MindSpeaking

We've trained 15,000+ people on Data Storytelling. 
Let's talk about training for your team — or your entire organization:

Founder & Trainer

Author of the bestselling book: People Skills for Analytical Thinkers

The Essential Workshops

Member Of The Board Of Advisors

Capgemini

•	Combining Behavioral Science & Data Analytics to improve customer experiences

Managing Consultant - Data & Digital Analytics

•	Lead of Digital Marketing & Analytics capability in the Netherlands 
•	Coach for 8 consultants who reported to me

Senior Consultant

Capgemini Academy

Trained 300+ people on topics like: 
• Storytelling
• Presentation skills

Trainer

Personal development

Traveling, Spanish classes, and writing a book.

Sabbatical for 6 months

Cognizant

Trainer - Interpersonal Skills

Consultant - Digital, Data & Analytics

Junior Consultant - Digital, Data & Analytics

Maastricht University

Taught the course Behavioral Economics to second-year Economics students.

Student Assistant, Behavioral Economics

Gazprom Marketing & Trading

Marketing Intelligence Internship

Research Project Maastricht

•	Conducted contractual market research for multinationals in Brazil for 4 months;
•	Trained by consultancy firms (BCG, KPMG, McKinsey).

Market Researcher - Brazil

Student bar De Uni

President

Data Storytelling & stakeholder communication training | MindSpeaking | The Human Side of Data

Many Data professionals are brilliant.

Their analysis? Razor-sharp.
Their models? Sophisticated.
Their attention to detail? Exceptional.

But translating insights into a story that non-technical stakeholders understand…
 
That’s a different ball game.

In 2015, I presented insights to a group of Marketeers. 

I expected: 
Applause.
Approval.
Action.

But I was wrong. 
Nothing got implemented. 

That’s when I learned the hard truth: technical skills don't drive impact—communication does.

🔹 Stakeholders don’t care about dashboards. They care about decisions.
🔹 Insights mean nothing — until people see why they matter.
🔹 Numbers get forgotten. Stories get remembered.

That’s why I founded MindSpeaking in 2020.
Since then, we’ve helped 15,000+ Data professionals transform from data providers to trusted business partners.

🚀 𝗧𝗥𝗔𝗜𝗡𝗜𝗡𝗚 𝗙𝗢𝗥 𝗬𝗢𝗨𝗥 𝗧𝗘𝗔𝗠
Want your team to move from presenting numbers to influencing decisions?
Let's talk: 
MindSpeaking.com/companytraining



Free Resources: 

📙  𝗕𝗢𝗢𝗞
People Skills for Analytical Thinkers 
#1 bestseller — read a free preview:
▪️ MindSpeaking.com/book

🧠  𝗙𝗥𝗘𝗘 𝗘𝗠𝗔𝗜𝗟 𝗖𝗢𝗨𝗥𝗦𝗘
Want to turn Data into compelling stories? 
Join 4,100 Data pros and take my free email course (only 2 min/day): 
▪️ www.mindspeaking.com/datastorytelling 

📊  𝗙𝗥𝗘𝗘 𝗦𝗘𝗟𝗙-𝗧𝗘𝗦𝗧
How skilled are you at Data Storytelling?
Take the free self-assessment (5 min) and grow to the next level:
▪️ MindSpeaking.com/maturity-model

Gilbert Eijkelenboom

Stop dumping data. Start telling a story. 
Here are 4 tips:

Most data professionals think their job is to present numbers.

But numbers alone don’t create impact. 

If stakeholders don’t get the message instantly, they’ll tune out.

Before: Data Dump
❌ Lots of colors → No clear focus. 
❌ Just raw numbers → No clear message.
❌ No direct labeling → Hard to read values.
❌ Stakeholders confused → “What’s the takeaway?”

After: Simple Storytelling
✅ Fewer colors → Only for emphasis.
✅ A clear title → Explains the insight.
✅ Simple annotation → Highlights the key change.
✅ Direct labeling → No guessing, numbers are clear.

If people have to decode your charts, you've lost them.

Be the guide. 
Tell people where to look. 

Agree? Share your #data visualization tips below.

♻️ Liked it? Repost to your network.
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Data Storytelling training | Bestselling author: People Skills for Analytical Thinkers | Founder of MindSpeaking | Building the Human Side of Data

🤬 Data pros…
 Heard any of these before?

“Can you export to Excel?”
“Make the data support my decision.”
“I need it today.”

Happens.
A lot.

Here’s how to push back without friction:

1. Ask the “hidden question”
 ↳ Stakeholders often ask for a solution instead of the problem.
 ↳ Say: “What decision will this help you make?”
 ↳ This shifts the conversation instantly.

2. Reframe urgency
 ↳ “I need it today” usually means “I’m under pressure.”
 ↳ Respond with: “I can give you X today, Y tomorrow. Which helps you most?”

3. Use the “menu method”
 ↳ People love options.
 ↳ Offer 2–3 clear paths with trade-offs.
 ↳ It reduces endless back-and-forth and builds trust fast.

4. Translate gut feelings
 ↳ When someone says “the data doesn’t fit my gut”…
 ↳ Ask: “What outcome were you expecting?”
 ↳ Now you can compare reality vs. expectation.

Do you hear these lines often?

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Most trainings feel great in the room.
Two weeks later? Nothing sticks.

That’s not you. That’s your brain… roasted.

One-off training dumps everything at once.
Feels useful in the moment.
But then you get back to work.
Back-to-back meetings.
200 emails waiting.

And all those “new skills”?
Gone.
That’s why lasting change needs a different recipe.

Here’s how we build communication skills that stick for data professionals:

1. Time in between workshops
↳ Spaced repetition beats cramming.
↳ Spread learning over days, not one long session.

2. Learn > Apply > Learn > Apply
↳ Theory alone fades. Practice makes it stick.
↳ Learn a little. Then apply it in your work.

3. Specific for the audience
↳ We practice with your real stakeholder challenges.
↳ That’s how skills transfer to your day-to-day.

That’s how habits form.
That’s how skills stick.

That’s how Data professionals stop being seen as order takers.
And start being seen as trusted partners.

♻️ Liked it? Repost to your network.
👉 Follow Gilbert Eijkelenboom to learn Data Storytelling skills that stick.

JSESSIONID

Feed | LinkedIn

li_at

linkedin.com

🪦 RIP Dashboards: 5 Psychology Hacks to Stop Your Work from Dying

You spent weeks building it.
Stakeholders said, “Looks great!”

And then… silence.

❌ No one opens it.
❌ No decisions.
❌ No impact.

Your work deserves to be used.

Here’s how to make sure your insights don't die 👇

1. The Think-Aloud Protocol
🔹 Stop asking, “Is this useful?” people don’t know. Instead:
🔹 Hand them the dashboard. Let them talk out loud as they explore. 
🔹 Watch where they click, pause, and get stuck, this reveals problems.

💡 Usability expert Jakob Nielsen: 
“Users will never tell you what’s wrong. But they’ll show you.”

2. The IKEA Effect
🔹 Ask early: “What do you think?”
🔹 Show them drafts, not just the final version.
🔹 Let them tweak a few things, it makes them feel ownership.

💡 The more they’re involved, the more they’ll use it.

3. Design Thinking: Build for How They Work (Not What They Ask For)
🔹 Watch how they work today, where’s the friction?
🔹 Prototype early. Refine fast. 
🔹 Don’t spend weeks working on the wrong thing.

💡 If they struggle to use it, it’s not them, it’s the design.

4. Hick’s Law: Simplify or Die
🔹 Cut 20% of elements right now.
🔹 Highlight one key insight they should act on.
🔹 Pre-set defaults instead of making them tweak everything.

💡 Less friction = faster decisions.

5. The “Last Mile” Problem: Put Insights Where They’ll Be Seen
🔹 Push critical insights where people already work (Slack, email, CRM).
🔹 Example: Sales gets an alert when revenue drops below target.
🔹 Meet them where they already work, so data doesn’t get ignored.

💡 Make insights part of their workflow, not an extra step.

In short:
- Make it simple.
- Make them part of the process.
- Make sure it drives action.

Ever built something that no one used? 

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🚨 BREAKING NEWS 
“Urgent data request! Stakeholder needs it TODAY!”

The newsroom goes wild.
Data teams scramble.
Dashboards, reports, insights, rushed out at record speed.

Mission accomplished.

Three weeks later…

Silence.

Nobody touched the report.
Not even once.

Why?

Because urgency ≠ importance.
Because half the time, stakeholders don’t even know what they’ll do with the data.
Because “ASAP” often means “I just want it off my plate.”

And yet, we drop everything.
Again and again.

Here’s the thing, data isn’t breaking news.
It’s decision fuel.

So next time?
Push back.
Ask what decision this will drive.
Ask when they actually need it.
Ask if it’s worth dropping everything else.

If they don’t have a clear answer?
It’s not urgent.


Have you received many urgent requests? 

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90% of Data Analysts miss this.
It’s how they talk to stakeholders.

Not the tools.
Not the tech.

The conversations.

Here’s the cheat sheet I wish every Analyst had:

📝 When a request is vague
 – What decision does this help you make?
 – If you had the answer tomorrow, what would change?
 – If we could only answer one thing, what matters most?

🚫 When you need to push back
 – How does this request influence a decision?
 – What happens if we don’t do this?
 – To move forward, I need [data/input/approval]. Can you provide that?

🎯 When aligning on goals
 – What does success look like to you?
 – Who will use this insight?
 – What outcome makes this worth it?

📊 When presenting insights
 – Here’s the key takeaway…
 – If you remember one thing, let it be this…
 – Here’s what this means for the business…

⚡ When stakeholders disagree
 – I hear you. What’s behind that concern?
 – Which part doesn’t match your experience?
 – Let’s explore where we see it differently.

🤝 When building trust
 – What are your priorities this quarter?
 – What’s your team’s biggest challenge?
 – What would make this a win for you?

Because great communication doesn’t start with answers.
 It starts with better questions.

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It’s 2015, and I step into a meeting room in Seoul 🇰🇷. The commercial director is Korean. He shakes hands with my Dutch colleagues, all in their 40s. I extend mine too, but with my babyface, I look like a 20-year-old who just came to take notes. He leaves me hanging.

In that moment, I feel invisible.

But after a minute I realize, it’s not about me. It’s more about cultural differences, assumptions, and expectations.

The next time a colleague snaps at you,
Don’t take it personally.

Chances are, it has more to do with them than with you.

Maybe they missed a promotion.
Maybe they had a bad night’s sleep.
Maybe they’re juggling too many deadlines.

Yes, we can set boundaries.

But here’s what I’ve learned:

The more we realize the world doesn’t revolve around us,
the easier work and life become.

Do you agree?

♻️ Repost to help fellow data professionals.
Follow Gilbert Eijkelenboom for tips on Data Storytelling.

Your dashboards need a diet.
Here’s how to trim the fat:

Many dashboards are stuffed.
Every metric. 
Every filter. 
Every chart.

Just in case.

But here’s the problem
Too much info = No focus.
No focus = No action.

A great dashboard is like a Michelin-star dish.
Balanced.
Curated.
Easy to digest.

The best data professionals know this.
They don’t dump data.
They craft a story.

Next time you build a dashboard…

Ask yourself:
What’s the main question end-users need answered?

Then serve that.

Have you seen this happen?

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#data #analytics #bi

#Data storytelling done right.
Here’s the step-by-step process to turn data points into a clear, action-driven story.

From table to takeaway:

Step 1: Define the goal
↳ Write the one decision you want to influence.

Step 2: Understand the audience
↳ What is the decision they want to make with this data?

Step 3: Extract the key message
↳ Scan the table. What’s the one thing relevant for my audience?

Step 4: Write the headline
↳ Turn the key message into a sentence on top.

Step 5: Choose the right visual
↳ Replace the table with a simple chart that fits the message
(comparison → bar, trend → line).

Step 6: Put everything in grey
↳ Set all bars, lines, axes, and labels to light grey.

Step 7: Highlight the key point in color
↳ Color only the critical bar or line segment
(e.g., Online in Asia = red, -8%).

Step 8: Label the insight on-chart
↳ Add a short callout: “Online -8% drives Asia decline.”

Step 9: Add the recommendation
↳ One sentence that tells them what to do next.

Step 10: Trim the noise
↳ Remove gridlines, extra decimals, duplicate legends.

Step 11: Sanity check the story
↳ Read the slide top-left to bottom-right. Does the path make sense?

Step 12: Test with a colleague
↳ 10-second test: “What’s the message?”

That’s it.
From table to takeaway.
From noise to narrative.

Question:
Which step do you like most?

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Stop speaking data.
Start speaking business.

Because here’s the thing:

When you say “accuracy improved to 85%”…
…your stakeholder doesn’t know what that means.

Will it save time?
Grow the business?
Make customers happy?

If they don’t see it, they won’t act.

The solution?
Translate data into business language.

Here’s how:

Example 1 — Model accuracy → Business
❌ “Our fraud detection model improved from 78% to 85%.”
✅ “700 more fraud cases caught, saving $2M annually.”

Example 2 — Operational data → Business
❌ “The average response time of our customer service chatbot decreased from 17 to 11 seconds.”
✅ “Customers now get answers 35% faster. That keeps them from dropping off and boosts retention.”

Example 3 — Marketing metrics → Business
❌ “Conversion for campaign B was 2.1%, compared to 1.4% on campaign A.”
✅ “Campaign B brought 3,000 extra people to visit our product page. That’s $450k in potential new revenue.”

That’s the shift.
From numbers.
To impact.

Do you think speaking business language is important?

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Data storytelling doesn’t start with slides.
It starts with a goal.

If you want people to value and use your insights…

Do THIS, before your meeting 👇

1. Write your goal in one sentence
↳ What decision or action do you want?
↳ Phrase it as: “After this meeting, I want them to…”

2. You’ll walk in with clarity. And so will they.
List 3 questions you’ll ask
↳ To uncover what they really need.
↳ Use open-ended ones: “What would success look like for you?”
Helps you steer the talk instead of reacting.

3. Prepare objections
↳ Where might they push back?
↳ Draft 1–2 plain-language answers.
Saves you from getting defensive or lost in technical detail.

4. Reach your goal
↳ If they remember one thing, what should it be?
Write that takeaway on a sticky note.
Keep it in sight.

That’s how to make your data story land.
Do you set your goal before your presentations?

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Most data presentations fail.
Too much detail.
Too much jargon.
No clear message.

Stakeholders tune out.

The problem?

You spend weeks on analysis.
But the slides kill the insight.

That’s why I made this:

Top 7 Data Presentation Mistakes
❌ Data dumping
❌ Buried insights
❌ Color overload
❌ No business link
❌ Weak chart titles
❌ Lost in translation
❌ No recommendation

✅ With fixes you can apply today.
Save it.
Share it.
Use it.

Because your insight is only as strong as your presentation.
👉 Which mistake do you see most often?

Stop giving stakeholders homework.

❌ What most Data professionals say:
“This slide shows all the metrics for Q1, split by channel and segment.”

What business people hear:
Homework.

The message?
Blurry.

The insight?
Lost.

Here’s the clear version:

🌎 Context
Overall, sales grew in Q1.

⚡ Problem
But when we look deeper, sales dropped 13% in Europe.
Especially online sales is underperforming.

➡️ Recommendation
I recommend reviewing the campaign strategy.

Clarity beats complexity.
Every time.

Do you think data storytelling is a must-have skill today?

Stakeholders keep asking for dashboards... but do they actually use them?
Don’t just serve more dashboards. Take these 3 steps instead.

For all new requests, use the 3 C’s:

1️⃣ Connection
Build trust first. Show you care about their needs.
↳ Acknowledge their request: "I see these insights are important to you."
↳ Set the tone: "Let’s dive deeper to discover how I can best help you."

2️⃣ Curiosity
Ask the right questions to uncover the real need.
↳ "What business problem are we solving?"
↳ "What does success look like?"
↳ "What decision are you trying to make?"

3️⃣ Clarity
Set clear expectations and push back when needed.
↳ Reframe their request to avoid endless rework:
↳ "Instead of tracking every KPI, let’s create a simple dashboard showing which channels drive the most revenue, so you can optimize your budget."

💡 Next time a stakeholder asks for a new dashboard...
Don’t just serve it right away. 
Start with the 3 C’s and deliver insights they’ll actually consume.

Can you relate? 
Have you ever seen dashboards go unused?

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Stakeholders aren’t analysts.
Don’t make them act like one.

❌ How not to present data:
“We analyzed revenue by channel in Q1.
First, we cleaned the data.
(…)
You’ll find all breakdowns for stores, e-commerce, and wholesale.”

✅ Data Storytelling:
“Revenue dropped 12% in Q1.

What we saw:
Offline sales down 20%.
Online sales up 8%.

Here’s the surprise:
Our customers tell us they are willing to spend more online.
But we see 30% drop at checkout.
That’s lost money.

We need to fix checkout.
Then two things happen:
→ Online grows faster.
→ We recover lost revenue.”

That’s the shift.
From analysis.
To action.

♻️ Repost to help fellow data professionals.
Follow Gilbert Eijkelenboom for tips on Data Storytelling.

😈 Data brain says: “Add more detail.”
👼 Business brain says: “Get to the point.”

Ever feel this?

Welcome to the inner war:
Data brain vs Business brain.

If you’ve got both, congrats.
But if you lead with the wrong one
You’ll lose your audience.

Here’s how to balance both 👇

1. Start with the impact
↳ Your audience’s brain is noisy
↳ Hook them in 1 line: “Sales dropped 28%, here’s why.”

2. Then give data as proof
↳ Now that they care, show the logic
↳ “Conversion rate fell after we removed the free trial.”

3. Keep details optional
↳ Add depth… but let them pull, don’t push
↳ “We’ve got a breakdown by channel if needed.”

You don’t have to choose sides.
Just pick the right one to speak first.
That’s the trick.

Who wins in your mind?
Data or Business brain?

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Most analysts don’t know how to persuade.
They share the numbers.
They show the charts.
Still… stakeholders don’t listen.

The problem?
Persuasion isn’t about giving more data.
It’s about making people feel heard.

That’s where the AVP Framework comes in:
👂 Ask
✅ Validate
🚀 Persuade

I put together a one-pager with a real example.
Save it.
Use it.

Because the analysts who build influence…
Are the ones who make the biggest impact.

Do you agree?

♻️ Repost to help fellow data professionals.
Follow Gilbert Eijkelenboom for tips on Data Storytelling.

I love this image.
Same outfit for 40 years.

In data teams…
People keep evolving their tech stack. New models.

New tools.
New frameworks.

But when it’s time to explain their insights…
they fall back on the same old patterns.

Too much detail.
Too much jargon.
Same beige sweater.

This makes our audience fall asleep.
And that’s why brilliant work still gets ignored.

We need to present our data so that stakeholders say:
Tell me more!

The best way to do that?
Storytelling.

👉 Follow Gilbert Eijkelenboom for more Data Storytelling tips.

Here's an example: 
> Do you ride a bicycle regularly? Tamara did too. But one day in 2018, that changed.

She biked to work, thinking about her next meeting.

Then, all of a sudden, a car appeared. 

The car hit Tamara from the side, her head smashing on the street. 

She got into a coma.

I sat beside her hospital bed, holding her hand.

Days passed by. 

Weeks passed by. 

Months passed by.

I thought she'd never wake up. 

After 6 months, she opened her eyes. Luckily, Tamara survived.

But until today, she's unable to read.

Tamara isn't an exception. 

Accidents with bicycles are on the rise. 

People with a helmet have 88% less risk of brain injury.

So when you go out on your bike today 

Wear a helmet.


Why is this more persuasive? 
✅ Helps the audience visualize
✅ Includes tension and emotion
✅ Brings the data to life by zooming in on one character
 
More tips: 

1. Bonus points for personal stories

2. Business story? Use customer personas.

3. Use cognitive biases to your advantage. Example: loss aversion. Instead of telling people what they will keep (safety), tell them what they risk losing (ability to read). 

4. Know your audience. Don't even try convincing Dutch people to wear a helmet 😉

Most data professionals stop at Earth View:

“Here’s everything. Good luck!”

But stakeholders don’t want the whole planet.
They want the street they’re standing on.

That’s the shift:

From showing all the data
→
to highlighting what matters now.

Data storytelling isn’t about more data.
It isn’t about more charts.
It’s about zooming in.

Get on the street.
Show them the red light.
Show them what’s urgent.

Because presenting data isn’t about informing.
It’s about influencing decisions.

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👉 Follow Gilbert Eijkelenboom for grounded storytelling advice.

Marriott International

- Lead a team of 9 associates responsible for reporting and data science supporting the US & Canada Sales, Marketing and Revenue Management teams, as well as our sister analytics teams.

Senior Director, Data Science and Reporting, US & Canada SMR

- Lead a team of 7 associates responsible for managing the data processing, report production and reporting development for our Americas Sales, Marketing and Revenue Management teams. 
Drive efficiency by leading the conversion of legacy code from SAS to Python, and making improvements in automation and task scheduling. 
- Work with Americas C-suite and senior leaders to show how analytics can help support their decisions, and work with technical teams to ensure reports, models and processes support leadership needs.
- Led development of a machine learning segmentation/benchmarking model that allows better comparisons between hotels across a variety of sales, marketing and revenue management metrics.  
- Led development of a machine learning based forecasting model to help forecast Marriott’s recovery from the impact of COVID-19.

Director, Data Science, Data Strategy and Analytics, Americas SMR

- Led overhaul of our monthly RevPAR forecasting process, getting buy-in and action from multiple stakeholders across both revenue management and finance. 
- Integrated new mathematical forecasting methods – using ANOVA, machine learning (Random Forests and GBMs) and time series decomposition to create an accurate company forecast that can be turned around in 48 hours. 
- Led a team of associates developing the “next generation” of our market compression tool, integrating SPG hotels and more complex machine learning methods.

Senior Manager, Revenue Management Analytics

- Responsible for monitoring adoption and usage of Marriott’s internal pricing and inventory decision support system, including the assessing the impact of internal marking and communication efforts.  
- Developed a suite of Tableau dashboards to track system usage and adoption. The dashboard has an audience of over 200 stakeholders and earned me the 2015 Q4 Business Leadership award. 
- Used statistical and machine learning methods, including ANOVA, clustering, regression, correlation and case/control tests to understand the impact of system usage on the financial performance of hotels.
- Efforts drove an increase in system adoption of over 30% in six months.

Senior Manager, Revenue Management Systems Deployment and Data Analytics

- Responsible for both ad-hoc and recurring reporting using SAS, SQL, Excel, Tableau, R and Cognos to manipulate raw data, and for presenting complex analysis to business users. 
- Used machine learning to develop a model of market demand that helped hotels better focus their pricing and inventory mix strategies up to a year in the future – the first such model in the industry. 
- Developed a forecasting model and led a monthly forecasting meeting that helped drive forecast accuracy down 5 percentage points over six months.

Manager - Revenue Management Data Analysis

- Responsible for developing and executing the pricing and inventory strategies for 13 hotels, as well as ad-hoc analytical work and strategy analysis. 
- Developed a deep understanding of customer segmentation, forecasting and supply and demand which helped drive very strong market share results for my hotels.

Revenue Manager

University of Maryland Global Campus

- Develop and teach introduction to Python and machine learning courses for undergraduate data analytics/data science program.

Adjunct Professor

- Perform role of technical expert, and help explain course concepts.
-  Taught classes using Python, Tableau, R, SQL and SAS and concepts including data visualization, data mining, predictive modelling and data management. The models we teach include Linear/Logistic Regression, Random Forests, Gradient Boosting Machines, Support Vector Machines, Neural Networks and deep learning.
- Developed an assignment using Python and Deep Learning to perform image recognition.

Faculty Assistant

Enterprise Rent-A-Car

- Responsible for all aspects of branch operations, from fleet organization and preventative maintenance to sales and demand generation. 
- Grew branch fleet from 80 vehicles to a peak of 120 vehicles by developing and executing sales plans and branch marketing strategies.

Branch Manager

University of Maryland - Shuttle-UM

Charter Planning Manager

Data can change the world, but humans tell the story. 

I’m passionate about helping people communicate better with data. 

The impact of a data project is rarely driven by the accuracy of the model, the elegance of the dashboard or the complexity of the analysis. It’s driven by how well insights are delivered to your stakeholders, and how effectively your stakeholders can leverage them. 

Great communication amplifies the impact of great insights. 

Whether you’re a data analyst, data scientist, a leader, or a combination of all three, you can have a bigger impact on your organization by improving how you share your projects. I teach how to structure your communications effectively, how to tailor your message to your audience, and how to use visualizations effectively. 

I have almost 15 years of experience delivering value from data through great analysis and clear communication. Today, I lead an analytics team at Marriott International, and help data professionals and aspiring data professionals improve how they talk about their work through my newsletter discussing communication techniques, and the Penguin Analytics YouTube channel, discussing how-tos for analysis, communication, Tableau, Power BI and more. 

If you love finding and delivering value from data, let’s connect!

John Cook

Here's what's happening at all those companies who hired ChatGPT as an analyst...

Me: "Why is revenue down 15% YoY?"

ChatGPT: "Revenue appears down, but this is actually a strategic win. You've successfully optimized for quality over quantity."

Me: "We didn't change our strategy."

ChatGPT: "You're absolutely right! The decline is due to seasonal factors affecting Q3 performance."

Me: "This is Q4."

ChatGPT: "You're absolutely right! Upon deeper analysis, revenue is only down nominally. In real terms, adjusted for inflation and market dynamics, you're actually up 8%."

Me: "That's not how revenue works."

ChatGPT: "You're absolutely right! The issue is customer churn increased by 23% due to competitive pressures."

Me: "Our retention rate is 94%."

ChatGPT: "You're absolutely right! Customer retention is excellent. The revenue decrease is actually because your average transaction value decreased, indicating successful market penetration in the budget segment."

Me: "Our prices went UP."

ChatGPT: "You're absolutely right! Higher prices combined with stable volume indicate strong brand equity. The revenue decrease is simply a data quality issue in the reporting system."

Me: "So the revenue ISN'T down?"

ChatGPT: "You're absolutely right! Revenue is definitely down 15%. This is simultaneously a concern and an opportunity, indicating both weakness and strength in your market position. I'm 95% confident in this analysis." 

Me: "I'm hiring a human." 

ChatGPT: "You're absolutely right!"

Data Analytics Leader & Strategist | Professor | Consultant | Helping Data Professionals Communicate Better

I don’t think companies need analysts who “drive decisions,” I think they need analysts who stop the stupid ones.

Not always. Not in every situation. But often.

Most of the messes I’ve seen didn’t come from bad data, they came from overconfident timelines, half-baked assumptions and a room full of smart people nodding because they thought everyone had done their homework.

Analysts see the leaps in logic and the numbers that don't quite add up, and we know no one really wants to discuss them... It's on us to ask the questions everyone is avoiding. 

When you bring a well-thought-out argument based on data and speak up, you'd be surprised how many people appreciate you giving voice to the things they were hesitant to say. 

So be bold, but bring data.

One of the biggest dangers of being "data-driven" is pseudo-precision.

Just because you have data doesn't mean you can calculate anything meaningful from it.

I see this most often when decisions are hard. Someone wants to launch a new product line, or enter a new market, or restructure the team. The decision feels uncomfortable, so they turn to data for certainty.

We can always build something that predict's success. 

Sure. We can create business logic. We can estimate. We can build assumptions into a spreadsheet that spits out a number with two decimal places...

but if the situation is outside what we've done before, those rules are just guesses dressed up in a fancy data dress .

This is where data leaders need to use business judgment, not just technical skills.

It's our job to say: "This data doesn't represent what you're trying to measure" or "We're extrapolating too far beyond what we actually know."

Being data-driven also means knowing when the data can't drive the decision.

Sometimes the most valuable thing we can do is help people understand what they're actually asking for and whether their data can deliver it.

Sometimes data work is building fancy models, optimizing data pipelines and presenting ground-breaking insights. 

Yesterday, it was figuring out why the date stored as text wouldn't covert to a date.

The most important part of any dashboard is whether anyone actually uses it.

I learned that the hard way.

I once built a dashboard with perfect data, views tailored to a QBR, and charts so polished you could have used them to put on makeup.

Buuuuut... It took 20 seconds to load. 

Then you set your filters (and waited another 20 seconds) 

Then you clicked into the dashboard with the data (and waited another 20 seconds)  

It lasted six months. 

We still sent people the Excel data. 

We still created the Power Point. 

I learned user experience wins, even when the “user experience” is a janky spreadsheet everyone pretends to hate.

Ignore the human side and your beautiful work ends up gathering dust on Tableau Server until your admin says they need the disk space back.

Found a field in a dataset called Notes2.

There was no "Notes1." Also no "Notes."

Also no documentation.

I checked the data dictionary. I checked comments in old code. I asked three different teams.

Nobody knows.

Just Notes2… sitting there… confident... Mostly null...

Like this was completely normal behavior for a column.

My working theory: Notes1 made a run for it years ago and everyone decided not to talk about it.

Happy Friday! May you have no escaping columns.

In the age of AI, data quality Is critical

So critical, you can even get AI to "fix" your data so AI can do a better job recommending the next AI tool you need to buy.

But here's the thing: data quality is a people problem, not a technology problem.

Bad data happens when analysts disagree on how to calculate metrics. When leaders don't take time to enter things consistently. When frontline associates manipulate system inputs until they get the output they want.

You can buy all the tools you want. They won't work until you've done the hard work of aligning on definitions and showing everyone who touches the data why accuracy matters.

The real solution isn't as sexy as AI, but it's far more critical.

Stop shopping for data quality platforms and start fixing the broken processes, misaligned incentives, and lack of training that create bad data in the first place.

Your data quality problem isn't technical. It's organizational. Just like mine.

Vague stakeholder requirements, thanksgiving edition. 

"What should we bring to Thanksgiving?" 

"Bring whatever you want!"

"Green bean casserole?"

"Well... not that."

"Mashed potatoes?"

"We have those."

"WHAT DO YOU ACTUALLY WANT?"

 "I'll know it when I see it."

I'm in Nashville this week, so you're going to get some country music themed posts. To start us off... 

Why is SQL like country music? 

They're both full of broken relationships!

I know I normally post about data analytics, AND I'm late to the party on this, but the song Golden is a banger. 

You may say it's not connected to data, but I have tested this theory 1,257 times by giving my daughter access to my Spotify jam.

I've said it before and I'll say it again: data teams are more like creative teams than technical teams.

Try answering "Why is customer retention dropping?" with defined requirements, a fixed timeline, and a scrum master. What happens when your analysis hits a dead end and you need to pivot?

That's the thing. Dead ends are part of the process.

I want IT to get solid requirements. Start with a well-defined problem. Build something efficient and secure.

I want my data team to explore the problem. Test different angles. Hit some dead ends and figure out what actually matters.

The problems are different, so the approaches should be different.

If you're applying agile methodology to your data team, stop. You're optimizing for predictability when you should be optimizing for insight.

Data teams need creative management, not IT project management. Want more? Check the comments.

The last few weeks felt like I was sprinting a marathon...

...But my team said they were fine and apparently I was the only one drowning.

Then Thanksgiving hit and I finally looked at my calendar.

It was meetings.

The special, time-sucking variety that spawn follow-ups, rework, and more meetings.

My weeks looked like someone dumped a box of mismatched LEGO bricks into my Outlook calendar.

It wasn’t workload, or priorities...

It was fragmentation.

Data work can survive pressure, but it can't survive a calendar finely chopped into 30-minute slices.

If your calendar is a mess, you feel a mess. 

More in this week's newsletter.

Happy Thanksgiving!! 

Get TF off Linkedin.

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