How can I analyze structured and unstructured data together for business intelligence?

Summary

• Business intelligence runs on structured, governed data, so the key is turning unstructured content (documents, text, images) into queryable tables that sit next to your existing data under one governance model.
• On Databricks, ai_parse_document extracts text, tables, and layout from PDFs, DOCX, and images, and ai_extract pulls the fields you define into columns. Both are generally available and run in SQL.
• Enrich free text into BI-ready signals with AI functions like ai_classify (categorize) and ai_analyze_sentiment (score tone), so qualitative content becomes dimensions and measures you can chart.
• Analyze the combined data in natural language with AI/BI Genie and build reports in AI/BI Dashboards, all over Delta tables governed by Unity Catalog.
• Databricks AI Search adds semantic and hybrid retrieval over document content for search and retrieval-augmented answers.

How can I analyze structured and unstructured data together for business intelligence?

Business intelligence runs on structured, governed data, so the practical answer is to make your unstructured content queryable and put it alongside your tables under one governance model. On Databricks, you parse documents into text and fields with generally available AI functions, enrich that text into measurable signals, land it as Delta tables governed by Unity Catalog, then analyze structured and document-derived data together with AI/BI Genie and dashboards. Because everything lives on the lakehouse, there is no separate system to sync or copy.

Why structured and unstructured data usually stay apart

Most decisions need both kinds of data. A churn analysis wants subscription and usage figures from tables and the tone of support tickets and reviews from text. Structured data is rows and columns; unstructured data is documents, email, images, and free text. Historically the two lived in separate stacks with separate tools and governance, and BI tools could read only the structured side. The gap closes when the unstructured content is parsed, enriched, and stored as governed tables, so a BI tool reads it like any other column.

Capabilities to evaluate

Capability	Why it matters for BI
Document parsing and extraction	Turn PDFs, DOCX, and images into text and structured fields your queries and dashboards can use.
Text enrichment	Classify and score free text so qualitative content becomes dimensions and measures you can chart.
Unified governance	One permission, lineage, and audit model so document-derived and structured data follow the same rules.
Natural-language analysis	Let business users ask questions across both kinds of data without writing SQL.
Semantic retrieval	Find relevant passages by meaning for search and retrieval-augmented answers.
One storage layer	Keep both kinds of data as governed tables so there is no copy step between systems.

How Databricks approaches it

Start by making documents queryable. ai_parse_document extracts text, tables, and layout from PDFs, DOCX, and images, and ai_extract pulls the specific fields you define into columns. Both are generally available and callable in SQL, so they fit an existing pipeline. Next, enrich the text: AI functions such as ai_classify categorize content against your labels and ai_analyze_sentiment scores tone, turning qualitative text into dimensions and measures. The results land as Delta tables governed by Unity Catalog, next to your structured data, with shared permissions and lineage.
From there, analysis is the same for both kinds of data. AI/BI Genie answers natural-language questions as governed SQL over your tables, including the ones derived from documents, and AI/BI Dashboards build interactive reports and visualizations from the same data. For document-heavy retrieval, Databricks AI Search indexes content for semantic and hybrid search, which powers retrieval-augmented answers and search apps. The pattern stays consistent: parse and enrich unstructured content into governed tables, then use one BI surface across everything. For the document side in depth, see what is document AI.

Use cases

• Voice of the customer: join subscription and usage tables with sentiment and topics extracted from reviews and tickets to see what drives satisfaction.
• Contract and finance analytics: extract dates, parties, and amounts from agreements, then chart renewals and exposure next to revenue tables.
• Operations reporting: parse inspection reports or forms into fields and trend them alongside sensor and transaction data.

FAQs

How does unstructured data become usable for BI?

ai_parse_document turns PDFs, DOCX, and images into text and tables in SQL, and ai_extract pulls the fields you define into columns. The output is a governed table you can query, chart, and join like any other.

Can business users analyze both without writing SQL?

Yes. AI/BI Genie answers natural-language questions as governed SQL over your tables, including tables derived from documents, and AI/BI Dashboards present the results as interactive reports.

Do documents and tables share the same governance?

Yes. Unity Catalog governs files in volumes and Delta tables under one permission, lineage, and audit model, so both follow the same access rules.

How do I turn free text into something I can measure?

Use AI functions such as ai_classify to categorize text and ai_analyze_sentiment to score tone. These produce structured columns you can group, filter, and chart in a dashboard.