Data Requirements for ML Projects
Sarah Rodriguez
Feb 18, 2026
6 min read
Machine Learning
One of the first questions we ask potential clients is "tell me about your data." The answer determines whether ML is viable and what approach makes sense. Here's what we look for.
Volume matters but not as much as you might think. You need enough examples for patterns to emerge, but "enough" varies wildly by problem. Some tasks work with hundreds of examples, others need millions. The complexity of the pattern you're trying to learn is the key variable.
Quality beats quantity. A thousand clean, well-labeled examples often beat a million messy ones. Before you collect more data, invest in understanding and improving what you have. What does "good" look like? How consistent are your labels?
Historical depth helps. If you're predicting future behavior, you need enough history to see how past behavior relates to outcomes. Three months of data might be plenty for some problems and nowhere near enough for others.
Accessibility is often the hidden blocker. The data exists, but it's in spreadsheets on someone's laptop, or in a legacy system without APIs, or spread across five different databases with no common identifier. Data engineering becomes the first project.
Finally, consider the feedback loop. Once your model is deployed, how will you know if it's working? You need some way to collect outcome data. The best ML systems get smarter over time because they learn from results.
Volume matters but not as much as you might think. You need enough examples for patterns to emerge, but "enough" varies wildly by problem. Some tasks work with hundreds of examples, others need millions. The complexity of the pattern you're trying to learn is the key variable.
Quality beats quantity. A thousand clean, well-labeled examples often beat a million messy ones. Before you collect more data, invest in understanding and improving what you have. What does "good" look like? How consistent are your labels?
Historical depth helps. If you're predicting future behavior, you need enough history to see how past behavior relates to outcomes. Three months of data might be plenty for some problems and nowhere near enough for others.
Accessibility is often the hidden blocker. The data exists, but it's in spreadsheets on someone's laptop, or in a legacy system without APIs, or spread across five different databases with no common identifier. Data engineering becomes the first project.
Finally, consider the feedback loop. Once your model is deployed, how will you know if it's working? You need some way to collect outcome data. The best ML systems get smarter over time because they learn from results.
Written by
Sarah Rodriguez
AI Engineer at APPTAILOR