Takeaways
- Data availability is catching up with document complexity.
- Intelligent Document Processing uses machine learning and natural language processing to catch what ETL and ELT methods can’t.
- IDP extracts data from documents at ingestion points and outputs structured data in popular formats that can include JSON, CSV, and XML.
- The IDP market is blasting off as it improves data quality and data possible in lakehouses.
Historically, traditional methods of capturing document data have fallen short of large-scale analytics and have been primarily all about indexing content and providing basic metadata. But traditional capture has always struggled with variations in document structure and schema.
Intelligent Document Processing (IDP) is the second hottest toy on every analyst and CEO’s Christmas list (under AI of course). As of 2025, 63% of Fortune 250 companies have implemented IDP because of the way this improved capture solution captures and adds meaning and structure to data locked in content and documents, granting greater access to data for analysis and AI. It’s not hard to find data points throughout the tech industry suggesting an 80% to 90% increase in access to data due to the sheer volume confined across documents and content within an organization.
Let’s take a look at the why, how, and where IDP fits in a data pipeline for document analysis.
Comparing modern data querying pipelines.
Schema-on-Write
(Extraction, Transformation, Loading)
- Takes raw structured data – typically relational databases, logs, or APIs.
- Normalizes and structures the data with a predefined schema.
- Loads it into a Data Warehouse.
Benefits: High performance and consistency for analytics with fast reliable business reporting that emphasizes data quality and easy querying.
Schema-on-Read
(Extraction, Loading, Transformation)
- Extracts raw data.
- Loads it into a Data Lake.
- Adds structure during queries, scheduled jobs, or batch processes
Benefits: Can hold anything from structured tables, semi-structured logs, or unstructured free-form content in raw formats. Schema and parsing rules are created at the point of querying.
The Data Lakehouse
The architecture in cloud databases like your Databricks and Snowflakes of the world, Lakehouses merge the performance and management features of Warehouses with the flexibility of a Lake. Today, 85% of organizations, a 20% increase from last year, are leveraging some form of Data Lakehouse architecture to store enterprise data and support AI and machine learning projects. And AI needs data.
Where does the Lakehouse struggle
There is lots of data stored in a data lakehouse but we’re either lacking unstructured document data (about 80% if you remember from above) or we’ve got unformatted document data tossed in a lake like sunken treasure. And that’s when a Lake can turn into a Data Swamp, a term for a poorly managed data repository ridden with poorly organized or unusable raw data.
Many Lakehouses do contain some native toolsets to master many document types, and custom Python scripts can be made around your most common document schemas to turn raw data into usable formats like JSON, CSV, or XML. But when there is no easily discernible organization in a document, or the format varies widely, error rates and a steep rise in manual scripting ensue. And we don’t like that.
Parsing unstructured data to JSON
IDP utilizes natural language processing to make sense of documents of varying structure while also gleaning context and meaning to add valuable insights to unstructured documents. After capturing the important data, IDP gives it a new structure (JSON, XML, CSV, etc.). No manual scripts or debugging — just models wielding artificial intelligence and machine learning to adapt to changes in format and provide a digestible structure to data as documents flow from internal or external sources into your organization.
Why is that important? Because every document or piece of content, whether a highly structured invoice or a bewildering and long-winded letter from the CEO on quarterly performance, is littered with important data that can be analyzed by data engineers to fuel business decisions, or by LLMs to fuel those coveted AI-experiences.
