What does a CPG company need to do to prepare its data for generative AI use cases?

Summary

• Unify structured and unstructured data on one governed lakehouse so transaction data sits alongside reviews, social signals, images, and documents for AI to use.
• Ingest and continuously refresh data with Lakeflow — managed connectors plus declarative pipelines with built-in data quality.
• Govern everything with Unity Catalog: lineage, automatic PII detection and tagging, column masking and attribute-based access, and audit.
• Prepare unstructured content for generative AI by parsing and extracting with built-in AI Functions, then indexing it in AI Search for retrieval-augmented generation (RAG).
• Build, evaluate, serve, and monitor with Mosaic AI and MLflow, and deliver results through Databricks Apps and AI/BI Genie.

What does a CPG company need to do to prepare its data for generative AI use cases?

Consumer packaged goods (CPG) companies generate large volumes of both structured data (point-of-sale, ERP, syndicated feeds) and unstructured data (product reviews, social posts, service transcripts, packaging and product images, R&D documents). Generative AI use cases such as review mining, product onboarding, and consumer insights depend on getting that data into one governed place, ensuring its quality and governance, and making unstructured content retrievable. The Databricks Data Intelligence Platform provides these building blocks so teams can bring AI to their data rather than copying data into separate systems. See Simplifying product onboarding with generative AI.

Steps to prepare CPG data for generative AI

• 1. Unify data on the lakehouse. Bring structured and unstructured data into a single lakehouse using open formats (Delta Lake and Apache Iceberg), so one governed copy of data serves analytics and AI without creating a new silo per use case.
• 2. Ingest and refresh with Lakeflow. Use Lakeflow Connect's managed connectors to ingest from POS, ERP, retailer portals, social platforms, and syndicated providers, and Lakeflow Declarative Pipelines for reliable batch-and-streaming ETL with automatic data-quality monitoring.
• 3. Govern with Unity Catalog. Manage all data and AI assets in Unity Catalog: automatic column-level lineage, AI-powered PII detection and tagging, column masking and attribute-based access control (ABAC), and built-in audit logs — important for handling consumer data and data-subject requests.
• 4. Prepare unstructured content. Use built-in AI Functions in SQL (for example, ai_parse_document, ai_extract, ai_classify, and ai_summarize) to structure documents, reviews, and images, then index them in AI Search — a managed vector database — to power retrieval-augmented generation (RAG) grounded in your own content.
• 5. Build, serve, evaluate, and monitor. Build and orchestrate generative AI apps and agents with Mosaic AI, track and manage the lifecycle with MLflow, evaluate quality and safety before scaling, and monitor data quality and model behavior in production.
• 6. Deliver to the business. Surface results through Databricks Apps and natural-language analytics in AI/BI Genie so business users can act on them.

Getting started

• Land structured and unstructured data in the lakehouse and standardize on open formats.
• Register data and AI assets in Unity Catalog and apply PII tagging, masking, and access policies early.
• Ingest and continuously refresh sources with Lakeflow, adding data-quality checks in your pipelines.
• Parse and extract unstructured content with AI Functions and index it in AI Search to ground generative AI in company data.
• Start with a scoped use case, evaluate it with Mosaic AI and MLflow, then expand.

FAQs

What data foundation does generative AI need in CPG?

A single, governed lakehouse that unifies structured and unstructured data, so transaction data sits alongside reviews, social signals, images, and documents that generative AI can retrieve and reason over.

How do you make unstructured CPG data usable for AI?

Parse and extract it with built-in AI Functions (for example, ai_parse_document, ai_extract, ai_classify, ai_summarize), then index it in AI Search to power retrieval-augmented generation grounded in your own content.

How is consumer data governed?

Unity Catalog provides column-level lineage, automatic PII detection and tagging, column masking, attribute-based access control, and audit logs across data and AI assets.