# Data sourcing & governance > Language-data specifications, acquisition strategies, rights, provenance, documentation, and stewardship controls. [Multimodal data curation capability](https://admas.net/capabilities/data-curation/index.md) ## The challenge Data can be technically accessible but unsuitable, unrepresentative, poorly licensed, or impossible to explain once it enters a training pipeline. We define what the dataset must represent before choosing how to source it. Acquisition options are evaluated against language coverage, task fitness, legal basis, consent, risk, and maintainability. Governance is designed for the working pipeline: clear enough for teams to make decisions and specific enough for audit. ## How Admas works 1. **Specify the need:** Define task, population, languages, domains, exclusions, quality, rights, and acceptable collection methods. 2. **Evaluate sources:** Assess licensed, commissioned, public, partner, and synthetic options for coverage, bias, provenance, and risk. 3. **Operationalize stewardship:** Establish documentation, approval, access, versioning, retention, deletion, and change controls. ## Typical outputs - Dataset requirements and coverage model - Source assessment and acquisition plan - Provenance and rights documentation - Governance, access, and lifecycle controls ## Frequently asked questions ### What is data sourcing & governance? Language-data specifications, acquisition strategies, rights, provenance, documentation, and stewardship controls. In practice, the work is bounded by a defined product or model decision, named audiences and locales, representative inputs, and acceptance criteria that can be reviewed. ### When does a team need data sourcing & governance? Data can be technically accessible but unsuitable, unrepresentative, poorly licensed, or impossible to explain once it enters a training pipeline. The useful starting point is the smallest representative flow that can expose the cause, impact, and ownership of the problem. ### What does a data sourcing & governance engagement include? Specify the need: Define task, population, languages, domains, exclusions, quality, rights, and acceptable collection methods. Evaluate sources: Assess licensed, commissioned, public, partner, and synthetic options for coverage, bias, provenance, and risk. Operationalize stewardship: Establish documentation, approval, access, versioning, retention, deletion, and change controls. ### What should we provide before data sourcing & governance starts? The most useful inputs are the model or evaluation decision the data must support, target populations and modalities, sampling and rights constraints, label definitions and edge cases, security, privacy, retention, and and acceptance requirements. Admas can begin with a partial package, but missing context, rights, access, owners, or acceptance criteria will be made visible in the plan rather than treated as harmless assumptions. ### What does Admas deliver for data sourcing & governance? Typical outputs include dataset requirements and coverage model, source assessment and acquisition plan, provenance and rights documentation, governance, access, and and lifecycle controls. Deliverables are adapted to the team that must use them, with decisions, evidence, limitations, owners, and next actions made explicit. ### How is the quality of data sourcing & governance evaluated? Quality is measured against the real task and risk. Relevant evidence can include coverage and representativeness, label validity and consistency, agreement and adjudication patterns, rights and provenance completeness, privacy and safety controls, and downstream model or evaluation utility. Sampling, severity rules, reviewers, adjudication, and pass or fail thresholds should be agreed before the result is used as a release decision. ### Can AI replace the human work in data sourcing & governance? Models can propose labels, find duplicates, prioritize uncertain items, and assist quality sampling. Human contributors and domain specialists are required to define categories, supply grounded judgments, resolve ambiguity, protect participants, and detect systematic model-shaped bias. The right allocation depends on consequence, content stability, available references, language coverage, reversibility, and the cost of a plausible but wrong result. ### How much does data sourcing & governance cost? The estimate changes with collection or asset volume, language and domain scarcity, participant and specialist requirements, annotation complexity, adjudication and audit depth, rights, security, and and delivery constraints. Pricing should distinguish setup and discovery, repeatable units, specialist or engineering time, independent review, management, and external costs. A low unit price is not comparable if it excludes the QA cycle or shifts rework back to the buyer. ## Related multimodal data curation services - [Annotation operations](https://admas.net/capabilities/data-curation/annotation-operations/index.md): Guidelines, workforce design, calibration, tooling, quality control, and adjudication for multilingual labeling. - [Data quality & evaluation](https://admas.net/capabilities/data-curation/data-quality-evaluation/index.md): Evidence about whether a multilingual dataset is representative, consistent, safe, and fit for its intended model task. - [Multimodal data pipelines](https://admas.net/capabilities/data-curation/multimodal-data-pipelines/index.md): Traceable pipelines for multilingual text, speech, image, video, annotation, transformation, and model-ready delivery. - [Benchmark dataset development](https://admas.net/capabilities/data-curation/benchmark-dataset-development/index.md): Representative multilingual benchmarks that connect model measurements to real tasks, populations, and release decisions. ## Start a project - [Build a project brief](https://admas.net/start-a-project/index.md?focus=data-curation): Tell Admas what you are building, which modalities and languages matter, and where progress is blocked.