Data Readiness for AI: Metrics & Workflows

• Data Readiness for AI is a framework that defines data as suitable for AI pipelines when it is cleaned, validated, normalized, and enriched with comprehensive metadata.
• It integrates key dimensions such as accessibility, quality, extractability, FAIRness, and governance, emphasizing task-specific data preparation.
• Various quantifiable metrics and operational workflows, including DRLs and readiness indices, enable continuous evaluation and enhancement of AI data assets.

Data Readiness for AI (DRAI) denotes the degree to which a dataset satisfies the end-to-end requirements of an AI pipeline. In the scientific literature, it extends traditional data quality and stewardship by explicitly addressing the downstream computational, statistical, and governance requirements of AI workflows; an AI-ready dataset is described as one that has undergone domain-appropriate cleaning, validation, normalization, and feature engineering, and that carries sufficient metadata, provenance, licensing, and governance to support reproducible training and evaluation. At the same time, readiness is task-conditioned: a dataset is not simply “good” in the abstract, but appropriate for a concrete modeling or decision context (Hiniduma et al., 22 May 2025, Wilkinson et al., 2 Jul 2026, Lawrence, 2017).

1. Intellectual origins and problem framing

The immediate precursor of DRAI is the proposal of Data Readiness Levels (DRLs), which introduced a common language for the hidden labor of “getting data ready” for machine learning and statistical modelling. DRLs were organized into three bands: Band C for accessibility, Band B for faithfulness and representation, and Band A for data in context. The framework borrowed the spirit of NASA’s Technology Readiness Levels while shifting attention from model maturity to data-centric obstacles such as poor collection practices, missing values, inconvenient storage mechanisms, intellectual property, security, and privacy (Lawrence, 2017).

Within DRLs, Band C asks whether the data exist and can be legally and technically accessed; Band B asks whether the values mean what analysts think they mean; and Band A asks whether the dataset is appropriate for a specified task. The framework also made explicit that the same dataset may qualify as A1 for one question but only B1 for another, thereby establishing context dependence as a central property of readiness rather than a special case (Lawrence, 2017).

Subsequent work in natural language processing transferred this logic to application settings outside academia. One paper identified the state of data as an important reason for failure in applied NLP projects and argued that the gap between academic NLP and operational deployment is rooted in poor mutual understanding between academic researchers and non-academic peers. It proposed improving communication regarding the accessibility, validity, and utility of data based on Data Readiness Levels, and reported iterations of the method in innovation and research projects with stakeholders in both private and public sectors (Olsson et al., 2021). A related NLP-oriented document described how an organization may proceed to identify, make available, validate, and prepare data to facilitate automated analysis methods (Olsson et al., 2020).

A broader mapping study on data engineering for AI systems reinforces this framing by showing that AI data engineering spans ingestion, transformation, serving, and storage, while also concluding that the field still lacks a unifying “Data Readiness” maturity model (Heck, 2024). This suggests that DRAI emerged not as a single closed doctrine, but as a family of attempts to make data preparation visible, communicable, and governable.

2. Core dimensions and competing taxonomies

Across the literature, DRAI is decomposed into different but overlapping dimensions. Some frameworks remain close to data quality, whereas others explicitly include provenance, FAIRness, privacy, explainability, licensing, sustainability, and deployment compatibility. The result is a plural taxonomy in which readiness is assessed through different “cuts” of the same problem space rather than through one universally fixed checklist (Grüger et al., 12 Mar 2025, Hiniduma et al., 22 May 2025, Qin et al., 12 Mar 2025, Caufield et al., 12 Sep 2025, Oderinwale et al., 2024).

Framework	Core dimensions or criteria
DRLs	Accessibility; Faithfulness and Representation; Data in Context
Clinical DRAI	Data Availability; Data Quality; Data Extractability
AIDRIN 2.0	Data Quality; Understandability; Usability; Structure & Organization; Governance; Impact on AI; Fairness
SciHorizon	Quality; FAIRness; Explainability; Compliance
Bridge2AI	FAIRness; Provenance; Degree of Characterization; Explainability; Ethical Data Practices; Sustainability; Computability

In oncology, DRAI was formalized as a three-dimensional model comprising Data Availability, Data Quality, and Data Extractability. Availability was defined through currency and amount of data; quality through accuracy, trustworthiness, completeness, consistency, and structure; and extractability through the distinction between structured and unstructured sources, together with the need for NLP or manual curation (Grüger et al., 12 Mar 2025).

AIDRIN 2.0 broadened the dimensional scope by decomposing readiness into seven interlocking pillars and mapping each pillar to quantifiable metrics. Its definition of DRAI emphasizes sufficient completeness, consistency, lack of undesirable bias, adherence to privacy constraints, and proper structuring to support reliable model training and inference (Hiniduma et al., 22 May 2025). Earlier AIDRIN work similarly enumerated nine core dimensions, including completeness, outliers, duplicates, feature importance, feature correlations, class imbalance, fairness, privacy, and FAIR compliance (Hiniduma et al., 2024).

In scientific benchmarking, SciHorizon defined four principal dimensions—Quality, FAIRness, Explainability, and Compliance—subdivided into 15 sub-dimensions, including completeness, accuracy, consistency, timeliness, findability, accessibility, interoperability, reusability, diversity, unbias, domain applicability, task applicability, provenance, ethics and safety, and trustworthiness (Qin et al., 12 Mar 2025). In biomedical metadata work, Bridge2AI formulated seven AI-readiness criteria: FAIRness, provenance, degree of characterization, explainability, ethical data practices, sustainability, and computability (Caufield et al., 12 Sep 2025).

A distinct open-government formulation defined AI-ready data through legal openness plus engineering maturity. In that view, readiness depends on data quality and annotation, accessibility and discoverability, interoperability and standardized formats, scalability and automation, and governance, security, and licensing (Oderinwale et al., 2024). A related scientific-agent formulation characterized Scientific AI-Ready data through three principles: task-conditioned organization, downstream compatibility with model-specific input constraints and executable workflows, and cross-integration across heterogeneous modalities (Rao et al., 9 Feb 2026).

3. Levels, stages, and quantitative scoring

Two complementary formalisms dominate the literature: readiness levels and readiness metrics. The level-based tradition begins with DRLs, whose sub-levels run from C4, where data existence is only hearsay, to A1, where a concrete modelling task has been defined and the dataset is ready for training, validation, and deployment in that context. The transition criteria are procedural: existence verified, format and access channels specified, machine-readability and legal clearance obtained, initial load into the analysis environment, normalization and EDA completed, and task definition finalized (Lawrence, 2017).

Large-scale scientific AI systems have recast this structure in more explicitly operational terms. One framework defined five Data Readiness Levels: Level 1 Raw, Level 2 Cleaned, Level 3 Labeled, Level 4 Feature-engineered, and Level 5 Fully AI-Ready, coupled to five processing stages: Ingest, Preprocess, Transform, Structure, and Shard. The resulting maturity matrix is HPC-aware and tailored to transformer-based generative model training (Brewer et al., 30 Jul 2025). REDI operationalized a closely related five-stage pipeline—Ingest, Preprocess, Transform, Structure, Output—and defined overall readiness as

$R=\frac{1}{5}\sum_{k=1}^{5} r_k,$

where $r_k\in[0,1]$ is the readiness fraction for stage $k$ (Wilkinson et al., 2 Jul 2026).

Clinical DRAI introduced an attribute-level scoring perspective. For each critical attribute, availability, quality, and extractability were operationalized separately, with an aggregate readiness index

$$\text{DRS}=\sum_{i=1}^{n} w_A\,A_i + w_Q\,Q_i + w_E\,E_i,$$

and equal weights $w_A=w_Q=w_E=\tfrac{1}{3}$. In the same study, $A_i$, $Q_i$, and $E_i$ were defined on $[0,1]$, with availability tied to source location and structure, quality to expert judgment against the quality criteria, and extractability to whether the value was tabular, form-based, free-text requiring NLP, or irrecoverable (Grüger et al., 12 Mar 2025).

AIDRIN 2.0 adopted normalized composite metrics for several readiness axes. Its Data Quality component, for example, used Completeness, Duplicate Rate, and Outlier Proportion to form

$Q=\alpha_c\,Q_c + \alpha_d\,Q_d + \alpha_o\,Q_o,$

while its Fairness and Privacy components were similarly expressed as weighted composites. All metrics were normalized to $r_k\in[0,1]$0, where 1 indicates ideal readiness (Hiniduma et al., 22 May 2025). By contrast, the earlier AIDRIN paper explicitly stated that it did not yet implement a single overall “AIDRIN Score,” even though it outlined a future weighted-sum formulation over normalized dimension scores (Hiniduma et al., 2024).

SciHorizon pushed aggregation further by defining sub-dimension scores, dimension-level averages, and an overall DRAI score:

$r_k\in[0,1]$1

In the equal-weight case, the four dimensions—Quality, FAIRness, Explainability, and Compliance—receive identical weight (Qin et al., 12 Mar 2025).

The coexistence of qualitative levels, pipeline-stage scores, attribute-level indices, and dimension-level composites indicates that DRAI is methodologically heterogeneous. This suggests that the literature treats readiness measurement as a design choice shaped by domain, infrastructure, and stakeholder requirements rather than as a settled standard.

4. Operational workflows and assessment architectures

DRAI is operationalized through workflows that combine data discovery, quality assessment, remediation, documentation, and re-assessment. In a clinical setting, one six-step methodology began with preparation by an interdisciplinary team, continued through data source identification, sample extraction, data discovery, decision-relevance questionnaires, and expert workshops, and ended with a final evaluation of presence, quality, and extractability for 41 consensus attributes (Grüger et al., 12 Mar 2025).

A documentation-centered operationalization appears in the Data Readiness Report (DRR). There, DRAI is defined as the aggregation of data-quality dimensions, the sequence of assessment and remediation steps applied to raw data, and the roles or personas that intervene in that sequence. The DRR is the accompanying artifact that records quantitative calibration against readiness dimensions, complete lineage of transformations, recommendations and remediations, and governance or policy constraints, thereby turning readiness from an implicit process into a standardized, shareable object (Afzal et al., 2020).

Automation-oriented frameworks embed these steps into software systems. REDI uses a unified five-stage pipeline with a shared PipelineContext and per-stage Flowcept provenance instrumentation, while SetGo ingests metadata.json and checks six metadata-readiness dimensions: FAIR compliance, licensing (SPDX), provenance, governance rules, reproducibility (DOI, versioning), and catalog readiness. The same toolchain automates publication to CKAN, Hugging Face Hub, or OpenMetadata (Wilkinson et al., 2 Jul 2026).

AIDRIN 2.0 implements a modular architecture with an Ingestion Module, Metrics Engine, Visualization and Reporting Module, Remediation Advisor, and a PPFL Adapter. In federated mode, the readiness mini-pipeline runs locally at the edge, transmitting only aggregated metric scores to the server over APPFLx’s gRPC/TLS channel, so that readiness assessment can occur without centralizing raw data (Hiniduma et al., 22 May 2025).

SciDataCopilot treats data readiness as a first-class operational primitive within an agentic system. Its architecture includes a Data Access Agent, Intent Parsing Agent, Data Processing Agent, and Data Integration Agent; together they construct a structured knowledge base, generate or adapt executable plans, iteratively refine code through execution feedback, and integrate processed units into a Scientific AI-Ready dataset (Rao et al., 9 Feb 2026).

Visual analysis has also been proposed as a readiness workflow. One study mapped readiness questions to histograms, bar charts, scatter plots, line charts, dimensionality reductions, and feature-importance plots, and extended the earlier readiness model by explicitly incorporating distribution shifts during data collection as Band B question B7. It also decomposed application-context assessment into seven Band A aspects, from model-assumption checks to stakeholder trust and deployment readiness (Tiger et al., 2024).

5. Domain-specific instantiations and empirical evidence

The practical meaning of DRAI varies sharply by domain. In open government data, AI-ready assets were defined as open Government data assets that are machine-readable, available or potentially available in an open format, unencumbered by non-IP restrictions, based on an underlying open standard, richly labeled, accompanied by detailed metadata, accessible through well-documented APIs or public repositories, and versioned, provenance-tracked, and governed. Best practices included datasheets for datasets, temporal metadata tagging, publication on GitHub or Hugging Face, open formats such as JSON, XML, and Parquet, DOI assignment, and policy-specific benchmarks analogous to LegalBench (Oderinwale et al., 2024).

In clinical oncology, the DRAI framework was applied to a skin tumor center to assess readiness for an analogy-based clinical decision support system. Forty-one attributes were deemed treatment-relevant and 20 were elevated to “must-have” status. Of these, 13 could be retrieved directly from structured registry exports, 5 more existed in structured HIS tables, 15 lay in free-text notes, and 8 were not documented anywhere. The reported extraction difficulties included multilingual abbreviations such as BCC/BZK, Latin versus English terms, neologisms, spelling errors, and negation handling (Grüger et al., 12 Mar 2025).

At leadership-computing scale, REDI evaluated climate, proteomics, materials science, and nuclear fusion workflows. The use-case summary reported 4,766 climate samples and 5.4 TB of NetCDF transformed to NPZ; 250 k proteomics samples and 4.3 TB of FASTA/mmCIF transformed to NPZ; 482 M materials graphs and 10 TB of JSON/LSMS transformed to ADIOS2; and 7,089 fusion runs and 106 TB of ADIOS2 transformed to .pt. Validation against domain references reported Pearson correlation of 1.000 for proteomics, materials, and fusion, and at least 0.999 for climate; MAE was 0.000 except approximately 0.01 K for climate bilinear regridding. The same study reported that file I/O was the dominant pipeline cost and that, at 32 DataLoader workers, Zarr read rate was approximately $r_k\in[0,1]$2 NPZ (Wilkinson et al., 2 Jul 2026).

SciDataCopilot supplied a different kind of empirical evidence by benchmarking autonomous data preparation across life science, neuroscience, and earth science. Reported results included 214,104 enzyme–reaction records assembled in approximately 5 hours, neuroscience task speedups of approximately $r_k\in[0,1]$3–$r_k\in[0,1]$4 while matching or exceeding human performance in quality, an earth-science table-processing speedup of approximately $r_k\in[0,1]$5, and up to $r_k\in[0,1]$6 speedup in data preparation overall (Rao et al., 9 Feb 2026).

Federated readiness assessment has also produced measurable downstream effects. In the AIDRIN 2.0 case study on the Flamby Heart Disease dataset, one client exhibited single-class pathology and two zero-variance features; excluding that client raised global model accuracy from 70.6% to 74.7%, improved the Data Quality score from 0.82 to 0.88 after imputing sparse features, and reduced Fairness SPD by 12%. The study further reported that local readiness assessment saved approximately 20% compute time by avoiding unproductive training rounds (Hiniduma et al., 22 May 2025).

6. Governance, metadata, and unresolved issues

A major theme in DRAI research is that readiness cannot be reduced to cleanliness alone. Biomedical metadata work defines AI-readiness in terms of FAIRness, provenance, degree of characterization, explainability, sustainability, computability, and documentation about ethical data practices, and recommends metadata standards such as RO-Crate with JSON-LD, OMOP, FHIR R4 profiles, BIDS, DICOM, FASTQ, HDF5, LinkML, and the Evidence Graph Ontology (Caufield et al., 12 Sep 2025). In this formulation, metadata are not ancillary; they are part of readiness itself.

This broader view also appears in AIDRIN, where fairness, privacy, and FAIR compliance are first-class dimensions, and in scientific DRAI systems, where provenance, licensing, governance, and reproducibility are explicitly instrumented (Hiniduma et al., 2024, Wilkinson et al., 2 Jul 2026). The Data Readiness Report similarly treats governance and lineage as core report components rather than post hoc appendices (Afzal et al., 2020).

Several common misconceptions follow from ignoring this breadth. One is that DRAI is merely a synonym for data quality. The literature does not support that reduction: quality is only one component alongside accessibility, extractability, privacy, fairness, interoperability, provenance, compliance, sustainability, and task applicability (Grüger et al., 12 Mar 2025, Hiniduma et al., 22 May 2025, Qin et al., 12 Mar 2025, Caufield et al., 12 Sep 2025). Another misconception is that readiness is inherently task-agnostic. DRLs explicitly state that a dataset may be A1 for one question and only B1 for another, while the Scientific AI-Ready paradigm defines readiness through task-conditioned organization and downstream compatibility (Lawrence, 2017, Rao et al., 9 Feb 2026).

A further unresolved issue is whether DRAI should culminate in a single scalar score. Some frameworks do provide explicit aggregation formulas, but others do not. The open-government formulation states that the authors do not hand down a single “DRAI score” formula; the original AIDRIN paper likewise states that it does not yet implement a single overall score; yet SciHorizon, clinical DRAI, REDI, and AIDRIN 2.0 all provide dimension-level or stage-level scoring schemes (Oderinwale et al., 2024, Hiniduma et al., 2024, Qin et al., 12 Mar 2025, Grüger et al., 12 Mar 2025, Wilkinson et al., 2 Jul 2026, Hiniduma et al., 22 May 2025). This suggests that DRAI is better understood as a family of task- and domain-dependent assessment frameworks than as a universally fixed scalar metric.

The field also remains organizationally plural. Some strands emphasize stakeholder communication and project scoping, others emphasize metadata standardization and documentation, and still others emphasize HPC pipelines, federated inspection, or agentic data preparation. What unifies these strands is the claim that failures in AI systems frequently originate not in model design but in inadequate articulation of what it means for data to be accessible, faithful, contextualized, governed, and reproducible.