Notion works beautifully for 50 contacts. At 300 records—especially if you're mixing clients, prospects, and projects across multiple workspaces—your database becomes a liability. Formula fields stop calculating mid-table. Relations collapse into orphaned IDs. Attachment file paths break. Exports land in your accounting software with mismatched customer IDs and missing deal context. And by then, you've already lost three weeks chasing data. The move to a purpose-built CRM doesn't have to be destructive. But it requires specificity: you need to know which Notion data structures translate cleanly and which ones require manual rebuild or transformation. This guide maps the migration path, identifies the data points that commonly fail, and gives you a pre-move audit checklist so you can migrate without rebuilding from scratch. Why Notion's relational model fractures under 300 records Notion databases are built on a flat architecture wrapped in a relational interface. It works because Notion's real strength is flexibility—you can restructure on the fly. That flexibility becomes a weakness at scale. When you're managing 300+ records across multiple Notion tables (say: Contacts, Deals, Projects, Invoices), the cardinality of your relations—how many-to-many connections exist between tables—explodes. A single contact might connect to five deals, three projects, and seven invoices. Each of those relations is stored as a two-way reference. When you query across tables, those lookups slow down. When a deal closes and you archive it, the relation reference persists as an orphaned ID in your Invoices table. More critically: Notion's formula fields are read-only and compute client-side. That means if you're using formulas to calculate deal value, close probability, or invoice total, you have no audit trail. You can't sort by a formula field reliably. You can't report on it. And if the formula references a relation that breaks, the entire column goes silent. Notion CRMs typically fail between 200–400 records because the combination of many-to-many relations and formula dependencies creates data integrity gaps that become visible only when you export and reconcile against your actual business state. What translates cleanly: contacts, single-line fields, and linear dates Start with the good news. The following data structures migrate without loss or transformation: Contact records (name, email, phone, company): Flat, single-value fields map 1:1. No computation needed. Your 300 contacts become 300 clean CRM contacts. Date fields (created, last contacted, birthday): Notion dates export as ISO 8601. CRM platforms consume this format natively. No reformatting required. Single-select fields (status, segment, source): These become picklist values. Orin's contact segmentation handles them as native tags and custom fields. Text and number fields (notes, revenue, headcount): No transformation. What you have in Notion exports as plain text or numeric value. Attachment URLs (but not embedded files): If you've stored links to PDFs or images as URLs in a text field, those carry over. Embedded Notion files do not (more below). For a services agency or small founder-led team with a straightforward contact list, this alone gets you 70–80% of the way there on day one. Where data breaks: relations, formulas, and attachment collapse Many-to-many relations and orphaned IDs In Notion, a Deal table relates to a Contact table (many deals per contact). When you export, Notion gives you the contact ID or name, but the relational structure itself doesn't export as a relationship—it exports as a comma-separated ID or a text reference. Your CRM has to re-index those on import. The failure point: if a contact was deleted in Notion but deals still reference it, your CRM's import will flag those deals as belonging to a non-existent contact. They either orphan or require manual reassignment. A typical Notion workspace with 300+ records has 5–15% orphaned relations because team members deleted records without cascading the cleanup. Audit step: Before migration, query your Notion database for any deal or project relation pointing to a deleted contact. In Notion, this means filtering for empty relation fields or searching your export for IDs that don't exist in your contact list. Flag these records and decide: reassign them to the correct contact, or archive them if they're dead deals. Formula fields collapse into empty columns Notion formulas—whether they calculate deal value, close probability, or invoice totals—are read-only and computed on-the-fly in the UI. When you export, Notion doesn't export the formula result as a value; it exports the cell as empty or as the last computed value cached in memory. The moment you import into your CRM, that formula is gone. This is catastrophic if your sales forecast or revenue reporting relied on Notion formulas to calculate total deal value or pipeline coverage. You'll land in your accounting system with invoices mis