Susan Walsh - The Classification Guru

Fixer of dirty data ★ Spend data classification, supplier normalisation, and taxonomies ★ DataIQ100 x 2 ★DataIQ Data Champion 2022★ Author "Between the Spreadsheets" ★ Creator of COAT ★ TEDx - say NO to NO ★ DataVenger

I’m Susan Walsh, also known as The Classification Guru, a globally recognised expert in spend data classification, cleaning, and transformation. For over a decade, I’ve helped organisations fix dirty, unreliable data and turn it into insight they can actually trust.

In 2017, I founded The Classification Guru Ltd, rebuilding my career from the ground up after coming back from bankruptcy after my first business failed. What started as a solo consultancy has grown into a specialist business supporting 100+ organisations globally, particularly in procurement, sales, and marketing, where clean data underpins reporting, automation, and AI.

I work with mid-sized and enterprise organisations, consultancies, and technology providers that rely on complex, high-volume data but struggle with inconsistency, duplication, and poor structure. My clients include procurement, finance, sales, and marketing teams who need data they can stand behind, whether that’s for spend analysis, supplier management, CRM reporting, forecasting, compliance, or AI initiatives.

Keeping a business alive and growing for almost nine years hasn’t been about shortcuts or hype. It’s been built through real-world delivery, practical problem-solving, and a refusal to accept “good enough” data as acceptable. Along the way, I’ve developed frameworks and tools to help organisations embed sustainable data quality, including the COAT framework (Consistent, Organised, Accurate, Trustworthy) and Samification, a self-service supplier normalisation platform launched in 2024 to make clean, structured data accessible at scale.

I’m also an active contributor to the wider data and business community. I’m a TEDx speaker, international conference speaker, and recognised thought leader in data quality and classification. I’ve authored three books, including Between the Spreadsheets 1st and 2nd editions, and the upcoming Optimizing Sales and Marketing. I teach globally via platforms such as Pluralsight, O’Reilly, and the Analytics Institute. Through consistent, practical thought leadership, I’ve grown an engaged LinkedIn audience of 43,000+ professionals.

Alongside my commercial work, I founded the Women Who Speak LinkedIn group, a free initiative connecting women speakers with event organisers to improve visibility and representation. I hope my journey shows that you don’t have to fit a mould to build a credible business, lead in your field, or create meaningful impact.

My mission is simple: help organisations move from spreadsheet chaos to clean, organised, trustworthy data that actually works.

Founder/MD (Mistress of Data)

I love improving the quality of procurement spend data, giving teams clarity so they can make better business decisions and cost savings.

Why not let me take the pain out of laborious data issues and make your life easier?

Whether it's a small or large data set, the accuracy can be improved by identifying classification errors, conflicts or gaps. I can provide a data verification service to efficiently find and correct these in a fraction of the time it would take you or a team member.

And a crucial part of having accurate data is also working with a comprehensive taxonomy, something that can be easily overlooked. I can provide a taxonomy development service to ensure you have all the vital areas covered for the greatest visibility - job done!

If you struggle with the time, resources or willingness to tackle your data, why not get in touch to see how I can make your life easier.

Who does quiet, easing back into work in January? Not me!

Just had a wonderful evening up in London meeting Adita Karkera, Ph.D. and lots of fabulous ladies from Women In Data™️

Gonna need a lie in tomorrow now 🤣🤣

Thanks Shubhangi Goyal for organising and Samir Sharma for being an honorary woman in data! 

The Classification Guru Ltd Samification

Susan Walsh

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Ok 2026, I may not be ready, but let’s do this!

When you work for yourself there’s very few h&s measures in place, like a warm workplace.

I woke up to -4 outside and not much different inside, even with the heating on, so whoever gets on call with me today will find me like this. #sorrynotsorry

The Classification Guru Ltd Samification

I asked ChatGPT one question: "Based on our interactions, how could I be leveraging you more?"

Because I want to work smarter, not harder this year.

And the answer wasn't, “write more posts faster," it was slightly unexpected - AI works best before things are polished, not after.

Which basically means that even with AI, I'm still a control freak, not delegating until the last minute, so that's something I'll be working on this year.

Here’s the recommendations I'll be taking forward:

 • Using it as a thinking partner, not just a writing tool
 • Pressure-testing ideas before they’re fully formed
 • Acting as an editor with opinions, not just a spellchecker
 • Repurposing one solid idea into multiple formats
 • Being a built-in “so what?” detector
 • Exploring bolder positioning without committing to it publicly
 • Preparing for talks, panels, and conversations earlier, not at the last minute

The biggest shift for me? Bringing AI in when things are messy, not when they’re “ready”. Drafts, fragments, half-formed opinions… that’s where the real leverage is.

So tell me, where in your workflow could AI take more of the heavy lifting, so you stay in your zone of genius? 

Why not try this prompt or a variation and let me know!

The Classification Guru Ltd Samification

Have you met the Dirty Data Villains?

Every dataset has them.
Some are obvious. Some hide in plain sight.
Together, they’re responsible for broken reports, failed joins, messy dashboards, and far too many “why doesn’t this match?” moments.

Here’s the current Dirty Data Villains lineup 👇

🟡 Rogue Punctuation
Commas, apostrophes, periods, and symbols turning up where they don’t belong. Breaking imports, splitting values, and causing errors that are frustratingly hard to spot.

⬜ Sneaky Spaces
Invisible troublemakers lurking at the beginning or end of values. The reason your “exact matches” aren’t exact and your duplicates refuse to reveal themselves.

👯 Duplicate Dan
The master of cloning. Same customer, same supplier, same invoice… entered twice (or more), often with just enough variation to avoid detection.

🔤 Typo Tracey
One wrong letter is all she needs. Misspellings, swapped characters, missing letters — causing failed lookups, bounced emails, and embarrassing mistakes.

📅 Format Fred
The chaos king of inconsistency. Dates, phone numbers, and text fields all formatted differently across systems, making automation and reporting painful.

💥 Why they matter
These villains don’t just make data messy; they undermine trust. And without trust, data can’t be relied on for decisions, insights, or AI.

The good news?
Once you can name them, you can spot them.
And once you can spot them, you can clean them.

More villains may be lurking… but for now, this is your most wanted list.  And if you need some help identifying them, ask me about our new data diagnostic service, more details coming soon!

Which Dirty Data Villain causes you the most pain?  And who would you add?

📘 Two books
📉 Big discounts
📊 Cleaner, calmer, more trustworthy data

Get more Susan for less!

🟣 Between the Spreadsheets from Facet Publishing – 20% off using NEW20
🔴 Optimizing Sales & Marketing Data from Kogan Page Publishing – 40% off with SALE40

What you’ll actually learn:

💡How to spot dirty data before it wrecks reporting
💡Practical ways to fix duplicates, inconsistencies, and naming chaos
💡Why “the tech will fix it” usually doesn’t
💡How to build data you can trust for decisions, automation, and AI
💡Real-world lessons from projects, not fluffy theory

If you’re wrestling with messy data, unreliable dashboards, or a CRM that feels held together with hope and pivot tables, these will save you time, frustration, and a few awkward meetings.

Built on real client work. Powered by the COAT framework. Zero nonsense.

Grab them while the discounts are live!

Shoutout to some fellow authors Jordan Morrow Tiankai Feng Nicola Askham Caroline Carruthers Kristen Kehrer Roberto Maranca Albert Bellamy Keith McCabe (MCIPS) Samir Sharma Dr. Joe Perez

The Classification Guru Ltd Samification

Movement-starter, TEDx speaker and award-winning author, I am an internationally recognized as a pioneer in data literacy and one of dataIQ™’s 100 Most Influential People in Data.  I am the owner of Bodhi Data and currently serves as the Senior Vice President of Data and AI Transformation at AgileOne as well as a speaker for The English Square. I have also served as the Chair of the Advisory Board for The Data Literacy Project and speaks at leading companies, events and universities among which are the United Nations, Fidelity Investments, Vanguard, Nike, Big Data London, Coca-Cola, the PBS annual meeting, Bacardi, and Columbia University.

I help audiences identify important signals in their data through visualizing and storytelling, stipulating that wisdom, above strategy and goals, is required to become truly data literate. I have authored five books on data and AI, and was awarded first place in the International Book Awards in 2023. My mission is to empower individuals and organizations to be more data literate and data-driven. By the end of my speeches, you will feel more confident to succeed with the necessary data and AI skills.

Contact www.theenglishsquare.com for speaker bookings.

Jordan Morrow

Lean into your struggles and failure, but don't give up.

In your career, have you been pulled aside or had a meeting where you received hard feedback?  What did you do with it?  

I have had it.  Early in my career, maybe less than 2 months after finishing my undergraduate degree, I was pulled aside and essentially told shape up or I would need to be moved.

What did I do?  Did I quit, sulk, what?  Now, in those moments, you can feel sad and worried, don't chase those emotions away, but what you do next matters.

I went on to be a President's Award winner at this company.  I moved to my next company, won an award again and pioneered data literacy.

Now, a personal example from my days of ultra marathon running illustrates that even in struggles, don't stop.  I was racing the Speedgoat 50k in Utah, one of the toughest 50ks in the world and one of my favorite races, but this time it hit different. 

The race has over 11,000 feet of vertical gain in it and you hit high elevation multiple times in the race.  But, there I was, struggling and, quite possibly, never wanting to quit a race more than I did there.  I was even talking to my wife through text and/or calling.  She and my kids were going to be on one of the peaks spectators could be on.  I didn't quit but maybe hoped to miss a cutoff so I could be done.

But, I didn't.  I kept going.  Then, when I was at the last big summit and made cutoff, things changed on the descent.  If I remember right, I even encouraged someone else as we went down.  

In the end, I didn't quit, I finished.  My watch, if I remember right, estimated a sweat loss of over 21 lbs in that race (don't worry, I was hydrating and drinking during the race).

Now, had I quit, what would my mentality have been, one where I was happy or would I live with regret?  In our careers, we have races where the climbs, the heat, the hydration, and other aspects of the career may make us want to quit... and at times, quitting a company might be the right choice, just like a race if you get injured.  

Now, what did I learn from that race?  My hydration strategy maybe wasn't what it should have been.  What lessons are you learning in your career struggles and what can you do with them?  Don't run from them, run with them.  Take the hard feedback, make a plan, and attack it.  Don't be a victim be one who shapes and owns their career.

Stay nerdy, my friends.

(The pic with me and the goat was before the race, I was happier at that time)

#data #AI #dataliteracy #AILiteracy

Amplification and augmentation...is that how you are viewing AI?  If not, you should be. 

AI is a human initiative powered by powerful tech.

In the era of the democratized intelligence the divide isn't between those who can code and those who can’t. It’s between those who use AI to AMPLIFY and AUGMENT their value, and those who are waiting for the "perfect time" to start.

- Amplification vs. Augmentation: Know the Difference

To take your seat at the table, you must understand how you’re using the tools:

Amplification (Doing more of what you do): This is about scale. It’s the HR Manager using Agentic AI to screen 1,000 resumes in seconds while maintaining a "culture-fit" sentiment analysis. It’s taking your existing expertise and giving it a megaphone.

Augmentation (Doing what you couldn’t do before): This is about capability. It’s the person using "vibe coding" to build a custom data dashboard without ever touching Python and iterating to get it right. It’s the logistics lead using multimodal AI to "see" supply chain disruptions before they appear on a spreadsheet.

- For Organizations: Stop Scaling Tools, Start Scaling Skills

Strategy isn't just buying a thousand Copilot licenses. If you want to thrive, your "AI Program" needs to look at the Work OS, not just the software:

Redesign the Workflow, Not Just the Task: Don't just "add AI" to a broken process. Evaluate where human judgment is the bottleneck and where it is the value-add.

Move from "Training" to "Fluency": Data and AI literacy are no longer a workshop; have it be a part of onboarding. Every employee—from Finance to Front Desk—must know how to turn intent into outcome using AI.

Appoint "AI Champions": You need influencers who bridge the gap between the C-Suite's vision and the operational reality of the "99%."

- For Individuals: How to Take Your Seat

Don't wait for a formal training program. The "coding wall" has crumbled.
Be a Domain Expert First: AI is a multiplier. If your expertise is 0, the result is still 0. But, guess what: your expertise isn't 0.  You have expertise.  Amplify and augment it.  Deepen your industry knowledge; let AI multiply.

Master the "Orchestration": In the old world, talent meant "Can you do the work?" In the new world, talent can mean "Can you orchestrate the work?" (Engineered Intelligence = Data + AI + IQ + EQ).

Show, Don't Tell: In 2026, a "Founder" mindset is more valuable than a resume. Build something. Solve a small departmental friction point with an AI agent. Take your seat at the table.

The table is set. The data is ready. The AI is ready. The only thing missing is you.  Sit at the table and partake.

How are you using AI?  The future is there, take the chance to craft and design it, not have it crafted and designed for you.

Stay nerdy and thrive, my friends.

#data #AI #Dataliteracy #AILiteracy

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The Battle

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