Fabian Heinrich:  Hello everyone, and a warm welcome from my side, to another episode of procurement unplugged. It is a true pleasure to have today Susan Walsh, the CEO of the classification guru.

And who is truly mastering the holy grail of data, which is really a mystic secret to most of the people working in procurement? So I'm truly excited to have you here, Susan.

Susan Walsh:   Thanks so much for having me on; I am always happy to talk about data, and especially in procurement.

Fabian Heinrich:   So Susan, how did you end up first and foremost in procurement? And then what was your path later on towards building the classification guru?

I think that's a truly interesting story, and I think our listeners will be really interested to hear it.

Susan Walsh:   Yes, so it's one of those things. I guess well, now it's a happy accident, at the time, it was not so much happy. I had my first business was a women's clothes shop here in the UK, and it didn't work out.

And I was desperate for some work; I would do anything to pay my bills. So I found an ad online for a spend analytics company, and so I went away to, spoke to them, they said oh, we need data classified.

I thought, well, I've worked in a few organizations, I'm sure I could do that, and so that's how it started. And after five years with them, I ended up managing a team of 14 people, and I was managing projects, and I could see that the clients were paying for this expensive dashboard and fancy analytics.

But the real issue behind it was the quality of the data; that's what we were spending most of the time fixing. But nobody was talking about it; it's like a dirty little secret you don't talk about.

And so I felt like I'd got as far as I could with that company, and I hadn't come from procurement or a data background, so I didn't know where I could get a job doing the same thing. Somy only option really was to set up another business.

So that's where the classification guru came from, so I've just hit my fourth birthday. And that in itself was hugely challenging because the procurement people that I was speaking to all thought it was a great idea my business.

But the reality was they maybe didn't need my services right away, or people weren't looking for me because they didn't know that I existed.

And so I've spent the last four years really building a presence, particularly on LinkedIn, and I'm really establishing myself as the fixer of dirty data and the expert in my field. And that's kind of how I ended up talking to you today, I guess.

Fabian Heinrich:   Amazing, yes, that's quite a journey. I think a fixer of data is also an interesting term. I mean like is there something, what really inspires you about procurement, and also that particular data topic?

Because I mean over my years in procurement, like I've found the same thing what you were saying that no one wants to talk about the data.

And then if the player data is portrayed the wrong way, the standard excuse is always shit in, shit out. So how did you get so excited about that topic nobody wants to look at?

Susan Walsh:   I guess I saw an opportunity because nobody was talking about it.But genuinely, I love the work that I do. And for me, no matter what task I'm doing, I'm always looking to improve my processes, be more efficient, work smarter, save money, increase profitability.

And the really easy way to do that is investing in your data quality; you can save so many people hours in a week just by having clean data. You can find cost savings; you can drive profitability on projects because you don't have people spending as much time fixing bad data or looking for information. It just makes so much sense to me, and so I'm just trying to spread that word.

But of course, the reality is it's a boring subject, and a lot of people within data who are immensely talented and highly skilled, and very intelligent don't necessarily convey to the business world the importance of data. And so, I'm trying to bridge that gap.

Fabian Heinrich:   Yes. And I think data also is related to taxonomies, and maybe you can elaborate a bit more about that topic. I think it's quite an emerging topic which has been overlooked as well for many years.

And I think, of course, everyone in the industry knows there's the Nike's code and the UNSPSC code. But maybe you can tell our listeners a bit more about that topic, which is, in my opinion, heavily related to data.

Susan Walsh:   Yes. So I'm seeing a huge shift away from taxonomies like the UNSPSE because they're so big and so detailed and don't necessarily fit the needs of the business. So I would say bar I think one client, maybe two, over the four years, I have built customized taxonomies for my clients.

And normally, it's about four levels, depending on the level of detail. Now your taxonomy should fit your data, so what that means is if you have detailed information in your spend like pens, pencils, paper clips, little keyboards, mouse, then you should have that level of detail in your taxonomy.

But if it only says office supplies orIT equipment, then you only need a two-level taxonomy. You can't go into any further detail than that because it's not there. So you should have a taxonomy that fits that data.

And something that I find really frustrating with the UNSPSC is it's not chart-friendly. So when you try to put level one of the UNSPSE on a chart, it's bigger, the wording is longer than the chart itself. And so, keep things simple with your wording: IT, HR, professional services.

Fabian Heinrich:   I think that's a very good topic; keep things simple.

Susan Walsh:   Yes.

Fabian Heinrich:   So I guess I get asked a lot of times the question by our customers, how do you ever classify services? And they really struggle with it. So I mean,I might pass that question to you as you are the classification guru.

Susan Walsh:   Yes. So I would say most services, well it depends on the service, there are professional services, so that would cover your legal, your accounting, engineering, surveying. Consultancy can be a tricky one; HR and IT consultancy can either sit in professional services.

Fabian Heinrich:   Yes, I think to that level most of the people are easygoing, but then when it really comes to the capabilities, and to the kind of products a service provider can provide. I think then they strap it because you're kind of missing the product category then, yes?

Susan Walsh:   Yes, well, that's an important part of your taxonomy. You don't want to force it.

Fabian Heinrich:   It's more like a skill or capability they provide. I think here they struggle, and it gets tricky.

Susan Walsh:   Yes. And what can end up happening is misclassification because someone tries to fit it into something that happens to be there. I normally build taxonomies as I'm classifying the data.

So I don't start with a taxonomy, I add to it as I go through the data, and then at the end, if there are any items in the taxonomy that have, say, only one classification in the whole data file,I'll delete those.

But if you've got hundreds of or thousands of rows against one classification, then that makes sense to keep it.If it's one or two rules then, just move it into the next level.

And I think maybe that's the mistake that some people make, is they think they need the taxonomy before they start classifying. But not necessarily. If you know what you're doing, then it's okay.

Fabian Heinrich:   Yes, okay, that's very interesting. Because I mean also we ourselves, if we have own service provider, we struck it because I mean we see a lot of cases in our own company, but also with people like who are our customers.

That for example, the sales team is using a head hunter, and then they're not sure how to classify that, they don't know the name of the supplier whatsoever. And then it's just classified as sales, whereas it's actually a HR service, right?

Susan Walsh:   Yes. So great tip for you, and this is how I train anyone that works for me. When you have a data set, always think about; you see the supplier. If you don't know what it is, Google it, find out what they do.

And then think about would my company be buying from this supplier. So like you just said, it's a headhunter, yes the sales department bought it, but they're a headhunter, so that's the service that you would be buying from them.

It's the same with deliveries. So if you buy something from Dell or IBM, you might have a delivery charge on the invoice. And if that's one of the invoice line descriptions, it might accidentally get classified as a courier.

However, the reality is that delivery charge is part of the computer or IT equipment that you just purchased. You're not paying dell or IBM for a delivery service or a courier service; it's part of the computer. So anything like that, I would always train my team up to classify as IT, not delivery.

Fabian Heinrich:   And what are, in your opinion, the biggest challenges with regards to classification?

Susan Walsh:   Well, actually, I think first it's actually knowing where to start, and starting, I think that's a big challenge for a lot of people. I think they find it intimidating. They think that they don't have the time or the resources to do it.

And so, they settle for less than good data. They might have to use a GL code which is notoriously unreliable, and then they get a rough idea of what they're spending their money on, but now here near close to what they're actually spending their money on.

And then the second thing is for those that do have classification; they don't maintain it. So they don't look after it, they don't update or refresh their data, so it becomes out of date very quickly.

People can accidentally cut and paste, people change their minds. If nobody's managing that, then it becomes a big problem. Within a few months, you could be back to where you were before you had clean data.

Fabian Heinrich:   Okay. So I mean, the key challenge is the maintenance actually of the data and to keep that up to date. I mean, I'm always surprised, because I mean let's say in sales, like the other kind of side of the business.

We've been using, or people are using, tools like Salesforce since like 10 or 15 years. So I mean, I'm always wondering why the problem of data maintenance in a kind of single point of truth system has not been solved.

Because if you think about it on the sales org, there are many users, many people so should be kind of similar problems and challenges. Why we kind of not get the knowledge how they tack it or how they solve those challenges over the years, into the other side of the business.

Susan Walsh:   Well, I'll tell you a secret, another part of my business is cleansing databases like CRMs so Salesforce etc.

Fabian Heinrich:   Interesting.

Susan Walsh:   They are so messy. There are so many duplicate records for the same person. So a step back from that is actually training people within the organization, and it should be anybody that works with data, not necessarily data people or procurement people.

But anybody that is using a spreadsheet or a CRM system should be having some kind of data quality training. So that they understand the importance of why it needs to be cleaned.Because otherwise, they just keep setting up new accounts everywhere.

I see it with suppliers as well. You can have five versions of PWC, Price Waterhouse Coopers, PWC, P W. C it goes on and on and on.

Fabian Heinrich:   Yes. I mean, I hear a lot about in the market, I mean also when I was in scalping now with Mercanis like self-service procurement, autonomous procurement, and kind of those buzzwords.

But I mean, I am always asking myself,I mean, does the foundation not need to be clean data? I mean how to achieve autonomous procurement without clean data. So it would not be the first step be something like cleansing all the data.

Susan Walsh:   Absolutely. But the thing about automation is it's great if you have clean data to start with, and it can learn from that. But you should alway shave an experienced person in that area checking the data.

Because if you just trust the automation, it might be auto classifying something wrong. And it could do that for months or years before it's picked up. But again, if you're doing your maintenance and you're checking, then that would get flagged, and it would avoid a lot of those errors.

Fabian Heinrich:   So if we look at a journey kind of, like if we kind of could paint the future to autonomous procurement, like you would advise basically any procurement organization to tackle first the data classification, then the data cleansing.

And from that, basically starting to automate things, in order to achieve at some particular stage autonomous performance.

Susan Walsh:   Yes, but don't blindly trust the automation. It always needs to be checked. So yes, it will do 98% of the work, but there's probably 10% that needs to be checked and tweaked to change to make sure that it continues.

If you just trust the automation and leave it, that's when you could end up with a lot of problems.

Fabian Heinrich:   And like, I mean, I think you advice clients every day, and you're a true expert in that area. But I mean, what is the point your clients struggle the most with? Or what's the main reason they call you?

Susan Walsh:   The main reason is they don't have visibility on their spend at all.

Fabian Heinrich:   Okay. So spend visibility kind of the key topic?

Susan Walsh:   Yes.

Fabian Heinrich:   Okay, that's very interesting. I mean, your business, as far as I'm concerned,is like depending kind of on your exceptional skills and your brain, and notnecessarily an automated software.

Susan Walsh:   No. So I have a team, and we do use some software; it's called Omniscope, and it's data modeling and a visualization tool. So I've developed amethodology to classify cleanse the data.

The first time around for a new dataset, it's all done 100% manually; we do every single row. But if there's are fresh, we can semi-automate that process. So it helps to be more efficient.

Because if we know that the existing set of data is already accurate, we can then map that over to future data.

Fabian Heinrich:   Yes. I mean, do you think like any kind of the big software players we are seeing around over the last 20 years will incorporate such a data modelling in order that, I mean imagine in one of those big software players I could press a button clean my data or maintain my data or update my data.

Do you think that's in the near future possible? Or do we need to wait for a best of breed solution, which is really tackling that? Or will it be more like the status quo, that you with a smart but software-enabled team with certain models can solve it?

Susan Walsh:   So there's a couple of different answers to this. So if you're talking about a global off-the-shelf classification tool that's automated, we're probably a decade or more away from that, I think.

Fabian Heinrich:   I mean, in that regard, you could theoretically use systems likeWand.

Susan Walsh:   Yes. You have to use what's best for your business, but be aware that it has limitations right now.

But having said that, if you are using an industry-specific or maybe even built your own in-house automation tool, that's likely to be far more accurate than something that you would buy off the shelf.

Because it's tailored to your business or your industry, the problem is when you try to classify for everybody across industries, and then the same thing can mean different classifications to different industries. So that's where the problems lie.

Fabian Heinrich:   Yes.

Susan Walsh:   But if you kind of keep siloed automation for industries or company-specific, then it's more likely to be successful.

Fabian Heinrich:   Very interesting, yes. I mean, since we almost kind of reached the end of our podcast. I think one super interesting question I would have islike, I mean, you've seen many stages of procurement maturities, right?

Like across your career, and across your business now, with your customers. So what do you think could be the future of procurement in general, regardless of the data or anything? Like if you could really see like how procurement evolved over the last 10-15 years, what is, in your opinion, kind of the future?

I mean, it would be super interesting if you could do a quick like drawback where you've seen procurement coming and where you could see it evolving to.

Susan Walsh:   So I think you know we've moved from some pivot tables in excel to dashboards and click tableau, power bi. People are reporting now in far more detail than they ever were before. In terms of the future, I think that it's about, yes, streamlining the classification process, maintaining it, and automating it where appropriate and where possible.

And then, on the other side of procurement, there's a lot of advancement in things like RPA and contracts. And so, I think a lot of the kind of laborious prone to error tasks that are carried out by humans will be automated and be far more accurate.

And it's not about taking jobs away, because those people will be needed in other areas now. But I think that's the way that it's going.

Fabian Heinrich:   Sorry to interrupt you; automation is always a big buzzword, but where do you see automation happening?

Susan Walsh:   Well, I think that's where we need to be realistic about what it can achieve. So things like classification? No, it's far off. But more black and white tasks, so scanning contracts, STP, p2p processes.

Invoice comes in electronically; it goes through a system. It never gets printed off; that kind of thing is going to be really great. And relying on automation to classify your data, that is a minefield, that is not good.

That will end up taking you more time to fix than it would to just start manually. So that's my argument. But it will come, but I just think we are quite far off from it at the moment.

Fabian Heinrich:   Yes. Look, that was a very interesting episode. I mean also I learned a lot from you, and it was an extremely interesting talk to really dive into the topic of classification, data, clean data, dirty data.

Susan Walsh:   Yes, and there's so much more going on than people realize.

Fabian Heinrich:   How we can automate it, yes. So yes, like thanks a lot, and maybe we look at another episode in a couple of months and dive more into one topic. BecauseI think we could speak hours about that topic, and still not solve it.

Susan Walsh:   Absolutely. Well, I don't know if you know, but I have a book coming out in about two or three weeks. So there are lots of topics in there. In fact, there's a whole chapter on taxonomies, so you would love it. So yes, there's lots to talk about.

Fabian Heinrich:   Maybe you can do a quick shootout to our listener, so what's the name and the title of the book.

Susan Walsh:   Yes. The book is called "Between the spreadsheets: Classifying and fixing dirty data". There's a whole chapter around what is dirty data, and what are the consequences on businesses.

But then there's how do you classify? How do you normalize? How do you build a taxonomy? And there's a data horrorstories chapter. So I share some stories that have been donated.

So it covers pretty much all the procurement data and top-line information that you might need.

Fabian Heinrich:   Yes, it sounds like an absolutely great read. So thanks a lot, and given what I experienced with procurement data, I truly think it's the fundament of every procurement organization. And even though I've unfortunately not read your book, I will blindly recommend it.

Susan Walsh:   Don't worry, I will make sure you get a copy when it comes out.

Fabian Heinrich:   Thanks a lot. And thanks a lot for your time, and have a good day.

Susan Walsh:   Yes, thank you. Yes, you too.

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