Data Architecture & Integration:
How Do You Unify Structured And Unstructured Data?
Unify structured (tables) and unstructured (docs, emails, chats, audio) by combining a canonical model, document intelligence (OCR/NLP), and embeddings + vector search in a lakehouse—then activate governed insights across MAP, CRM, CDP, and BI.
Use a lakehouse pattern to land all files and events, extract fields from documents with OCR/NLP, and generate embeddings for semantic search. Normalize extracted fields into the canonical warehouse model (People, Accounts, Opportunities, Activities, Assets), store raw text and vectors for recall, and expose both via BI and activation (reverse ETL to MAP/ads/CDP). Govern with lineage, quality tests, consent, and retention.
Principles For Unifying All Data
The Unified Data Playbook
A practical sequence to combine files, text, and tables into governed insights and activation.
Step-By-Step
- Land Everything — Ingest CRM/MAP/CDP, web, ads, chat, email, support tickets, PDFs, audio; store raw in the lakehouse.
- Extract Structure — Run OCR for images/PDFs; apply NLP to detect topics, entities, sentiment, and PII.
- Generate Embeddings — Produce vectors for text and media; index in a vector database with metadata filters (account, region, consent).
- Normalize To The Model — Map extracted fields to People–Account–Opportunity–Activity–Asset; enforce keys and data types.
- Publish Golden Tables — Curate journey, content, and conversation marts; expose through BI and metrics layers.
- Activate & Retrieve — Use reverse ETL for audiences; power RAG (retrieval-augmented generation) for assistive experiences.
- Govern & Monitor — Track SLAs (freshness, completeness, accuracy), embedding coverage, and privacy compliance.
Approaches To Unifying Structured & Unstructured Data
| Approach | Best For | Inputs | Pros | Watchouts | Output |
|---|---|---|---|---|---|
| Lakehouse + ELT | Scalable storage with curated models | Files, events, tables | Open formats; cost-efficient; flexible | Requires modeling discipline | Raw zones, modeled marts |
| Document AI (OCR/NLP) | Forms, contracts, tickets, emails | PDFs, images, text bodies | Extracts fields + meaning; PII tagging | Model drift; need QA & provenance | Parsed fields, labeled text |
| Embeddings + Vector DB | Semantic search & RAG | Text, audio, images | Finds similar content beyond keywords | Versioning vectors; privacy in indexes | Vector index with metadata |
| Knowledge Graph | Complex relationships & lineage | Entities, relationships | Great for impact analysis, policy | Upfront modeling effort | Graph of entities & links |
| Reverse ETL Activation | Operationalizing insights | Golden tables, segments, scores | Drives MAP/ads/CDP actions | Scope control; dedupe policies | Audiences, personalizations |
Client Snapshot: Documents To Decisions
A global team processed support emails and PDFs with Document AI, mapped entities to Accounts and Opportunities, and indexed content with embeddings. Result: 21% faster case resolution, +17 points in self-serve search success, and unified reporting across BI and Sales.
Tie your unified data to RM6™ operating rhythms and The Loop™ so insights reliably power experiences and revenue.
FAQ: Unifying Structured And Unstructured Data
Straight answers for architects, RevOps, and Marketing Operations leaders.
Turn Files & Text Into Action
We’ll extract structure, build embeddings, and connect insights to every channel—safely and reliably.
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