# ERP AI Data Source Inventory Worksheet > Source: https://netray.co/tools/erp-ai-data-source-inventory-worksheet > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer An editable inventory of ERP tables, file shares and document stores with classification and an AI-allowed decision per source. ## Category ai-automation ## Audience - CIO - IT manager - Data steward - Information security ## Tool **Kind:** worksheet **Mode:** editable **Columns:** #### Data source **Key:** source **Type:** text #### System type **Key:** system **Type:** select **Options:** - ERP database - MES - PLM - File share - SharePoint - Email - QMS documents - Other #### Data owner (role) **Key:** owner **Type:** text #### Classification **Key:** class **Type:** select **Options:** - Public - Internal - Confidential - Export controlled - Personal data #### Approx. records or documents **Key:** records **Type:** number **Format:** number #### Refresh **Key:** refresh **Type:** select **Options:** - Live - Hourly - Nightly - Weekly - Manual #### AI use decision **Key:** ai_use **Type:** select **Options:** - Allowed - Allowed with filters - Review needed - Not allowed #### Access method **Key:** access **Type:** text #### Quality score (1-5) **Key:** quality **Type:** number #### Notes **Key:** notes **Type:** text **Rows:** **Source:** Purchase orders and receipts **System:** ERP database **Owner:** Procurement Manager **Class:** Confidential **Records:** 480000 **Refresh:** Nightly **Ai use:** Allowed with filters **Access:** Read-only reporting view **Quality:** 4 **Notes:** Hide supplier pricing for non-buyers **Source:** Item master and BOMs **System:** ERP database **Owner:** Engineering Manager **Class:** Confidential **Records:** 65000 **Refresh:** Nightly **Ai use:** Allowed **Access:** Read-only reporting view **Quality:** 3 **Notes:** Duplicate descriptions need cleanup **Source:** Customer drawings **System:** PLM **Owner:** Engineering Manager **Class:** Export controlled **Records:** 22000 **Refresh:** Weekly **Ai use:** Review needed **Access:** PLM export **Quality:** 4 **Notes:** Confirm hosting location and user eligibility first **Source:** Quality procedures and work instructions **System:** QMS documents **Owner:** Quality Manager **Class:** Internal **Records:** 1800 **Refresh:** Weekly **Ai use:** Allowed **Access:** Document library **Quality:** 4 **Notes:** Only released versions **Source:** HR employee files **System:** File share **Owner:** HR Manager **Class:** Personal data **Records:** 9000 **Refresh:** Manual **Ai use:** Not allowed **Access:** None **Quality:** 2 **Notes:** Out of scope for current AI work **Totals:** #### Total records and documents **Key:** records **Agg:** sum **Format:** number #### Average quality score **Key:** quality **Agg:** avg **Format:** decimal #### Sources listed **Key:** source **Agg:** count **Format:** integer **Summary:** #### Average size per source **Key:** avg_records **Expr:** sum_records / count **Format:** number **Notes:** Start with the sources you expect the first use case to need, then widen. Never connect a source until the owner, classification and AI use decision are filled in. Quality is a quick 1-5 judgement of how clean and current the data is. ## Content **Intro:** Before an AI assistant can answer questions over ERP data and documents, you need a plain list of what data exists, who owns it, how sensitive it is and whether AI may use it. This worksheet is that list, in a form you can edit and export to Excel for the data governance board. **Sections:** #### How to use the worksheet **Body:** Add one row per source: an ERP module or reporting view, a document library, a file share, an email archive. Name an owner by role, pick a classification, estimate size, and choose an AI use decision. Use Allowed with filters where only part of a source should be visible, such as hiding supplier pricing from non-buyers. Review needed means someone with authority must decide before any connection is built. Export and circulate to data owners for sign-off. #### Classification and AI decisions **Body:** Align classifications with your existing scheme if you have one. For manufacturers, the two classes that most often block progress are export controlled technical data and personal data. Export controlled data may be restricted by who can access it and where it can be processed, so confirm requirements with your compliance officer before any AI system indexes it. Personal data brings privacy duties such as purpose limits and retention. **Bullets:** - Allowed: low sensitivity, clean enough, owner agrees - Allowed with filters: needs row, column or role limits at query time - Review needed: classification or hosting question unresolved - Not allowed: out of scope or prohibited by policy or contract #### Why quality and refresh matter **Body:** An assistant is only as good as the data behind it. A source scored 2 for quality, such as scattered documents with unclear versions, will generate confident wrong answers. Refresh frequency sets expectations: a nightly extract cannot answer what is on the dock right now. Record both so use case owners can choose sources that fit their questions, and so the project plan includes cleanup work where needed. #### Common mistakes **Body:** The usual errors are listing only ERP tables and forgetting the documents people actually search, skipping the owner column, and treating the inventory as a one-time exercise. Review it each time a new use case is proposed, and keep access methods specific, such as a named read-only reporting view rather than direct database access. **Cta:** If you want help turning this inventory into a retrieval and permissions design for on-prem AI, Netray can walk through it with your IT and data owners. **Faq:** #### Do we need to inventory everything first? **Answer:** No. Start with the sources needed by the first one or two use cases, plus any sensitive sources you want to explicitly exclude. Expand as new use cases are approved. A small accurate list beats a large unfinished one. #### Who should own each row? **Answer:** The business role accountable for the data, such as the procurement manager for purchase orders or the quality manager for procedures. IT is the custodian, not usually the owner, and should not make sensitivity decisions alone. #### How do we handle export controlled data? **Answer:** Mark it and send it to review. Requirements depend on regulations such as ITAR or EAR and your contracts, including who may access it and where it may be processed. Your compliance officer should decide before any indexing happens. #### Can this feed our data catalog? **Answer:** Yes. Export to Excel or CSV and import the key fields into your catalog or governance tool. The AI use decision column is often the new field catalogs lack. ## Search terms ai data inventory template, data source inventory excel, rag data source register, erp data classification worksheet, ai data mapping template, enterprise search data sources list, data catalog template for ai --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json