Stuck In A Niche Industry? Here’s Everything You Need To Know About Creating Demand Through Speaking With Susan Walsh

by | Oct 21, 2021 | Podcasts

SWGR 585 | Creating Demand


If you’re selling something niche, how do you find customers? You need to stand out and really create a demand for it. Susan Walsh‘s business is on data quality and most people don’t even know what that is. Susan is the founder of The Classification Guru and she is a specialist in spend data classification. Learn from the Mistress of Data herself on how she created demand through speaking. Elizabeth Bachman gets Susan on the show to share how she thrives in a niche industry. Find out if you’re spending the right amount for something, no matter where you bought it. Discover the Coat system and how Susan uses that when it comes to data management. Learn all of that and more today!

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Stuck In A Niche Industry? Here’s Everything You Need To Know About Creating Demand Through Speaking With Susan Walsh

When Customers Don’t Know They Need You

My guest is Susan Walsh, known as The Classification Guru. Susan is a fixer of dirty data, believe it or not. She made a name for herself in her unique niche through speaking so I was very curious to have her. The official bio is Susan Walsh is the Founder and Managing Director of The Classification Guru Ltd. She is Scottish. It’s a special data classification, taxonomy, customization and data cleaning consultancy. She’s an industry thought leader, TEDx speaker and author of Between the Spreadsheets: Classifying and Fixing Dirty Data.

She’s also the creator of COAT, it’s a system for Classifying, Organizing, Analyzing and Trusting your data. She has developed a methodology to accurately and efficiently classify, cleanse and check data for errors, which will help to prevent costly mistakes. This could save days of laborious cleansing and classifying and can help your business find cost savings through spend management and time management. She supports better and more informed business decisions.

Susan brings clarity and accuracy to data and procurement. She helps teams work more effectively and efficiently cuts through the jargon to address the issues of dirty data and the consequences of not cleaning it in an entertaining and engaging way. She’s passionate about helping you find the value in cleaning your dirty data and she raises awareness of the consequences of ignoring issues through her blogs, vlogs, webinars and speaking engagements so onto the interview with the wonderful, Susan Walsh.

Susan Walsh, I’m so happy to have you on the show. Welcome.

Thank you so much for having me.

It’s really delightful. I heard about you because we’re speaking at a conference together and I said, “Who’s Susan Walsh?” and I looked you up and I went, “She’s amazing up. I’ve got to get her on the show.” I have a lot of things to ask you about but before we start, let’s go to who would be your dream interview? If you could interview someone who’s no longer with us, who would it be? What would you ask them and who should be listening?

I was going to say Robbie Williams but he’s alive so that’s not going to work.

That’s Robbie Williams behind you?

Yes. The love of my life. I got asked this question as well and I have always said my gran. I’d love to interview my gran. She was 60 when I was born so she was already older and had dementia from when I was in my teens. She lived this amazing life up until the age of 30. She was single. She didn’t get married. This was in the ‘40s and ‘50s when that was just unheard of. I’d love to be able to ask her what she did, where did she go, what did people think of her because it must’ve been against the grain to be a single woman in those days so I’d love to ask her those kinds of questions.

To find more customers, you need to position yourself as the expert. That way people will know who to contact when there's a problem. Click To Tweet

What year was she born?


She would have been a teenager during the Second World War.

We had her identity bracelet and her Russian boots. A real piece of history. I remember a great story from her as well. We live in a seaside town and a ship was bombed and just got sunk. The whole of the stuff washed up on the beach and for the first time, they saw bananas. They had never seen bananas before.

Where was this?

This is in Broughty Ferry, which is a suburb of Dundee.

Dundee in Scotland. Let’s get the elephant out of the room. This is a very international audience. Where are you from with this charming accent you have?

The accent is a Broughty Ferry/Dundee accent but very torn down because I’d been living in England for years now.

Do they comment on your English accent when you go home to Scotland?

They don’t but I do speak to my dad every day so I’m clawing and holding on to my accent with everything I’ve got.

When I grew up, our next-door neighbor was from Mississippi. You could always tell when she’d been on the phone with her mother because she’d come over and the accent was back.

When I go home for a visit, it’s a lot stronger when I come back. Some of my friends who are European are like, “What? What did you to say?” I talk faster and my words are stronger. Same ones if I’ve had a few cocktails as well.

That always helps, too. I wanted to ask you, you’ve created this awesome company classifying data but you’re doing it all by yourself mostly?

I have a team now but they only started in January 2021 so most of it’s been just me.

The thing that I realized when we were chatting that you said people didn’t know they needed me and I had to get myself out there through speaking and I went, “I remember that. I’ve had great memories of trying to convince people that they had to hire me and they didn’t know they needed me until I finally found the audience that did understand the value of what I did.” That’ll be my second question because my first question is, I’ve read your bio but tell us what you do, why you created The Classification Guru and why do we need you?

SWGR 585 | Creating Demand

Creating Demand: Procurement departments could be paying very different prices for the same thing in different countries. So by using spend analytics, they can compare what they are paying for.


I was working for a spend analytics company and I could see that they were spending a lot of money on dashboards, reporting and analytics but before they could do any of that, the data had to be cleaned. Every single data set that came through the door was a mess. I knew that my skills were in the classification of the data and I couldn’t really do much on the analytics. I hadn’t worked in procurement so I didn’t know that but I did understand the data.

After five years, I wanted to move on. I didn’t know where I could get a job doing something similar. I didn’t even know if my job had a name. My options were to either find a whole new career or start a business doing this, just offering the cleansing part, which even to this day, nobody’s really doing that as a standalone service, except for me.

What’s spend analytics? For those who haven’t been in the weeds with that.

That would be used a lot by procurement departments. They are the people who buy goods and services for the company that they work for. If it’s a global company, for example, they could be buying office supplies in a number of different countries and they could be paying very different prices for the same thing in different countries.

By using spend analytics, you can compare what you are paying for certain items if it’s classified properly. You can look at how much you’re spending per supplier and that might mean that you can negotiate better rates with the supplier that you’re working with. It can help with things like rogue spending where people are buying things that they shouldn’t be. It can help to identify that. It could be people who are buying chocolates every week for the office. It could be something as simple as that. That would be rogue spending because it’s not really approved.

It’s classified as printer paper.

It might not even be classified so nobody knows they’re buying it. That’s the thing. In using spend analytics, if you classify your data, you can then see what your spending, where and how much.

Let’s use printer paper as an example. I’m sure there are all sorts of really interesting things that people purchase. To make it understandable for most people, you buy yourself some boxes of printer paper. If it’s more expensive because someone’s buying very expensive chocolates for the office every week, that could raise the price, correct?

If the chocolates have been misclassified, it could probably lower the cost of the paper potentially or skew the pricing. The other thing is in one particular office, you might be buying paper from 2 or 3 different suppliers and for the same paper, you’re paying a different price for the same thing but from each of the different suppliers. Using analytics, you can look at trying to make cost savings by maybe using 1 or 2 suppliers, not 3.

This sounds like a nice, organized, directed way of describing it but one of the things that you’re really good about is making data fun and relatable. Try to explain it. It could be copy paper, printer paper or anything. How do you describe this to say, “Great Aunt Sally, who’s talking to you at a family dinner?”

It’s basically my tagline on LinkedIn, fixer of dirty data. That’s the best thing I can do. It says everything.

What do you mean by dirty then?

Make it so that you are “what you see is what you get.” Click To Tweet

Dirty could be accurate, missing or partially-completed duplication so quite often, if you have a database, you might have one person in there more than several times. Same address, the name might be slightly different like R. Smith, Robert Smith, Bob Smith so cleaning all that up so that organizations can work properly, save time and money especially with things like databases. Make sure you’re contacting the right people.T

his means you have to have a human being looking at this because a computer would not understand the difference between Robert Smith, R. Smith and Bob Smith.

Now it is. You can find a cord and systems you could write to, to recognize that. I prefer to do it manually with my team. Get it right the first time and then it can be automated afterward. That’s how I would do it.

How did you get people to know that they needed you? This is a very specific thing that you do. How did you keep customers find you?

It was really difficult. I exhibited a procurement event, which is where the majority of my clients were going to be and they were like, “We need your services. I wish I knew about you months ago,” but I realized quite quickly that I’m needed at a specific point in time. It’s not just an instant purchase service. It’s still they only really need me when there’s a problem so I had to find a way to position myself as the expert so that people knew who to contact when there was a problem.

I started connecting with a lot of people on LinkedIn and leaving my details with them. I started connecting with some other thought leaders and influencers on LinkedIn and growing my network. At the beginning of January 2020, at that point, I hadn’t spoken, I hadn’t done any podcasts and I hadn’t done any events. My plan in January for the year was to do one in-person speaking event and two webinars.

Per month?

No, just for the year. I wanted a small target. For the year I ended up speaking at up to twenty events and I did multiple podcasts as well. I have never asked to go on someone’s podcast. I’ve always been invited on. It is a real snowball effect. Once one person gets you on and then someone hears that and then they want you on their podcast and someone else wants you on their podcast, someone else wants you to speak for them, it snowballed from that.

The past year was all about online speaking. I honed my skills at that time. This time, I really wanted to try and speak in front of people so my first opportunity was when I did my TEDx Talk in May 2021 which was absolutely terrifying because I was completely out of my comfort zone and I had no notes. I just had to talk for twelve minutes. I then had my first data in-person speaking event in September 2021 and it was amazing. I loved it. I was really comfortable. The room was packed and great audience. I could have done it again. I enjoyed it so much so we’re looking forward to doing more in 2021.

What I’m hearing here is that you practiced a lot so that you got better every time. Part of what’s fun is, you’re fun and funny. Did you ever have a moment of thinking, “How do I show up like me?” I’m a little disappointed that I don’t not seeing the glittery background that I’ve seen in other videos but you’ve got a very fuzzy lamp behind you and you’ve got this wonderful glittery background.

That’s my data den.

What’s the data den?

Before I moved, my old flat had my kitchen behind me. I’ve worked from home for years. I’ve done a lot of Zoom meetings before most people knew what Zoom was but most of the time it was audio only. Then it just got really hard to keep the kitchen tidy behind me so I got a white screen. I was like, “This is so boring. What can I do?” I got some tinsel and I’ve hung that over the white screen but now I’m in my new flat. I don’t use it as much because it feels quite restrictive whereas I’ve got lots of lovely space now.

SWGR 585 | Creating Demand

Creating Demand: Nobody is really talking about data quality in LinkedIn because it’s such a boring subject. So you have to do something to get people’s attention like making fun posts.


You’ve got a couple of life-size images behind you.

There’s a life-size 3D cutout of myself and then one of my future husband, Mr. Robbie Williams.

For our international audience, who’s Robbie Williams?

He’s a well-known singer in UK and Europe. He was in a boy band for a number of years and then went out on his own. I’ve been following him for many years. A long time.

One of these days he’ll meet you and realize that he’s your future husband.

I don’t know because I feel like, is it going to be a disappointment if I meet him? At the same time, I really would love to meet him.

Having come up in the opera business and worked with a lot of celebrities, many of them are wonderful and some of them are really horrible off stage so you never really know.

I get the impression he might not be like, “The money is on stage.”

You’ve built up this whole fantasy around him, which is when you’re a public figure, that happens. I’m wondering, have you had any people who come and approach you and feel like they know you because they’ve been listening to you?

All the time. It was exciting at Big Data LDN because I was walking and all I would hear was “Susan,” and it was all these people that I know from LinkedIn that we’ve never met before so we’re talking. Sometimes people do think they know you really well.

When you see an actor and you think, “That’s the character,” but no, it was just an actor doing his or her job. Part of putting yourself out there is creating an image. I’d be curious a little bit about the thought that went into creating your images. Your online images are awesome. I’m assuming they’re very, but I also imagine that there were people who said you can’t do purple tinsel on LinkedIn. Talk to us a little bit about the thought process of making your image really you.

I remember when I first started the business on LinkedIn, I had maybe up to 1,000. I had done a post and it just said, “I love data so you don’t have to.” It was just a picture of me and I remember the first time we got 1,000 views and I was like, “1,000 people like that.” They saw the personal side of me. It’s very much what you see is what you get with me and I realized that that worked well. Having seen what was around on LinkedIn, I could see that nobody was really talking about data quality and data cleansing.

It’s such a boring subject. You have to do something to get people’s attention so I did lots of fun posts, I hooked up with other influencers and did stuff over Christmas then I wanted some animated videos because I wanted to be able to appeal to not just data people but people who work with data. It’s not the same thing.

To do that, you have to do it in a really relatable way and I wanted to have fun with it because what’s the point in doing it if you don’t have fun. There are loads of excellent, particularly data, trainers out there who are very serious and very good at what they do but for the average person, I don’t think they’re going to remember them but they’re going to remember me with my sparkly background, my funny videos, my fluffy lamps and lipsyncs.

Humor is a huge asset for a presenter.

I didn’t think it would be received as well as it was. I thought that potentially it could divide the audience and restrict my customer base but if anything, it’s grown it. People feel like they know me before they approach me for help so it’s like an automatic filter before people even approaching me. People who are not interested in my style won’t come and talk to me and that’s fine. We’re probably not going to work together very well. That presence attracts like-minded people so it’s absolutely not done me any harm whatsoever.

I have introduced you to a couple of people because I know that they are going to love the purple tinsel and solid business behind it but the purple tinsel is going to definitely make a difference.

That came about during lockdown when people really needed some cheering up. That whole period, I posted a lot but none of it was about work. It was all just fun stuff and because I needed to keep my presence alive but I didn’t want to be a salesperson.

Make sure your data has its coat on. It needs to be consistent. Click To Tweet

One of the things that I talk a lot about is describing a complicated technical subject through a metaphor. What I love about you is the COAT system. I know people who arrange their closets by color. I know a lot of other people who just throw everything into the bottom of the closet and haul it out if they need it but sometimes there’s penicillin growing on that pair of shoes. What is your COAT metaphor? Talk to us about that.

It was about getting everybody relating to data and understanding the importance of data quality so by saying, “Make sure your data has its COAT on,” it needs to be consistent. That’s where the wardrobe analogy comes from. If you’re working in a global organization, things like letters, how do we spell that? What date format are we working in? European or US? All those kinds of things make a huge difference to your data then it needs to be organized, which is like a closet. If you’ve got your clothes all organized, you should just go and pull out what you need and the data is very much the same.

If you organize it, you can find that information really quickly then it needs to be accurate as well. Depending on what data you’re working with, we need these five columns filled in for this product or we need the customer’s first name, last name, address and email or it has to be 100% accurate if it’s financial data. Once you have all those three things, you then have trustworthy data. That means you can make decisions for your organization and your team based on accurate data. That’s where that came from and I’ve had a lot of fun with that.

COAT is a Consistent, Organized, Accurate and Trustworthy. Is that right?

Yes. Well done.

Have you found that a metaphor like that makes it easier for people to understand?

Yes and I’m so proud because I had commented on someone else’s post and referenced COAT and someone said, “What’s COAT?” Somebody else replied with the answer, not me. I was like, “Yes.” That’s something that everybody can understand. It’s not technical. The data people like it because it’s fun and the non-data people understand it so they’ll remember it too hopefully.

I worked with a lot of people in Silicon Valley and in the tech industry, mostly trying to get them to use metaphors to explain what they’re doing. What’s the difference between big data, which is crunching lots and lots of numbers? What do you do?

You can’t crunch the numbers until you know the numbers are right and the bit before that. There are a lot of different analogies that I use but if you’re having guests drowned, that’s crunching the big data numbers, I’m the hoovering before people to come up.

Translating that into American English, if you’re having guests for dinner, she’s the person who’s vacuuming before they get there. The thing about data is the one thing every company in the world has in common is people and different people will enter things in different ways. I go back and forth between the US, Austria and Germany. You have to switch the numbers. It’s English, it’s 37, in German, it’s 7 and 30. That has gotten me into trouble more than once if I automatically said 37 but I really meant 7 and 30. I could see that’s the human mistake that would be so easy to make.

You might have it in your data like Australia, my data might say Austria and a French data will say something else. That’s an example of dirty data. You’ve got the one country but it’s spelled multiple different ways so you would need to streamline that.

I live near the town of Innsbruck and in Downtown Innsbruck, the souvenir stores, have T-shirts that say, “No kangaroos in Austria. Not Australia.” People constantly say, “How’s life in Australia?” That’s a different continent. I waited until nearly the end. You have a book that’s just come out. Tell us about the book.Between the Spreadsheets: Classifying and Fixing Dirty Data.

I finished writing it and it’s been released. The reason that I decided to write it was because there are very few references, information or books on cleansing, cleaning and classifying data. I wanted to be the first to do that to get out there, put my mark on everything and share my knowledge.

I can’t wait to read it. I probably won’t understand half of it, except that the way you’re going to write it in a way that I am going to understand it.

Most of it, you will.

Who is it particularly for?

I purposely didn’t want to narrow it down to just procurement people or data people. If you are starting out in procurement or data, you can read this and get something out of it. If you’re already working in procurement and data then you could still get tips, tricks and ways to work more efficiently from this book. If you’re a decision-maker within an organization, you could read this book and understand the importance of actually investing, cleaning or classifying your data. One of the biggest challenges I face is most of the people that speak to me are ready to work with me but they can’t get a sign-off from further up the organization. The businesses won’t invest in the data.

The businesses don’t understand how important it is.

They see it as a cost. They don’t really see what the benefit is. It’s hard to quantify.

SWGR 585 | Creating Demand

Creating Demand: Managing your data is like cleaning your closet. If you’ve got your clothes all organized, you can just pull out what you need. So if your data is organized, you can find information really quickly.


This is one of the things I’ve been thinking about a lot is how often women especially will run a department that runs smoothly through lots of little tiny things. They don’t get promoted because they’re not noticed. There’s a wonderful book that came out a couple of years ago called The Art of Doing Nothing, about the value of things not going wrong. We are so conditioned to having a crisis and rushing to fix it. Just a quick story, way back in my very early opera days, I worked in Puerto Rico. I could tell this story because the people I worked with aren’t there anymore.

I learned it’s mañana land, which means that nobody really gets to work until the day before opening night. The opening night is mañana, which means tomorrow, people suddenly get to work. They rush and they pull it off. They pull off a miracle and there’s this huge adrenaline rush from it. I was trying to say, “If you planned, you don’t get this adrenaline rush of pulling it off at the last minute but there is a great satisfaction and having something that runs the way you intended it to.”

I tried to sell this idea to the company and they sent me a letter saying, “Thank you. We no longer need your services,” because they love that adrenaline rush. You see it in business all the time, the adrenaline rush of fixing a problem, pulling off a rescue, the whole hero thing. The whole hero thing where what you’re doing is making sure the issue doesn’t happen, which saves a lot of money but it’s invisible.

There have been a few jobs I’ve left and I’ve thought to myself, “You have no idea what you’ve been doing the whole time. You are going to be in so much trouble when you leave because you just have no idea what’s going to happen.

This is part of an ongoing conversation I’m having with several of my clients, trying to really get the message out in the world on how to accurately measure the contribution of the women who are doing lots of little fixes and tweaks all along the way instead of one huge change so that that contribution is measured and valued. The value of fixing it before it goes wrong, which is much cheaper but invisible. We may have to get together a year or so from now, you and I. Let’s see how far this conversation has moved forward because it’s a constant challenge.

In the data world, probably not very far because the conversation around data quality has been going on for decades and we have more data than ever. I hope that this book will at least start a conversation with some people or encourage other people to get into the space and talk about it as well. I hear quite often about millions of dollars going down the tube because the data wasn’t clean when they did an implementation and it doesn’t work anymore. It would have cost them a fraction of that, literally less than 1% of the cost of the software implementation to fix the data first but they just don’t get it.

I keep thinking about the mission to Mars where the Rover either exploded or it went off into space because there were two different labs working on it. One lab was working in inches and feet and the other lab was working in meters and centimeters and nobody noticed.

I haven’t heard that one before. There’s something in my book in the data horror stories chapter. There was a hospital in Scotland and it was delayed because the aircon had been put in at the wrong heigh. Somebody had copied and pasted a column into the next column and nobody had checked the numbers. Somebody did notice and reported it and they still ignored it.

This has been such a delight. We’re going to have to circle back and talk again and see how this doing because I could talk to you for hours.


Thank you so much for being on the show.

Thank you. Next time, I promise I’ll come back with my data den.

I want the purple tinsel. Thank you so very much and thank you, everybody. My guest has been Susan Walsh, The Classification Guru. Let me remind you that if you’re curious about how your presentation skills are doing, you can take our free four-minute assessment at That’s where you can find out if your presentation skills are strong and perhaps a little bit of support could get you the recognition and the results that you need. Thank you so much for joining. I’ll see you on the next one. 


Important Links


About Susan Walsh

SWGR 585 | Creating DemandWith a decade of experience fixing your dirty data, Susan Walsh is the Classification Guru. Susan is the Founder and Managing Director of The Classification Guru Ltd, a specialist data classification, taxonomy customisation and data cleansing consultancy. She is an industry thought leader, TEDx speaker and author of ‘Between the Spreadsheets: Classifying and Fixing Dirty Data’. She’s also the creator of COAT.

Susan has developed a methodology to accurately and efficiently classify, cleanse and check data for errors which will help prevent costly mistakes. This could save days of laborious cleansing and classifying and can help your business find cost savings through spend and time management – supporting better, more informed business decisions.

Susan brings clarity and accuracy to data and procurement; helps teams work more effectively and efficiently; and cuts through the jargon to address the issues of dirty data and its consequences in an entertaining and engaging way. Susan is passionate about helping you find the value in cleaning your ‘dirty data’ and raises awareness of the consequences of ignoring issues through her blogs, vlogs, webinars and speaking engagements.

Dream Interview: Her grandmother