Define data requirements, and gather and validate information, applying judgment and statistical tests.

“Define data requirements, and gather and validate information, applying judgment and statistical tests.” is a core task performed by Operations Research Analysts. Among the occupation's 16 rated tasks, workers place it 15th by importance (#2 most important). About 95% of workers say it is relevant to their job.

This is a single occupation-specific task statement from O*NET. The figures below describe how central the task is to the job and what independent studies measure about AI and this kind of work — not a prediction that the task will be automated.

Work activities this task rolls up to

O*NET groups concrete tasks into broader work activities shared across many occupations.

• Evaluate the quality or accuracy of data. in Processing Information
• Analyze scientific or applied data using mathematical principles. in Analyzing Data or Information
• Determine operational methods or procedures. in Making Decisions and Solving Problems

AI exposure

The OpenAI / Eloundou “GPTs are GPTs” study rates this task E2. Exposure with tools — software built on top of a language model (not the model alone) could cut the time by at least half.

Exposure measures whether a model could meaningfully speed the task up — it is an estimate of overlap with model capabilities, not a measure of whether the work will be done by software. The study's intermediate score (β) for this task is 0.50. Automation potential label: T2.

How AI is actually used on this kind of task

The Anthropic Economic Index observes how people actually use AI on tasks like this one across millions of real conversations.

• 0.011% share of AI-use records mapped to this task
• Average autonomy of the AI: 3.1 (1–5; higher = more autonomous)
• 92% of interactions still needed a human in the loop

Observed AI use describes people choosing to use AI as a tool on this kind of task today. It is augmentation and assistance, not a measure of jobs replaced.

Other tasks in this occupation

• Present the results of mathematical modeling and data analysis to management or other end users. · importance 4.6
• Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary. · importance 4.5
• Prepare management reports defining and evaluating problems and recommending solutions. · importance 4.5
• Collaborate with others in the organization to ensure successful implementation of chosen problem solutions. · importance 4.4
• Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters. · importance 4.4
• Observe the current system in operation, and gather and analyze information about each of the component problems, using a variety of sources. · importance 4.4
• Analyze information obtained from management to conceptualize and define operational problems. · importance 4.3
• Study and analyze information about alternative courses of action to determine which plan will offer the best outcomes. · importance 4.3
• Collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives. · importance 4.3
• Specify manipulative or computational methods to be applied to models. · importance 4.0
• Design, conduct, and evaluate experimental operational models in cases where models cannot be developed from existing data. · importance 4.0
• Develop and apply time and cost networks to plan, control, and review large projects. · importance 3.8
• Break systems into their components, assign numerical values to each component, and examine the mathematical relationships between them. · importance 3.7
• Educate staff in the use of mathematical models. · importance 3.6

See all tasks on the Operations Research Analysts page.

Sources for this page

Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.

• O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
• Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27) Anthropic
• “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic

Data compiled June 2, 2026. Figures are estimates, not advice.

@misc{singulariki-task-7382,
  title  = {Define data requirements, and gather and validate information, applying judgment and statistical tests.},
  author = {{Singulariki}},
  year   = {2026},
  note   = {O*NET 30.3; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
  url    = {https://singulariki.com/tasks/task-7382}
}