# AI Training Data Readiness Assessment: Is Your Data Ready to Ground a Model? > Source: https://netray.co/tools/ai-training-data-readiness-assessment > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Score your data across volume, machine readability, labeling, lineage, permissions, rights, and refresh so you know what to fix before building an AI system. ## Category ai-automation ## Tool **Kind:** assessment **Questions:** #### Do you have enough relevant domain content for the use case? **Help:** Retrieval-based systems need coverage of the questions users will ask, which matters more than raw document count. **Options:** ##### Comprehensive coverage of the target domain, verified against real user questions **Score:** 3 ##### Good coverage with a few known gaps **Score:** 2 ##### Partial coverage; many common questions are undocumented **Score:** 1 ##### The knowledge lives in people's heads, not in documents **Score:** 0 #### How machine-readable is your content today? **Options:** ##### Structured or clean text with reliable extraction, including tables and drawings metadata **Score:** 3 ##### Mostly digital documents with usable text extraction **Score:** 2 ##### Heavy mix of scanned PDFs and images requiring OCR **Score:** 1 ##### Paper, drawings, and legacy formats with no extraction pipeline **Score:** 0 #### Do you have labeled examples or ground truth for evaluation? **Help:** You cannot tune what you cannot measure. Even fifty good question-and-answer pairs change a project's trajectory. **Options:** ##### A curated evaluation set of real questions with verified correct answers **Score:** 3 ##### Some labeled examples assembled during earlier work **Score:** 2 ##### We could produce examples but have not yet **Score:** 1 ##### No labeled data and no plan to create any **Score:** 0 #### How well do you understand data lineage and freshness? **Options:** ##### Every source has a known owner, update cadence, and system of record **Score:** 3 ##### Owners are known for most sources; freshness varies **Score:** 2 ##### We know roughly where things come from but not how current they are **Score:** 1 ##### Content is copied between shares with unknown origin and age **Score:** 0 #### Are source-system permissions preserved when content is indexed? **Help:** Flattening access control during indexing is the most common way an AI rollout leaks documents internally. **Options:** ##### Permissions carried per chunk and enforced at query time for every user **Score:** 3 ##### Index partitioned by broad security group **Score:** 2 ##### One index for everyone with restricted content excluded manually **Score:** 1 ##### Permissions are not considered in the indexing design **Score:** 0 #### How do you handle duplicates, superseded revisions, and contradictions? **Options:** ##### Deduplication and revision control ensure only current approved versions are indexed **Score:** 3 ##### Obsolete content is removed periodically by a manual review **Score:** 2 ##### We know duplicates and old revisions exist but do nothing about them **Score:** 1 ##### Multiple conflicting versions of key documents circulate freely **Score:** 0 #### What is the quality of the structured master data the use case depends on? **Help:** Item, BOM, routing, customer, and supplier masters feed most manufacturing AI use cases directly. **Options:** ##### Validated by business owners with governance and measured accuracy above 98% **Score:** 3 ##### Generally reliable with known problem areas **Score:** 2 ##### Inconsistent, with duplicates and free-text fields carrying critical meaning **Score:** 1 ##### Widely distrusted; people keep their own spreadsheets instead **Score:** 0 #### Do you have clear legal rights to use this data with AI? **Help:** Check customer contracts, supplier NDAs, licensed content terms, and export control classification. **Options:** ##### Rights reviewed and documented per source, including export control classification **Score:** 3 ##### Reviewed for the main sources; some third-party content is unclear **Score:** 2 ##### Assumed acceptable but never formally reviewed **Score:** 1 ##### Unknown, and some content is certainly customer or export controlled **Score:** 0 #### Is sensitive data identified and handled before it reaches the model? **Options:** ##### Classification applied per source with masking or exclusion enforced in the pipeline **Score:** 3 ##### Sensitive sources excluded wholesale from indexing **Score:** 2 ##### We rely on the assumption that nothing sensitive is in these repositories **Score:** 1 ##### No classification or handling has been considered **Score:** 0 #### Is there a pipeline to keep the data current after launch? **Options:** ##### Automated incremental refresh with monitoring, alerting, and removal of deleted content **Score:** 3 ##### Scheduled full reindex on a defined cadence **Score:** 2 ##### Someone reloads the content manually when they remember **Score:** 1 ##### The index would be a one-time snapshot that goes stale immediately **Score:** 0 **Bands:** #### Not ready: data work is the project **Min:** 0 **Max:** 39 **Verdict:** At this level of readiness, an AI build would spend most of its budget on data remediation while producing output nobody trusts. That is not a reason to stop, but it is a reason to sequence differently: treat data preparation as the first funded phase with its own deliverables rather than as an assumption inside an AI project plan. **Recommendations:** - Pick one narrow use case and scope data work to only the sources it needs, rather than boiling the ocean. - Build an extraction pipeline for the highest-value document set, including OCR for scanned drawings and specs. - Create a fifty-question evaluation set with verified answers before any model work begins. - Assign an owner and an update cadence to each source you intend to index. - Review legal rights and export control classification on every source now, not after the build. #### Partially ready: fixable gaps, real risk **Min:** 40 **Max:** 64 **Verdict:** You have usable data with specific weaknesses that will surface as wrong or stale answers once real users arrive. The two gaps that most often bite in this band are permission handling and revision control, because both fail silently and are discovered by the wrong person at the wrong time. **Recommendations:** - Design retrieval so source-system permissions are enforced at query time, not approximated by index partitioning. - Implement deduplication and revision control so superseded documents cannot be retrieved as current. - Close the largest content coverage gaps identified against real user questions. - Automate the refresh pipeline including deletion propagation, not just additions. - Baseline structured master data accuracy for the objects the use case touches. #### Ready with conditions: build and instrument **Min:** 65 **Max:** 84 **Verdict:** Your data supports a production build. The remaining risks are usually freshness monitoring and evaluation depth rather than raw content quality. Proceed, but instrument heavily so data problems surface as alerts instead of as user complaints. **Recommendations:** - Expand the evaluation set to cover edge cases and the questions users actually ask most often. - Add freshness and coverage monitoring so stale or missing sources raise alerts automatically. - Document data classification and rights per source so the compliance review is quick rather than exploratory. - Run a retrieval quality review with subject matter experts before opening access broadly. - Define a content ownership model so gaps found in production have a named person to fix them. #### Well prepared: make it reusable **Min:** 85 **Max:** 100 **Verdict:** Your data foundation is stronger than most enterprises achieve, which means the marginal cost of your next AI use case is low. Focus on turning this preparation into shared infrastructure so other teams inherit it rather than rebuilding their own pipelines. **Recommendations:** - Publish the ingestion, permission, and refresh pipeline as a shared platform service. - Maintain the evaluation set as a living asset with versioning and regression tracking. - Extend the same treatment to the next domain before its owners build a parallel pipeline. - Automate rights and classification checks at ingestion so new sources cannot bypass review. - Report data coverage and freshness as standing metrics alongside AI usage and value. ## Content **Intro:** This free AI training data readiness assessment tells you whether your data can actually support an AI build, and it is written for data leaders, ERP managers, and AI project sponsors in manufacturing and defense. Ten questions cover content coverage, machine readability, labeled evaluation data, lineage and freshness, permission preservation, duplicate and revision control, structured master data quality, legal rights, sensitive data handling, and the refresh pipeline. You get a score, a readiness band, and five prioritized actions. Most enterprise AI projects that disappoint do so because of data preparation gaps that were visible before a single line of code was written. **Sections:** #### How readiness is scored **Body:** Ten questions each carry four options worth zero to three points, giving a maximum of thirty which converts to a percentage. Four bands follow: not ready below 40%, partially ready from 40 to 64%, ready with conditions from 65 to 84%, and well prepared at 85% and above. The questions cover both unstructured content and the structured master data most manufacturing use cases depend on, because a system that answers questions about parts is only as good as your item master. Equal weighting is deliberate. A single zero on permissions or rights can stop a project entirely regardless of how strong the rest of the picture looks. #### What good data readiness looks like in practice **Body:** Readiness is not about volume. Teams routinely assume they need enormous corpora when what they actually need is coverage of the questions users will ask, cleanly extractable, correctly permissioned, and current. These reference points describe what separates projects that ship from projects that stall. **Bullets:** - A curated evaluation set of fifty to two hundred real questions with verified answers changes project outcomes more than any model choice. - Scanned drawings and legacy PDFs commonly require an OCR and extraction phase measured in weeks, not days. - Permission-aware retrieval must be designed in from the start; retrofitting it usually means rebuilding the index. - Superseded revisions retrieved as current are the most damaging single failure mode in engineering and quality use cases. #### Reading your score by category **Body:** Group your low scores before acting. Weakness clustered in questions one, two, and three is a content problem: the knowledge exists but is not extractable or measurable, and the fix is a data engineering phase you should fund explicitly. Weakness in questions five, eight, and nine is a governance problem that will be caught by security or legal review and can stop a launch on the day it was scheduled. Weakness in questions four, six, and ten is an operations problem that will not stop launch but guarantees the system decays within a quarter. Sequence governance first because it blocks, then content because it is slow, then operations before go-live. #### How Netray gets data ready for AI **Body:** Netray does the unglamorous data work that determines whether enterprise AI succeeds: extraction pipelines for scanned drawings and specifications, permission-aware indexing that respects entitlements in Infor SyteLine, Infor LN, M3, ServiceMax, and Salesforce, revision control so obsolete documents cannot surface as current, and automated refresh with deletion propagation. We build evaluation sets with your subject matter experts so quality is measured rather than debated. For aerospace and defense clients, we classify export-controlled content during ingestion and keep it inside an approved on-prem or air-gapped boundary, so rights and residency questions have structural answers rather than policy promises. **Cta:** Get a source-by-source data readiness review from Netray with a costed remediation plan before you commit to an AI build. **Faq:** #### How much data do we actually need for an enterprise AI use case? **Answer:** Far less than most teams assume, if you are grounding a model on your content rather than training one from scratch. What matters is coverage of the questions users will ask, not raw volume. A few hundred well-maintained, current documents that answer real questions outperform tens of thousands of stale and duplicated files. The failure mode is almost never too little data. It is unextractable, contradictory, or obsolete data indexed without curation. #### Do we need labeled training data to use large language models? **Answer:** You rarely need labeled data for training, but you always need it for evaluation. A curated set of fifty to two hundred real questions with verified correct answers lets you measure whether a change helped or hurt, which is the difference between engineering and guessing. Build it before you build the system, using questions collected from actual users, and treat it as a versioned asset that grows as new failure modes appear. #### What happens if we index documents without preserving permissions? **Answer:** Users retrieve content they have no right to see, and they usually discover it by accident in front of colleagues. Salary letters, customer pricing, and export-controlled drawings are the classic examples. Retrofitting permissions typically means rebuilding the index and the retrieval layer, so design for it from the start: carry access control metadata alongside each chunk and filter at query time against the requesting user's actual entitlements. ## Search terms AI training data readiness, data readiness assessment for AI, RAG data preparation, enterprise AI data quality, is my data ready for AI, AI data governance readiness, document readiness for LLM --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json