Are we finding a data source? Our customers have data source as well, and we want them to contribute to our data roadmap by, ideally in a future self-service. You know, if someone is crawling some data source because they need this attribute in some, you know, of their reporting and they have to do this to date with a crawler, they have to import into Excel, they have to manipulate it. They will be able to just add the data source through a pipeline do an automatic refresh. It's gonna add to their U S P, and it's gonna contribute to expanding the quality of our, our data and, and the quantity of data that we have. So, you know, we have a pretty robust data strategy. For us, our product is how can we make these digital profile, you know, increasingly better. And introduce logic that makes it easier for customers to, you know, match it to their own taxonomy, to their own entities. How they wanna structure the data, how do they wanna see the data? Where do they wanna see the data to consume it? And so hopefully that that answers your question.

Tom Raftery: 26:35

Well, yeah, the, I'm thinking as well though, there's a lot of new legislation coming up. There's the, you know, the German, due Diligence Act and there's a similar one that's passed in Canada recently looking for forced for looking to make sure there's no forced labor in your workforce. There's a new. EU Deforestation law, which is coming into effect next year. There's a lot of legislation around climate and climate reporting, for example some of it's already rolled out in some geos, some more is coming down the line. I I, is this something that your customers are looking for as well?

Stephany Lapierre: 27:10

A hundred percent. I mean, the, it, it, it's nonstop if you're, if you operate globally, it's very, very challenging to keep track of all the different legislation. And access the data that you need to be able to report and be compliant, right? And so, you know, that's a perfect use of AI and, and, and understand and automating, you know, those rules, those, those requirements, those data attributes that you need to collect, to refresh, to report, versus having to buy software to solve for each of those legislation you know, you're gonna end up with 300 different niche solutions that are gonna address different component of it. Meanwhile, everything leads back to a company. So if the information about that company can be collected and automated, and accelerated, everything else sort of becomes more possible. You may still need some workflow because we're not collecting data that suppliers have to produce, at least not today. And so you may still need, you'll, you'll need an onboarding tool. You'll, you may need this other tool that you know is collecting information that can only be available if suppliers are providing it, for example. But all the rest, the 75 to 80% of the information that can be automated should be pre-populated. It should be consistent with all your other systems, and it should come back to a centralized place where it gets leveraged and optimized and democratized across all the different, you know, functions of the organization. And a big use case for our clients is also on the sell side. What information did they have about who you know, the company does business with that they can leverage for building pipeline, for understanding spend to negotiate, but also to understand the impact of their supply chain to make themself more competitive in an RFP? And where do they get this data today? It's very challenging to access. You dunno where to go. It's not easily published, it's not easily accessible. It's not per supplier. It's not in a place that can be just integrated into your CRM systems. And so there's a strong use case to sort of have that that full view for both sides and connecting revenue to spend in a much more intuitive way.

Tom Raftery: 29:09

Okay. Do you have any big kind of customer success stories you can share to kind of explain to people listening, you know, what practical benefits can come from this?

Stephany Lapierre: 29:24

I mean, yes. So we have a lot of use cases on the legacy solution because our customers at baseline have been able to get their diverse spend, you know, Some, I mean, I, if customers without a solution like TealBook would do this on their own, we've heard it takes a year, year and a half. We have customers who even just to get their baseline to start building spend reporting that will enable them to move faster you know, we're talking about two to three year projects. We have customers who bought BI tools or SLP from Ariba that's been sitting around. They spend half a million dollars on a module that's sitting around because it can't be implemented until they have data. And so there's a lot of reasons, there's a lot of catalyst for why they need access to better information. We have, you know, there's a story of Jeff Wright at NASDAQ that talks about and he's, he's talked this publicly on, on stage, and it's recorded so I can quote him, but his CEO came to him five years ago and asked for spend with a specific company that they do business with and the procurement team couldn't produce the information on time for the CEOs to meet. I was like, this is completely unacceptable at this time. And you know, and the journey of our organization that we can't, and that's because they had 36 instance of the company in different systems. They couldn't easily reconcile the data. So there's always a catalyst of something that our customers are trying to achieve. And then are more progressive. I'd say clients who are five companies that we've partnered with on this new platform, and that's Goldman Sachs, Nasdaq, Freddie Mac, Albert Marle, and Standard and Poors have put, you know, invested, you know, money. They've invested time with our product and engineering team because they fundamentally believe that having a data foundation is a true key to digitizing the procurement function and achieve digital procurement success. And so we've been really, really fortunate to have worked with those companies. And in each of their case, the use case is a bit different. And so in an example, like a Nasdaq, they've built a data lake, and so they want more data in their data lake and they wanna be able to automate all the work that we've talked about of like matching, you know, the right suppliers to the right entity just to produce a spend reporting. And so reduce the cost, optimize their processes, make data easily accessible and trusted. We have clients who are moving from you know, one ERP to another ERP. And so they wanna be able to build a data foundation, not dependent on the ERP because the data gets stale in an ERP. really want the data to be dynamic. To have more trust and then expedite the time to value of their ERP and keep the data refreshed. They also have bought BI tools that they wanna continue to optimize. And so they also want a feed into their BI tools. And a lot of our clients are kind of looking at how do we centralize all of the data into our Tableau or Power BI versus having all these different niche solutions with different analytics. Right. And so, I mean there, there's a lot of stories. You know, and, and, and we're really looking forward to make the future platform more tangible from a full data foundation, because a lot of the use case we have are very specific to, you know, attaining a hundred percent more diverse spent over, you know, in a matter of even sometimes weeks to the first year to be able to drive, you know, what value does it add? Was it for funding reasons? Is it for, you know, hitting a certain target that they've made a commitment to their shareholders and, and, and, right. So hopefully that helps. But data has so many, so many use cases attached to it. It's almost, it's almost hard, right? You, each of our customer need to understand what is, what would, having quality data at scale would enable them to achieve and what are the priorities? And then our team will work with, you know, that customer to really define what's the business case, what's the RoI, what's the path to getting the data into that right systems or tool to enable that outcome to be achieved and then measure it.

Tom Raftery: 33:15

Okay. And where do you see all this headed? Because we've had a lot of changes in the last couple of years in your customer's requirements for data. You know, the, the kind of things we mentioned, the climate, deforestation, supplier diversity, all these kind of new things that have bubbled up in the last four or five, 10 years. What's gonna happen in the next four or five, 10 years?

Stephany Lapierre: 33:43

So my view is, you know, one of the biggest awakening was covid because suddenly companies who've implemented S2P systems, who had maybe some visibility into their spend, Suddenly spend became, it's still important, but it became less important. All the other information, about their suppliers, right? To be able to have contingency plan or be able to diversify their supplier base to mitigate their risk, to find alternative suppliers, to shift their production or stay in business or keep their employees safe. That data they did not have. It, it paralyzed a lot of organizations because they couldn't access it. And that was good for us.'cause that allowed us to grow really fast. Then Black Lives Matter happened and suddenly everybody cared about supplier diversity in, in industries that were not regulated. Think about tech, about retail. Those are companies that never had to report to their regulators, but suddenly they wanted to make a statement. They want, you know, the shareholders were asking. The The board was asking because they wanted to be able to make a commitment to having more diverse supply chain to showcase the diversity of their employees and, and, and consumers, et cetera. And suddenly everyone's putting ESG targets, right? Oh, that's the next thing. Oh, let's buy. All these other ESG solutions. Suddenly recession, right? Like there's a whole talk about like now inflation recession. So now it's cost savings, it's becoming more efficient. Then there's... it has not stopped and it will not stop. And if you're solving this with software, you're just gonna keep buying an endless amount of tools that you're gonna implement to satisfy all these different requirements versus thinking if I had good data at baseline, if I had more visibility at scale of the suppliers I do business with and the suppliers that I should know about, and I can have easy access to this through my systems and in the hands of people, they can move faster, right? They can access the information that they need to satisfy their regulators. They can, you know, . Build a contingency plan and act on it because they have access to the information. They can drive better strategies, they can influence business decisions in a more impactful way. They can do less tactical work. So focusing more on strategies and building plans, right, so that the next disruption happens. They're better equipped. Like, I mean, I, I, I, I'm a big proponent of data obviously I'm, I'm pretty biased on it, but that's because I've seen it cripple organization and, and the the, the power or the opportunities to think data first and make that accessible so that you're spending less time of the doing and more time of the impact. is so tremendous and because it's not gonna slow down, it's now. Now it's the time to start building that foundation. Right? Because if you keep doing this through software , It's just gonna, you know, it's gonna, it's not scalable. It's not giving you the flexibility, it's not interchangeable, it's not connected. You're just creating more disparate sources of information that different teams are using, and you're doing a ton of duplication of effort versus having more of a centralized function with a centralized data source that enables all these different teams to move faster and more coordinated and more alignment to drive, you know, your business objectives.

Tom Raftery: 36:53

Cool. Cool. We're coming towards the end of the podcast now, Stephany, is there any question I didn't ask that you wish I did, or any aspect of this we haven't touched on that you think is important for people to think about? I.

Stephany Lapierre: 37:07

I maybe like where to start, right? This is, you know, I'm painting a pretty amazing picture, right? I'm like, oh, that would be, you know, to have access to, and it, and what I would say is that it's possible. Because of the technology today. It was not possible five, you know, even five years ago was hard. Like we were building the data set five years ago is, there's a lot of challenge and how we're building it now is very different than five years ago because of the technology that's available and the amount of data that we can access. 10 years ago that was not possible. Right? The, the technology didn't enable it. We are moving towards a world that will be more interoperable. We don't have that yet. So companies have to figure out how to connect the dots between systems, or connect systems to each other, which is very expensive. So data's a really good way of thinking about connecting different solutions, different dataset together. I think you know when, where to start. I do believe it's just a matter of turning the light onto your vendor master. So just start by if you're enriching your vendor master, it will highlight some really nice low hanging fruits for any organization, especially savings is sort of the permission to do business. You know, examples where you have 36 instance of a contract with the same supplier with different MSAs and you may not be able to see it today'cause you're highly decentralized. There are some really good gems and gifts when you can see your vendor master with the light turned on and accessible. That's a first step. And then defining, you know, where, where do I have gaps? Is it a tool that I've bought that I can't maximize as well? Or is there a driver in my organization that, that makes me, that, that we need better data quality? It could be spend, it could be ESG, it could be diversity. There's a lot of other reasons why you may access, you know, good quality information, but it's that first win. You don't have to take it all in. You don't have to integrate into all your systems. A really nice path, right, right now for our clients to integrate into a BI tool or data lake, right? It's easier, it's, it's faster. Best practice right now we're seeing a lot of our customers implementing data lakes and feeding'cause it's not just supplier data. You're gonna have a lot of other data assets that you're gonna wanna feed into a data lake. And then instead of looking at buying multiple analytics tools, I. You know, leverage your, your enterprise license with Tableau or Power bi. Certainly, you know, there's, on the revenue side, they will have license. So if you're in supply chain procurement and you don't have, you know the tools yet, see what you have internally and be able to leverage that. And if the more you centralize these things, you know, you're thinking about ChatGPT and all this really cool tech. We're seeing a lot of software providers coming up with their own version of ChatGPT right now. And if, if you buy ChatGPT from Workday and ServiceNow and you know this other niche tool has a ChatGPT, you're now, you're gonna recreate the disparate siloed portal, situation, , because you're gonna have people using different algorithms. versus, you know, leverage the one on Power BI or Azure or whatever cloud technology you're using your Power BI tools because then you get to centralize and those models are just gonna get smarter, right? So again, less software. Don't get so caught up in the hype of like all the new things that are coming out. The new niche solution. They are, they are really cool technology. If you're thinking data first, you're not gonna be stuck with whatever solution you buy. It's gonna be more interchangeable, it's gonna be easier to implement. Anyway, but start somewhere where you can show value. I think that first win creates energy. People can see a path that's more tangible. They can see business outcome that are driven that would be very difficult to achieve, you know, with humans or in traditional ways. And that just, you know, gets people behind you and, and you need, you need and change any change management. You know, you need to create that kind of energy.

Tom Raftery: 40:47

Sure, sure, sure. Of course. Stephany, if people would like to know more about yourself or any of the things we discussed on the podcast today, where would you have me direct them?

Stephany Lapierre: 40:58

Yeah, I mean, we're we pro, we produce a lot of of content, so please follow Tealbook.com. You can register on Tealbook.com to get a access to our news and our resource center. We're also very active on LinkedIn, and I'm personally active on LinkedIn. So Stephany, what a y. So if you wanna hear, you know, and I usually apologize for people following me on LinkedIn because I do write a lot about technology, data, supply chain, procurement, et cetera. I do have strong views, but I also have created a lot of conversations on LinkedIn. So if you wanna be part of the conversation, if you wanna learn, if you wanna hear best practices from our customers, if you wanna hear from our customers, we're quite vocal. I, I encourage you to, to, to follow us and reach out directly. I'm always happy to have a good conversation.

Tom Raftery: 41:39

Sure. Great. Great. Fantastic. Stephany, that's been really interesting. Thanks a million for coming on the podcast today.

Stephany Lapierre: 41:46

Yeah, thanks for having me, Tom.

Tom Raftery: 41:48

Okay, thank you all for tuning in to this episode of the Digital Supply Chain Podcast with me, Tom Raftery. Each week, over 3, 000 supply chain professionals listen to this show. If you or your organization want to connect with this dedicated audience, consider becoming a sponsor. You can opt for exclusive episode branding where you choose our guests or a personalized 30 second mid roll ad. It's a unique opportunity to reach industry experts and influencers. For more details, hit me up on Twitter or LinkedIn or drop me an email to tomraftery at outlook. com. Together, let's shape the future of the digital supply chain. Thanks. Catch you all next time.