Data Management Is a Dirty Job But Everyone Has to Do It

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Hey, glad to catch you no matter what you're in the middle of doing today.
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Just uh thrilled that you decided to take some time out and hang with us.
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I'm Melanie with ISM, and yep, another episode of Supply Chain Unfiltered.
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Today we are focusing our efforts on data.
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And I would say a few years ago, you know, a lot of companies were looking for data analysts, people that specialize in data.
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And now it seems that almost no matter what your role is within a company, you're expected to be able to manage data, to analyze data, because it's at the crux of everything.
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I want to share something with you I ran across before I introduce our guest.
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It's a 2025 study by Precisely and Drexel University's Labo College of Business.
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And in the study, they found that 76% of the participants said that data-driven decision making is their number one goal.
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But here's the here's the other side of that.
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Of those participants stating that, 67% of them said that they don't trust data.
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Yeah, that's a problem, right?
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So uh we're gonna kind of head into everything data, and who better to chat about it than uh guest Susan Walsh.
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She's the founder of the classification guru and a longtime friend of ISM.
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So good to have you here, Susan.
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So great to see you.
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How are you?
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I'm I'm great, I'm great, you know.
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How could I not be great with you on the show?
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Um true.
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And we're talking on Friday afternoon, so that's always, you know, we're cool, we can sniff the weekend.
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Yeah, yeah, it's just the teaser, the teaser is here.
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So um I want to kind of set things up a little bit, uh, you know, before I let you kind of dive in.
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But when you think about it, if we all look at how we're operating today compared to five years ago, and I don't need to spell out what happened five years ago.
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We all know and we lived it.
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Um but I'm wondering with all the things that have changed in the last five years, uh it still seems like thing, you know, I I'd say operationally speaking, and data has a lot to do with it, things are kind of out of control.
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I don't know if it's an again or just kind of continuing on.
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But how should businesses be revisiting and focusing on their data today and what, if anything, has changed since five years ago?
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Yeah, a little bit.
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Actually, coincidentally, it's our eight-year anniversary for classification guru this month.
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So I've just uh scheduled up and built a post around what is different now from eight years ago.
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So even rolling back a little bit further, procurement analyst wasn't a job back then, it didn't exist, like a lot of the spend analytics was all outsourced, and now that's all in-house.
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Um, and so when they took a lot of those skills in-house, there's very qualified um and capable data analysts who can do amazing coding, they can do fantastic scripts and AI and dashboards and analysis, but nobody really gets trained on the cleaning data part.
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And you know, I speak to people, and this is this is a conversation that's been going on since we were paper-based, like pre-computers.
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Like data quality standards have always been an issue.
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Um, unfortunately, something that hasn't changed in all that time is the focus and priority of it.
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I think it's kind of seen as something less important, almost menial as a task, but actually getting someone who is skilled in it, you know, can really improve and enhance your data quality really quickly to help you get to where you want to go.
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But all guess what?
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Like we're going around in circles, you know.
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There was new tech like five years ago, and it needed clean data to work.
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All these like spend analytics tools out there, all that work, it needed clean data to work on.
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Now we've got AI, gen AI, agentic AI.
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Guess what?
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It needs clean data to actually work properly.
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Nothing has changed.
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And and I really hope, and I am seeing people talk more about data quality now.
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So I think maybe this might be the turning point with with the use of AI now.
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Yeah.
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But we'll see.
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Yeah, and we'll get to AI a little bit in a little bit, but I wanted uh um get your thoughts on tariffs.
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And and um, you know, a lot of we're relocating some of our supply chains for various reasons, but um so I'm wondering with with those two things going on, is it a good time to upgrade our data systems, or is it better to hold off until some of this geopolitical dust settles?
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This is the best time.
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I have literally had projects put on hold because of the tariffs, and actually that's the worst thing that could have happened because you need accurate, up-to-date information and real-time visibility to be able to do like modeling on what-if scenarios, um, forecasting for the future, looking at historical information, um, making decisions based on that information.
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How can you compare your data if it's not accurate or you don't even have that information?
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So, like really you are you're on the back foot before you've even started with the tariffs if you don't have clean and up-to-date data.
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It's going to be a and it might not be a problem that people realise until it's too late as well.
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You know, it's one of those things you really need to be on top of.
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And again, it's nobody, apart from the people who really are in the weeds and do it dealing with it, and the the more senior levels of management don't seem to appreciate the the importance of investing in your data quality, and it really is an investment, it's not a cost.
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And I have a question.
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I'm wondering, you know, with that kind of mentality or prioritizing those kind of decisions, is it that people have an aversion to doing the data cleaning because they'd rather just, oh, it'll be fine and we're just going to move forward and um focus on you know integrating um, you know, the innovation that has all the bells and whistles, but they don't take care of the fundamentals first.
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Is that is that what goes on?
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Well, I I think there's definitely uh uh thought around that it's something that's really easy to do.
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And the reality is we've helped clients shave months off of projects.
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Um, you know, something that was taken one of our university clients a year to do, we could do in like three months for them.
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Oh like what is the value of that to the business?
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And and that isn't something that they could have just done themselves, it was taking them a year because they have all the other things to do too.
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We do this day in, day out, and all the other suppliers out there that do it, you know, that that's really important.
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And then the other thing is I think that most people think their data is so bad and it's such an overwhelming task that they're like, you know, la la la la la la la.
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No plus, you know, let's just pretend it's not happening, let's just keep moving forward.
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And and I can tell you now that nine out of ten clients that we work with, something has gone wrong massively.
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There's trouble, and they need to fix it yesterday.
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And that's the only time that the money actually becomes available to clean the data, which is such a shame because it should really be about being proactive, not reactive.
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So that must be a lot of, I would imagine, a lot of stress on you because your clients, uh, I would imagine many of them come to you in fire drill mode.
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Yeah.
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Yeah.
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Um and you have to like, I mean, with me, what you see is what you get.
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I'll be honest.
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If it's gonna take, I mean, our longest project normally takes about four months, but if it's gonna take four months and not four weeks, I'm gonna let you know that.
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Um, I always try to over overpromise and underdeliver.
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No, yeah, that's the one.
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Um wait, and you you overpromise and under deliver or the opposite?
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No, over underpromise and over-deliver.
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That's the one you mean.
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Yeah, yeah.
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Let's go with that.
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I know.
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But yeah, so basically, I will always add in buffer time to each project because I know things can go wrong.
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So nine out of ten times we can deliver before the the actual estimated finished date rather than after it.
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So try to manage the expectations of the clients and be honest with them about how much work is actually involved.
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And sometimes it's about doing like a little proof of concept and showing them like the difference between what their data is and what it should look like.
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And sometimes that's the only time they kind of realize that they've got an issue.
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Yeah.
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Oh, yeah.
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Aha moments are good.
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Um, yeah.
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So also, sorry, no, um, just one more thing.
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So a lot of people will kind of maybe cheat and look at their top 80% of spend or suppliers or you know, their top top companies or top products or whatever, and and if they're looking okay, then they think, okay, that's fine.
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But actually, in that bottom little bit, the tail, there is so much trouble hiding in there, you really need to check it and be thorough.
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I and so I wanna I wanna kind of head back into AI for a minute and and tech integration.
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And with all these things going on, um I I imagine it's hard for decision makers to kind of decipher where they should put the spend on this type of uh on the innovation.
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And because there's all these new products, lots of new capabilities, they all roll out so quickly, and you don't want to invest in something that might end up being antiquated sooner than later.
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Um so in that vein, is there one consideration that you can offer up um that businesses should take a look at before they shell out those dollars?
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Absolutely, and that is is it fit for what the purpose that you need it?
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So I hear a lot about uh with with clients we speak to who have committed to a three, four, or five-year deal with a with a company, and then they're actually paying for like half of these add-ons that they don't even need.
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So I think it's really important before you even set out looking at suppliers, look at what your end goal is.
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What do you need to achieve that?
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And and try not to fall into the trap of being sold, you know, this magical tool that's gonna do everything for you.
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It doesn't exist really.
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And what is right for your organization might be very different to the next one, to the next one, like don't just follow what your competitors are doing.
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That's not gonna work either.
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Good point, good point.
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Um, do you think part of that too is where you end up um committing yourself to more, I'll say more tools than you need.
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Could that be uh in a way where a company's looking at their longer-term goals and they think that, hey, you know what, if we invest in this now, we're kind of ahead of the game because we'll be ready once we reach this goal and this one and this one.
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So they think they're being smart, but what if, you know, as we know, things happen, they don't always go according to plan.
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Most companies I speak to are not thinking of the end goal, they're thinking of the we need to fix something right now.
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And so the first conversation I have is let's not just think about the right now, let's think about longer term.
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What can we build in now that can be useful for you in the next six, twelve, eighteen months?
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And I think that is where some of the problem lies too.
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When you're having these discussions uh with providers, they're telling you all these solutions and problems they can solve.
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But do you have any of those problems?
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Is that actually what you need?
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Like, what do you need to be successful in the next three to five years?
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Will this tool help you get there?
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And I don't think most organizations are thinking like that.
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And regarding AI and all the iterations of AI, yeah.
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Um do you think businesses should still be exercising caution?
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Oh, absolutely.
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Um, more important than ever, every single piece of AI that we use, whether it's in the home or at work, is built on training data.
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So that training data has come from somewhere else.
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So it has to be clean, it has to be accurate, otherwise what the AI is learning is not right and could actually be contaminating and populating your data with misinformation.
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And if you've got your own in-house model, that's great because you know exactly where the data's come from.
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You know, you'll know if the quality is good or bad and and and be able to judge it.
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But what if that information has been sourced from the internet?
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Where has that come from?
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How can you verify that?
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You know, you really need to be able to sense check it and make sure that you know it kind of looks like what you expect it to in the areas that you are familiar with.
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So get an expert in a specific area to have a look at the data and say, does this look right to you?
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Intuitiveness is a good thing too, right?
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Yeah.
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And just experience, like we we say it all the time.
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We we will spend a day on a client's data, and by the end of that day, we'll start to know where things don't look right, numbers might be elevated or lower than they should be, or the description doesn't really tie in with everything else around it.
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So, you know, it's it's a a good skill to have and to use, but definitely.
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So as we uh work together and we become more integrated across the supply chains, and we get that greater visibility and transparency that we all want, is it at the same time making data management more complex?
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Is there more risk in them?
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Um and and something that that I talk about is my data quote.
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So making sure your data is consistent, organized, accurate, and then it's trustworthy.
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And that C, that consistency is like really important.
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So if you're working with a global data set, is is the language all all uniform?
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Are we all using the same units of measure, the same date formats?
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Um, I think sometimes we make it more complicated than it needs to be by not defining standards and and again thinking about what do we need from not just our department or business office or but the company globally, each country, you know.
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Um, and so that's where the problems start to arise.
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And because you there's tools out there that can standardize all that data, but if you still have people inputting and it's wrong, that's not that's always going to be a problem.
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So let's say we get to this place where we've simplified data management and um we're looking to through that to amplify our performance, that would be ideal.
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But in between the time where it takes to get to that state, uh is that comp is that goal compromised because not everybody is operating from that same foundation that you just mentioned?
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Where um and is that going to take more time?
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Like how how long is it gonna take before we're all in that same same place, I'll say, of data nirvana?
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Well, that's a whole change management piece.
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Yeah, that's and that's a people problem.
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So I do say data problems are people problems on the whole, and that is because you know, in especially with procurement data and supply chain data, you can have more than one right answer.
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And again, it's about being consistent, having the same thing.
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And if you have two people who are arguing because they're both right, and one is always going to input data as this A, and one is always going to input it as B, then you're gonna have problems.
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So it's about communication, education, um giving them responsibility as well for the data that they're managing and and telling them how it impacts the rest of the business when it's not right.
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Yeah, it absolutely can like hold things up, and and it depends on the type of data, it depends on within the organization, like you know, how important is that piece of part of the data to the rest of the organization?
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You know, if it's I've tried to think of something that wouldn't be important, but uh oh, it's all important.
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I don't know, someone's shoe size, right?
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Okay, so you need a shoe size, someone's shoe size for I don't know, uniform or something that that's not gonna be as important as uh categorizing your supplier correctly, giving them the right GL code, um, giving them a correct bank account and head office number, tax ID, those kinds of things.
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So, you know, it's gonna depend as well.
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Okay.
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So I know you know everything, you are the queen of data.
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So I want to know, and I want you to share with us is there something going on that we should get excited about?
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Um, what's coming?
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You know, maybe there's a specific program or methodology that's gonna make our lives easier, uh, something you're working on that you want to share.
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Well, I mean, I'll start with me because why not?
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But um, I have built a tool called Samification, and at the moment it normalizes supplier names.
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You literally sign into the website, drop your file in, and within anything from 10 minutes to two hours, you get a file back depending on the volume.
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Nice, and that's based on a 400,000 supplier master list that we have, so we match everything.
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Anything that doesn't match then goes through a set of rules and our own chat GPT model.
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So it's getting really good, but very exciting.
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Um, in the next few weeks, we're going to start launching an add-on, which is two-level generic classification per supplier, just as uh a bit of information.
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But I know the thing that the procurement are asking for a lot is the parent company information and not from a finance point of view where they need to know like from a risk perspective, but more from a we just need to know who the parent company is so we know who to go to negotiate with at the top level.
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So we're going to try to work over the summer and try and build that in as an add-on as well.
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But I think us aside, I think agencai is really gonna take over and dominate in the next 12 months.
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It's already all the events that I go to, people are already talking about it, but I've seen some amazing use cases already.
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And I mean, literally the only thing that's stopping you is your imagination with these things, you know, it can it's gonna really help streamline a lot of processes and take away some of that manual effort of just you know pulling it from this spreadsheet and putting it into this spreadsheet, and then it might make a decision, you know, it can make decisions and things if you put the right information in.
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So again, it's all gonna come down to accurate data, but it could truly transform all our lives.
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I mean, I'm thinking about how I can use it more in my business.
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Hmm.
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That's pretty exciting.
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Uh it's and you know, it's it's super important to stay connected to the people that can share these updates, like yourself.
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Uh I I I guess for uh for anyone um tuning in that um wants to connect with you, if you haven't signed up for Susan's newsletter, you still have that going on, right?
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I do.
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Okay.
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It's always really chunky as well.
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There's always a lot going on.
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So if you'd like to uh get connected with her newsletter or want to just you know have your own conversation with Susan, how can they do that?
00:21:24.799 --> 00:21:28.960
LinkedIn is the best place for me, Susan Walsh, the classification guru.
00:21:29.200 --> 00:21:34.960
Um, if we connect or if you follow me, you'll get an invite to the newsletter so you don't even have to do anything.
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Um I you can go to theclassificationguru.com and have a look around, samification.com.
00:21:41.759 --> 00:21:44.880
Um, and yeah, we answer any questions.
00:21:44.960 --> 00:21:49.200
You my email address is on there too, Susan at theclassificationguru.com.
00:21:49.279 --> 00:21:49.680
So yeah.
00:21:50.160 --> 00:21:53.519
So, so glad that you were able to carve out some time for us.
00:21:53.680 --> 00:21:55.599
Uh really great to catch up.
00:21:55.680 --> 00:21:57.359
And um that's great.
00:21:57.920 --> 00:21:59.680
And I'm so jealous of your offer.
00:22:02.160 --> 00:22:03.839
You put mine to shame.
00:22:04.160 --> 00:22:10.640
Oh well, um yeah, but you know, at least at least I'm I'm venturing to guess where you are.
00:22:10.720 --> 00:22:13.680
It's not, it doesn't get to 115 degrees out.
00:22:13.920 --> 00:22:17.759
I don't know what that converts to in Celsius, but let me tell you, it's hot.
00:22:18.480 --> 00:22:24.160
Um, it's well, you say that it's actually we're having our hottest day of the year today.
00:22:24.400 --> 00:22:24.799
Really?
00:22:25.039 --> 00:22:27.599
But you guys, you guys have nailed the aircon.
00:22:27.839 --> 00:22:32.799
We don't really have aircon here, so like tonight it's going to be sweaty for me.
00:22:33.119 --> 00:22:33.680
Really?
00:22:33.839 --> 00:22:34.160
Okay.
00:22:34.400 --> 00:22:34.640
Yeah.
00:22:34.880 --> 00:22:37.200
Well, go get yourself a cool beverage.
00:22:37.440 --> 00:22:37.920
Thank you.
00:22:38.079 --> 00:22:39.039
Thank you for hanging out.
00:22:39.200 --> 00:22:40.240
Really appreciate it.
00:22:40.400 --> 00:22:46.319
Um, to catch any of our shows, feel free to check out the library of content, ismworld.org.
00:22:46.400 --> 00:22:57.599
That's where you'll find it, along with a lot of great uh resources to keep your business in the know and on track to achieve the goals that uh you're looking forward to doing.
00:22:57.920 --> 00:22:59.039
Thanks for tuning in.
00:22:59.200 --> 00:22:59.759
Always a pleasure.
00:22:59.839 --> 00:23:02.079
I'm Melanie Stern for ISM.