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Start by defining the prediction
Before assembling data, specify what the system should predict, which person or entity each prediction concerns, when it will be made, and what decision will use it. For example, “predict customer churn” is incomplete unless the team defines the customer population, the prediction date, what counts as churn, and the future period in which churn must occur.
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Each training example should pair the information available at its prediction point with an outcome that can be established later. That outcome is the target, or label. Its definition must be consistent across records: if “late payment” means 30 days overdue in one part of the dataset and 60 days in another, the model is being taught conflicting answers.
The prediction horizon matters, too. A forecast for next week and a prediction of whether an account will close within a year are different tasks. Their labels, relevant historical window, evaluation method, and deployment conditions may differ.
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Which data belongs in the dataset?
Predictors available when the agent makes its prediction
Predictors—also called features—are the inputs used to estimate the target. Include only information that would actually be available at the moment of prediction. A feature that appears useful in historical records but arrives after that moment creates data leakage: offline evaluation can look strong even though the agent cannot use the signal in practice.
For instance, a model intended to flag a likely missed payment before a due date should not use a field populated only after the payment is missed. Write down the prediction-time cutoff for each feature and check that the same rule is enforced in training and live use.
Timestamps and entity identifiers where relevant
Keep timestamps when order, recency, seasonality, or a future horizon matters. Retain stable entity or series identifiers when records belong to customers, devices, locations, products, or other recurring units. These fields help establish which observations belong together and when they occurred; they are not automatically useful predictors in every model.
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For forecasting, Google Cloud’s Gemini Enterprise Agent Platform documentation calls for a target, a time field, and a time-series identifier, along with consistent observation intervals and a narrow/long data format. Those are requirements for that platform’s forecasting workflow, not universal rules for every predictive system. Other tasks and platforms may use different schemas.
Representative outcomes and reliable labels
Historical outcomes should reflect the intended prediction population and relevant operating conditions. Check whether important groups, rare events, or changing conditions are missing or underrepresented. A dataset dominated by ordinary cases may not tell you whether a system can identify an uncommon but consequential outcome.
Also examine how labels were produced. Human review, delayed outcomes, policy changes, and inconsistent source systems can all affect label quality. Record the label definition and how it was assigned so that later model results can be interpreted against the same meaning.
Derived features that can be reproduced
Lagged values, rolling historical aggregates, calendar indicators, and geographic measures can be useful when they reflect information available at prediction time. Their calculation must be repeatable in both training and deployment. A historical aggregate, for example, must exclude events that had not yet happened at the prediction cutoff.
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How should the data be checked and split?
Profile and validate before training
Check the records and labels for missing values, invalid values, duplicates, inconsistent categories, and unexpected changes in schema. Confirm that timestamps and identifiers are valid where needed, and that the target is populated and defined consistently. Investigate errors that could change the meaning of an input or outcome rather than treating every anomaly as a harmless formatting issue.
For each feature, document its source, definition, timing, permitted values, and transformation. Fit transformations—such as imputation rules or scaling—using training data, then apply the fitted transformations to validation and test data. This prevents information from the evaluation sets from influencing the training process.
Keep training, validation, and test data separate
Training data is used to fit the model; validation data supports model and setting choices; test data is held back for a final check. Do not use test results to tune the model and then report those results as an independent assessment. Google’s predictive ML guidance recommends separate holdout testing, representative splits, documented preprocessing, and experiment tracking.
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- Future-period predictions: use chronological splits so later observations are not used to predict earlier ones.
- Predictions for new entities: keep the same entity out of multiple splits when deployment requires generalizing to entities the model has not seen.
- Repeated observations of known entities: choose a split that reflects whether deployment predicts later records for those same entities or a different population.
These choices depend on the prediction task and intended use. A split that allows near-duplicate records or future information to cross into training can make evaluation unrepresentative.
How much data is enough?
No single number of rows proves that a dataset is adequate. Sufficiency depends on the target, feature count, event frequency, prediction horizon, population variability, and the task’s ability to generalize beyond the examples used for training. A large dataset can still be unsuitable if its labels are unreliable or its records do not resemble deployment conditions.
Google Cloud’s Gemini Enterprise Agent Platform documentation gives the following platform-specific limits and heuristics. The reviewed pages do not state a publication date for these figures; they are not general guarantees of accuracy or universal minimums.
| Use in that platform | Documented figure | How to interpret it |
|---|---|---|
| Tabular dataset | At least 1,000 rows | Platform guidance; the documentation cautions that this may still be insufficient for a high-performing model, depending on the number of features. |
| Classification | At least 10 rows per column | A platform heuristic, not a substitute for checking class representation or whether evaluation supports the intended use. |
| Regression | At least 50 rows per column | A platform heuristic, not a universal sample-size rule. |
| Forecasting: series coverage | At least 10 time series for every feature column used | A platform-specific requirement for that forecasting workflow. |
| Forecasting: supported dataset size | 3–100 columns; 1,000–100,000,000 rows; no more than 3,000 time steps per series | Documented platform limits, not a definition of how much data a reliable forecast needs. |
Use such limits to determine whether a particular platform accepts a dataset, not to conclude that a model will perform well. Assess whether the data covers the relevant outcomes and population, then test predictive performance against a simple baseline on held-out data.
How can you tell whether the data supports useful predictions?
Evaluate the model on data that was not used to fit it or choose its settings, using metrics that match the task and the cost of errors. Compare it with a simple baseline—for example, a straightforward historical or majority-class prediction—so that a complex model must demonstrate value beyond an uncomplicated alternative.
Look beyond a single overall score. Examine meaningful population slices, such as relevant customer groups, regions, time periods, or outcome classes. Overall performance can conceal weak results for a group that matters to the decision. Google’s predictive ML guidance recommends evaluating representative splits and considering whether predictive effectiveness is similar across data slices.
Record the data schema, feature definitions, transformations, split logic, model settings, and evaluation results. This makes comparisons reproducible and helps explain whether a change in performance came from a new model, changed data, or a different evaluation setup.
What does the AI agent need beyond the dataset?
An agent that performs predictive analytics also needs reliable, authorized access to the right sources and clear instructions about what their fields mean. Prefer authoritative sources, stable query or API access, and controls that limit access to approved data. Traceability matters: teams should be able to identify which source and transformations contributed to an analysis.
Google’s reference architecture describes separate analytics, database, and machine-learning agent roles, using BigQuery and AlloyDB as example sources. Microsoft’s guidance likewise emphasizes accessible, authoritative, and governed data. These are vendor examples, not requirements to use those products or to build a multi-agent system.
Operationally, plan to monitor input quality, data distributions, and prediction performance as outcomes become available. Assign responsibility for investigating problems and decide how features or models will be refreshed. The guidance reviewed here does not establish a universal monitoring interval or alert threshold; those need to be set for the system’s risk, feedback delay, and operating conditions.
Which requirements change with the prediction task?
| Task | Data considerations | Evaluation emphasis |
|---|---|---|
| Classification | A defined category or event label; enough representative examples of relevant classes to assess them. | Choose metrics suited to the decision and check performance on important classes and population slices. |
| Regression | A consistently defined numeric target and predictors available at the prediction point. | Use error measures suited to the target’s scale and the consequences of over- or under-prediction. |
| Forecasting | A target over time; timestamps and series identity where applicable; consistent time intervals or explicit handling of gaps. | Respect chronology and test at the horizon and under conditions expected in deployment. |
| Ranking | Examples that preserve the items or candidates being compared and the outcome that defines a useful ordering. | Assess whether the ordering supports the intended decision, rather than relying only on a metric for a different task. |
These are practical distinctions, not a complete schema specification. The exact fields and metrics depend on the model, decision, and deployment setup.
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Governance means more than granting the agent access. Define who may use each source, what data may be queried, how outputs and analytical steps are recorded, and who owns data or model problems. Keep field definitions and provenance available to people who review the predictions.
The Australian Government Digital Transformation Agency’s AI Technical Standard summary marks data-quality criteria, purpose-aligned data selection, representative model data, and separation of training, validation, and test datasets as required within its stated context; it lists practices such as profiling, checking label quality, and data engineering as recommendations. Its applicability depends on the system and jurisdiction, so it should not be treated as a rule governing every organization.
Quick Recap
Practical readiness checklist
- The prediction target, population, prediction time, and horizon are written down.
- Labels have a stable definition and reliable provenance.
- Every predictor is available at the intended prediction time and can be generated consistently in production.
- Timestamps and entity or series identifiers are retained where the task requires them.
- Records have been checked for missing, invalid, duplicated, or inconsistent data.
- Training, validation, and test sets reflect the deployment scenario and prevent leakage.
- Performance is compared with a baseline and reviewed across relevant slices.
- Data definitions, transformations, split logic, and experiment settings are documented.
- The agent has authorized, dependable access to appropriate sources, with operational monitoring and ownership assigned.
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