
Organizations are constantly challenged to extract value from the overwhelming amount of unstructured data they generate. This data, which ranges from scanned documents and emails to images and system logs, holds immense potential, but organizations often struggle to tap into it because of its complexity.
Traditional tools struggle to process and transform such data into actionable insights, putting businesses at a disadvantage. However, technological advancements, particularly multi-modal Large Language Models (LLMs), are revolutionizing how we approach unstructured data, enabling organizations to streamline processes, improve decision-making, and unlock new growth opportunities.
This article is the first in a series exploring how AI-powered workflows reshape unstructured data processing. Here, we’ll introduce the fundamentals. What unstructured data is, why it’s challenging, and how AI helps turn it into usable insights. The following articles’ll examine specific real-world applications, including receipts, claims, and medical records data flows.

What do we mean by “unstructured data”?
Unstructured data refers to information that doesn’t fit neatly into rows and columns, like the data stored in traditional relational databases. It includes diverse formats such as images, audio files, videos, word-processing documents, emails, and system logs. This type of data makes up approximately 80% of all enterprise data and requires more storage while being inherently more challenging to manage and protect than structured data.
Despite its complexity, unstructured data holds immense value. Better access to this data enables organizations to make more informed decisions, leveraging insights from a wide range of sources.
Unstructured data is growing 3x faster than structured data. By 2025 the amount of unstructured data is projected to be 175 zettabytes. – Gartner
What’s new with processing unstructured data?
The multi-modal capabilities of Large Language Models (LLMs) enable new ways to extract and transform data because of their ability to adapt to different types of documents and use reasoning to interpret their content. Here are some reasons you would choose this method for your extraction and transformation workflows over traditional methods.
Extraction
- Adaptable: Handles complex document layouts better, reducing errors.
- Multilingual Support: Seamlessly processes documents in multiple languages.
- Contextual Understanding: Extracts meaningful relationships and context, not just text.
- Multimodality: Processes various document elements, including images and tables.
Transformation
- Schema Adaptability: Easily transforms data to fit specific schemas for database ingestion.
- Dynamic Data Mapping: Adapts to different data structures and formats, providing flexible rules.
- Enhanced Insight Generation: Applies reasoning to create more insightful transformations, enriching the dataset with derived metrics, metadata, and relationships.
Historically, Optical Character Recognition (OCR) tools focused on text recognition but struggled to extract meaningful information beyond individual words. These tools often overlook critical information embedded in the structure and relationships between data points. While OCR helped digitize text, it lacked the deeper comprehension needed to interpret context and structure accurately.
LLMs have transformed document processing by interpreting entire documents, capturing content and context. Unlike traditional OCR, which recognizes characters but misses meaning, LLMs understand structure, relationships, and intent—making them a powerful evolution in document processing.
With capabilities that redefine traditional data extraction and transformation, LLMs are adaptable, multilingual, multimodal, and highly contextual. They excel at transforming content and applying reasoning to create more insightful and meaningful transformations.

From ETL to easy mode: LLMs are transforming unstructured data processing
For many years, organizations relied on the ETL model—extract, transform, and load—for data processing. This process involved extracting data from its source, transforming it into a structured format optimized for analysis, and loading it into a database or data warehouse. Considering the data volumes and tools available at the time, this approach was practical and efficient.
However, our approach to unstructured data has moved beyond the traditional ETL model, leveraging advancements made possible by LLMs. These models have revolutionized data extraction and transformation, making the process significantly more intuitive and efficient than earlier methods.
How LLMs simplify data extraction
With LLMs, extracting data from unstructured sources like PDFs and images has become remarkably straightforward. Instead of writing deterministic code or relying solely on OCR, we can use LLMs to interpret and extract text intelligently and with minimal effort.
Through prompt engineering, we can guide the model to pull relevant data with high accuracy in a fraction of the time it would take using traditional methods. This “easy mode” approach eliminates much of the complexity that once plagued the extraction phase.
Similarly, the transformation step has also seen dramatic improvements. LLMs can perform contextual understanding and reformat data on the fly, bypassing the need for complex transformation scripts. What once required extensive development cycles is now accomplished through dynamic, AI-driven processing.
An AI-assisted approach to unstructured data
We leverage advanced LLMs to transform messy, raw, unstructured data into actionable insights using a streamlined ELT workflow designed for unstructured data challenges.
- Extract: We begin by extracting data from sources like PDFs, using tools like GPT-4o to convert complex document formats into flexible structures like JSON. This ensures the data is accessible and ready for downstream processing.
- Load: Next, we load the extracted data into a data lake or similar repository in JSON form. Storing the data in JSON format at this stage allows us to maintain flexibility in handling diverse and unpredictable document layouts.
- Transform: After securely storing the data, we use GPT-4o to transform it into a structured schema that optimizes relational database performance. This step prepares the data for efficient querying and analysis, optimizing it for its intended applications.
Why load before transform?
Our approach of loading data before transforming it allows for greater adaptability. By avoiding rigid transformation rules that were predefined, we can iterate and refine the process as needed to accommodate unstructured data’s complexities and unique characteristics.
Ensuring quality with human oversight
We achieve high accuracy and reliability by integrating a human-in-the-loop review at critical points in the workflow. This approach combines AI’s precision and scalability with human expertise, delivering exceptional results at a much faster pace.
What’s ahead for unstructured data
Transforming unstructured data into actionable insights is no longer a distant goal—it’s happening now. AI-powered models like LLMs help businesses extract, load, and transform data more efficiently than ever, overcoming challenges that once made unstructured data difficult to manage.
This article builds the foundation for understanding the shift in unstructured data processing. But how does this work in practice? In the next few articles, we’ll explore real-world examples of AI-powered unstructured data flows, focusing on specific industries and use cases.
Up next: Unstructured Data Flow in Practice: Receipts Data Flow – where we break down how businesses can extract, process, and gain insights from receipts using AI-driven data pipelines. Stay tuned!