INFORMS CAP key terms

The step of turning a vague business complaint into a precise, answerable question, including who decides, what action the answer will change, and what counts as success.

The outcome the organization actually wants, such as lower churn or faster cycle time, stated independently of any particular model or algorithm.

A measurable quantity agreed before work begins that indicates whether the intervention achieved the business objective, including thresholds for acceptable and unacceptable performance.

A key performance indicator: a tracked operational number leadership already monitors, useful for tying an analytics project to existing accountability and reporting structures.

A framing of the work as a choice among actions under uncertainty, identifying available levers, constraints, and consequences of each option.

Identifying everyone affected by or able to block the project, along with their interests, influence, and information needs, so engagement and communication can be planned deliberately.

Structured interviewing and observation to surface what users need the solution to do, including tacit constraints, edge cases, and tolerances that are rarely written down.

Analysis that summarizes what happened in historical data, such as trends, distributions, and segment comparisons, without claiming causal or predictive power.

Modeling that estimates unknown or future quantities from observed inputs, producing scores, forecasts, or classifications used to inform decisions.

Analysis that recommends actions, typically by combining prediction with optimization or decision rules subject to business constraints.

A mathematical representation of a decision with an objective function, decision variables, and constraints, solved to find the best feasible alternative.

Imitating a system's behavior many times under uncertainty to estimate distributions of outcomes, useful when closed-form analysis is impractical.

A simulation technique that draws random samples from assumed input distributions and aggregates the resulting outcomes to quantify risk and variability.

Testing how conclusions change as inputs, assumptions, or parameters vary, revealing which uncertainties matter most and how robust a recommendation is.

A randomized controlled experiment comparing outcomes between a treatment group and a control group to estimate the causal effect of a change.

Methods for estimating the effect of an intervention from observational or experimental data, explicitly addressing confounding rather than relying on correlation alone.

Documentation of where data came from, how it was collected, and what transformations it underwent, supporting reproducibility, auditability, and trust in downstream results.

A traceable map of how data flows from source systems through transformations into reports and models, used to diagnose errors and answer audit questions.

The fitness of data for its intended use, judged across dimensions such as accuracy, completeness, consistency, timeliness, and validity.

Systematically examining a dataset's structure and content, including value ranges, frequencies, null rates, and key uniqueness, before analysis begins.

Iterative visual and summary investigation of a dataset to reveal distributions, outliers, relationships, and anomalies that guide later modeling choices.

Absent values in a dataset, which may be missing completely at random, conditionally, or systematically, each requiring different handling to avoid bias.

Filling in missing values using rules or models, such as mean substitution or regression-based estimates, trading bias risk against loss of records.

A third variable that influences both the supposed cause and the outcome, creating a spurious association if not adjusted for in analysis or design.

Systematic distortion arising when the data collected are not representative of the population the model or conclusion is meant to apply to.

Creating, transforming, or selecting input variables so that patterns relevant to the target become easier for a model to learn.

Partitioning data into a portion used to fit a model and a held-out portion used to estimate how well it generalizes to unseen cases.

Repeatedly splitting data into folds, training on some and validating on others, to obtain a more stable estimate of out-of-sample performance.

Fitting a model so closely to training data, including its noise, that it performs poorly on new data despite strong apparent training accuracy.

Adding a penalty for model complexity to the objective, discouraging extreme coefficients and improving generalization, as in ridge and lasso methods.

Independent assessment that a model meets its intended purpose, covering performance, assumptions, stability, and fitness for the decision it will support.

The tension between models too simple to capture real structure and models so flexible they chase noise, both producing avoidable prediction error.

Moving a validated model into production so its outputs reach decision-makers or systems reliably, with monitoring, versioning, and rollback provisions in place.

Ongoing tracking of a live model's inputs, outputs, and performance to detect degradation, misuse, or changed conditions requiring retraining or retirement.

Degradation of model performance over time as the relationship between inputs and outcomes, or the distribution of inputs, changes in the real world.

The potential for harm or loss from decisions based on incorrect or misused model outputs, managed through validation, governance, and documented limitations.

The policies, roles, and controls governing how models are developed, approved, changed, and retired, ensuring accountability and auditability across their lifecycle.

A maintained register of an organization's models recording purpose, owner, inputs, validation status, and dependencies, enabling oversight and impact assessment.

The ability of an independent party to obtain the same results from the same data, code, and documented environment, a baseline requirement for credible analytics work.

Systematic error in data, measurement, or modeling that pushes results consistently in one direction, distinct from random variation.

The property that a model's outputs do not produce unjustified disparate impact across groups, assessed with explicit criteria chosen in consultation with stakeholders.

Openness about how a model works, what data it uses, and what it cannot do, enabling affected parties and reviewers to understand and challenge its outputs.

The degree to which a model's reasoning or the drivers of a specific prediction can be communicated understandably to users, regulators, and affected individuals.

Handling personal information lawfully and ethically through minimization, consent, access control, de-identification, and limits on secondary use.

The obligations practitioners owe clients, employers, and the public, including honesty about uncertainty, competence limits, conflicts of interest, and the consequences of recommendations.