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If a model underperforms, check whether its training data and evaluation reflect the real task before spending more time on architecture or hyperparameters. That is a troubleshooting heuristic, not a rule that data work always wins: data-centric and model-centric work solve different problems and are most useful together.
What “fix the data” means
Data-centric AI is the systematic design and engineering of data for an AI system. It is more than collecting a larger dataset. A 2024 review distinguishes data refinement—making existing data better—from data extension—adding data to improve quantity or coverage. Both quality and quantity can matter, depending on the task. The review’s framework focuses mainly on supervised machine learning, while noting that data-centric methods also apply to unsupervised and reinforcement learning.
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Refine what you already have
Refinement can mean correcting mislabeled examples or inaccurate features, finding duplicates or low-quality records, and improving how relevant edge cases are represented. The goal is not to make the dataset look tidy at any cost. A rare example may be a valid and important case rather than an error, so domain knowledge matters when deciding what to remove or correct.
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Extend to cover what is missing
Extension can add observations, features or labels to address blind spots in the current data or changes in the population the model must serve. More examples help only when they add useful coverage or information; adding data that repeats the same limitations does not resolve them.
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First check whether your evaluation represents the real task
A score is only useful if the test data measures the problem you care about. A random test split drawn from the same pool as training data may show how well a model fits that sampled pool without showing whether it solves the underlying deployment task. Google Research’s DataPerf overview calls out this distinction and the importance of designing test data around the real task.
Before interpreting a score, check whether the evaluation reflects the deployment population, relevant subgroups and time period. If the model will face future data, an evaluation limited to a random split from a past pool may not reveal how it will perform under that shift. DataPerf’s benchmark work treats data selection and cleaning as part of the development problem, rather than assuming datasets must remain fixed; its first iteration included five benchmarks across data-centric techniques and modalities. The NeurIPS 2023 benchmark paper describes that suite.
Diagnose the likely failure before choosing the next intervention
Use the evaluation and a data profile to identify the most plausible source of failure. These checks help separate data problems from a model that needs different capacity or configuration:
- Labels: Are examples mislabeled, ambiguous or labeled inconsistently? Prioritize cases where an error would have meaningful consequences.
- Examples and features: Are there duplicates, low-quality records, missing values or inaccurate features that could affect the task?
- Coverage: Are important edge cases, groups or conditions underrepresented? Is a supposed outlier actually a valid rare case?
- Evaluation fidelity: Does the test set represent the intended deployment task and the populations or periods that matter?
- Constraints: What will the next step cost in expert review, annotation, engineering time and compute?
When labels or unusual cases are hard to judge, involve domain experts. Automated tools can help surface candidates for review, but they do not replace task knowledge in distinguishing an invalid record from an informative edge case. Manual review is limited, so focus it where corrections are likely to matter.
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Run a controlled data-versus-model iteration
- Define the deployment task and success measure. Specify what the system must do and how success will be judged. Confirm that the evaluation data reflects the relevant deployment population, subgroups and time period.
- Profile the training and test data. Look for label errors, duplicates, low-quality examples, missing or inaccurate features, and underrepresented cases. Keep the intended task in view when deciding whether an unusual record is a problem.
- Review ambiguous cases with experts. Use domain knowledge to judge uncertain labels and edge cases, prioritizing cases where an error would be consequential.
- Change one thing at a time where practical. Version the dataset and record the model and data versions used for each run. Compare each change against the same task-relevant evaluation so its effect is interpretable.
- Move to model changes when the data checks are adequate. If the evaluation is credible and data quality and coverage are sufficient, investigate model choice, architecture and hyperparameters. Data and model work are complementary, not competing doctrines.
When deciding where the next unit of effort belongs, weigh the likely failure source, the test set’s fidelity to the task, access to domain experts and annotation costs, and compute and engineering costs. Also check whether an apparent improvement holds across relevant groups and time windows; that is a practical safeguard against distribution shifts, not a result quantified by the studies cited here.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What published results do—and do not—show
One 2024 image-classification study reports an improvement of at least 3% in its tested ResNet-18 experiments. The authors used duplicate removal, noisy-label correction and augmentation on MNIST, Fashion MNIST and CIFAR-10. That is a study-specific result, not a gain to expect from data cleanup on another model or dataset. The Scientific Reports paper describes the experiments.
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A 2025 tabular-data study examines 19 machine-learning algorithms and six data-quality dimensions across classification, regression and clustering. Those figures describe the study’s scope; they do not establish a general effect size for improving data quality. The Information Systems paper covers that analysis.
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