Rebuilding My Tableau Practice Sets - Beyond Perfect Datasets
Hey there!
I've been getting a lot of messages lately about my old Tableau practice sets (you know, the Superstore ones).
The feedback has been consistently positive, but there's also been a pattern:
"These were great for learning the mechanics, but my real work data is... messier."
"I can solve the practice problems easily, but when my manager asks for quick insights from our actual systems, I feel lost."
"Why is real data so much harder to work with than the training datasets?"
That last question got me thinking. A lot.
The Problem with Perfect Practice
Most Tableau training (including my own previous work) uses sanitized datasets like Superstore and very structured, clear, step-by-step instructions.
Everything makes sense. Values add up correctly. Categories are consistent. No mysterious gaps or weird outliers.
The exercises are straightforward: "Create a bar chart showing sales by region."
But that's not real life, is it?
Real life is your manager dropping by at 9 AM saying
"Can you quickly show me which products drove our revenue last quarter? I have a call with the product team at 10:30."
Real life is figuring out what "quickly" means, what level of detail they need, and whether those negative sales amounts are errors or returns - all while the clock is ticking.
What Real Data Actually Looks Like
Here's what people discover when they move from practice datasets to actual company data:
Product names that follow three different conventions (probably from system migrations over the years).
Returns that show up as negative sales, but mixed with what might be actual data errors.
Dates that don't make sense.
Categories that overlap in confusing ways.
The real challenge isn't making charts.
It's making decisions about messy data under time pressure.
It's knowing when to dig deeper vs. when to work with what you have. It's communicating findings with the right level of confidence given what you know and don't know.
These are analytical thinking skills that transfer whether you're using Tableau, Excel, or presenting findings in a meeting.
What I'm (Re)Building Instead
I'm completely rethinking how I approach data education. Not just the technical skills, but the analytical thinking that makes you someone people trust with important questions.
I'm starting with a free 5-day email course that focuses on realistic workplace scenarios, messy datasets, and critical thinking challenges. This focuses on Tableau, but the learnings will go way beyond any single tool.
This will be especially valuable if you're:
Starting a new role and want to handle data requests confidently from day one
Switching industries and need to understand how different businesses use data
Preparing for interviews where they'll ask about your analytical process
Want to demonstrate practical problem-solving skills, not just technical knowledge
Here’s a sneak peek:
Scenario: "The Monday Morning Question"
It's Monday morning, 9 AM. You're settling in with your coffee when your manager stops by your desk.
Manager: "Hey, quick question. Can you show me which products drove our revenue last quarter? I have a call with the product team at 10:30 and need to know where we're winning."
Your mission: Create a simple bar chart that clearly answers this business question - but first you need to interpret what they actually need.
What you'll discover: Some negative sales amounts in the data (returns? data errors?), inconsistent product naming, and the difference between showing what they asked for vs. what they probably need.
Time: 15 minutes (because that's how long you actually have)
Notice the difference? You're not just learning Tableau mechanics. You're learning to think under pressure, decode unclear requests, and provide reliable insights when it matters.
These analytical and communication skills transfer to any tool, any role, any industry.
The Skills That Actually Matter
When you practice with realistic scenarios like this, you develop:
Analytical judgment - recognizing when data patterns make sense and when they don't
Business intuition - understanding how analysis will be used and adjusting approach accordingly
Communication confidence - explaining what the data shows without overselling or underselling
Practical decision-making - balancing thoroughness with deadlines
These capabilities transfer to any tool, any industry, any analytical context.
Why This Matters Now
In the age of AI, basic chart-making is becoming commoditized.
What's increasingly valuable is the judgment to interpret unclear requests, know when to trust data vs. dig deeper, and present findings with appropriate confidence levels.
These are human skills that complement AI tools rather than compete with them.
Still Figuring It Out
Well, this is still a work in progress. I'm experimenting with different approaches, testing scenarios with real professionals, and refining based on what actually helps people in their jobs.
But I'm excited about where this is heading.
Instead of teaching you to be really good with perfect data and artificial exercises, I want to help you be confident with messy, realistic, workplace complexity.
Because that's what's waiting for you on Monday morning.
What do you think?
Have you experienced the gap between tutorial scenarios and real workplace requests? What situations would be most helpful for you to practice?
Hit reply and let me know. Your feedback often becomes the seed for the next breakthrough.
Chat soon,
Donabel
P.S. If you've worked through my previous practice sets, thank you for the feedback that's helping shape this new direction. Your real-world perspectives are what make this possible.
P.P.S. Speaking of practical workplace skills - "Build to Sell: 7 Days to Your First Data Product" just launched and early bird pricing is still available through this week. It includes bonus templates for product validation, pricing strategies, and even a mini-course on using AI beyond the basics. If you've been thinking about monetizing your data expertise, this might be worth a look.