Fundamentals GUIDE
AI & Data
Data is the recorded information a machine-learning system learns from or processes.
Listen to this guideAI-narrated · ~2 min
On this page2 min read
Overview
Its usefulness depends on relevance, measurement quality, permissions, and coverage of the intended task. More records do not automatically correct systematic errors or missing populations.
Key takeaways
- Define the unit of an example.
- Use only information available at prediction time.
- Track data provenance, missingness, and subgroup coverage.
Deep Dive
Start by defining what one example represents. A row might describe a customer, a transaction, a photograph, or one moment in a time series. Those units determine how duplicates, labels, and evaluation splits should work. Ten measurements from one device are not necessarily ten independent devices.
Features are inputs available to the model. Labels are target outcomes used in supervised learning. Check when each feature becomes available: a cancellation reason recorded after a customer leaves cannot fairly predict that departure beforehand. This is a form of leakage even when the field looks highly predictive.
Inspect missing values, annotation disagreements, unusual ranges, and changes in collection methods. Missing information can carry meaning; replacing every missing value with zero can conflate an unknown quantity with a real zero. Document the treatment and test it on representative examples.
Record provenance and access rules alongside the dataset. A public URL alone does not establish permission to reuse every item for every purpose. Collect only information needed for the task and define retention and deletion procedures. Evaluate separately on groups or conditions where errors would otherwise disappear inside an overall average.
04Worked example
Find leakage in a cancellation dataset
Imagine records with signup date, monthly usage, cancellation date, and cancellation reason.
To predict cancellations at the start of June, freeze every input at that date. Remove reasons and dates recorded after the prediction time.
Train on earlier periods and test on a later untouched period. Compare results with and without the leaked fields.
What it shows
This hypothetical design exercise identifies an invalid shortcut before a flattering score becomes a deployment decision.
Real-World Implementation
Separate multiple photographs of the same object before splitting a recognition dataset.
Flag a sensor reading outside the physically plausible range for review.
Putting it into practice
Why it matters
- Clearer decisions. It helps you separate clear technical claims from marketing language.
- Cost and budget. You can ask better implementation questions before spending money or time.
- Team and workflow. Teams with shared understanding make better product, policy, and learning decisions.
Watch out for
- Different teams may use the same term differently, so define scope early.
- Benchmarks can look strong while real-world performance is uneven.
- Ignoring data quality and evaluation plans often creates fragile outcomes.
Next steps
- Start with a plain-language definition of the outcome you need.
- Pick one success metric and one failure condition before testing.
- Run a small pilot with representative data, not a polished demo set.
- Document where AI & Data helps and where simpler methods are better.
Sources and further reading
- GoogleDataset characteristics
Keep Exploring
Free newsletter
Get the daily AI briefing
Three verified AI stories every weekday morning, written in plain English. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the AI & Data quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Frequently asked questions
Can a large dataset still be poor?
Yes. Duplicated, mislabeled, irrelevant, or systematically incomplete records can make a large dataset unsuitable for the intended task.
Keep learning
Related guides
More guides picked for this topic