I once flew all the way to Australia just to take pictures of plants.
Not just any plants.
I was collecting images of crops in different farms for an AI-powered, robotic weeding system we were building at my former ag-tech company, Blue River Technology.
Our team spent weeks in Toowoomba gathering thousands of images using a custom-built push-cart under the hot, Aussie sun. We needed these to train our machine learning model, and we felt pretty good about all of the data we had.
Then reality hit.
When we tested the system back home, it struggled badly. The problem? Our model was overly trained on Australian farms, which look nothing like the ones in California. All that work, and we’d basically built an AI system that mostly knew the outback.
We had to start over—cleaning data, rebalancing datasets, retraining models, and learning a painful lesson about what really makes or breaks AI projects.
I share this story because data is such a critical piece of the AI puzzle (and nobody should have to fly across the globe to figure that out).
Data is a silent killer
After years of wrestling with data, what I’ve noticed is that even well-planned AI initiatives collapse without proper data foundations. Data issues are particularly devastating because of a few reasons (though there are others).
First, AI systems can only be as good as their data. Unlike traditional software bugs that can be fixed with code changes, data quality problems require complete retraining of models. I’ve seen a retail company build an entire product recommendation system only to discover their historical data was missing all canceled orders. This ultimately meant their AI kept recommending products customers frequently returned.
Second, data problems compound over time. What starts as a minor data gap during planning can balloon into major budget overruns by deployment. I know a healthcare provider that started with a seemingly minor data inconsistency (different departments recording blood pressure in different formats) that had a painful project delay when they had to clean the data later.
Third, insufficient data frequently kills otherwise viable projects. Sometimes discovering that you don’t have enough data brings you back to square one. A friend’s manufacturing company invested $200,000 developing an AI system to predict equipment failures, only to discover six months in that they didn’t have enough historical sensor data to capture seasonal patterns. As a result, they ran out of budget and the entire project was shelved. Oops.
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Why common data solutions fall short
In a lot of these cases, companies scramble to address these data problems by:
- purchasing external datasets
- launching intensive data collection campaigns
- applying advanced cleaning techniques
But these approaches create an illusion of progress while actually introducing new problems or postponing the inevitable.
Purchased datasets rarely match your specific business context, creating a mismatch between the data and your actual needs. Rushed data collection campaigns often sacrifice quality for quantity, giving you more data that’s just as problematic as what you started with. Finally, data cleaning is often a maintenance-heavy process that requires ongoing resources — something that many teams don’t have the luxury of doing.
There’s a better way
Instead of waiting for data disasters to derail your project, here are a few practical alternatives.
Start with a focused data audit
Before writing a single line of code, create an inventory of exactly what data you need, collect small samples from each source, and check for basic issues like missing values or inconsistent formats. Use a simple red/yellow/green scoring system to visualize your readiness.
Use a “Minimum Viable Data” approach
Identify the smallest subset of high-quality data that can deliver meaningful results. Define the critical 20% of data that will drive 80% of your AI model’s value, then focus your efforts exclusively on that core data. Start small, prove value, and expand from there.
Create data quality “gates”
Instead of complex cleaning after collection, implement basic quality checks at the point of data entry. Think of it as installing a filter before you store your data. Add 3-5 validation rules at data entry points and monitor any pattern changes. Remember: fewer, better records are more valuable than many flawed ones.
Ready to take the next step?
If you’re planning or already implementing an AI solution, take a step back and ask: “Do I have the right data?”
This would have been a great question for me to ask in Australia. I blame the sunstroke…
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