Automated Data Extraction & Extracting Data from PDFs: Why OCR Alone Is Not Enough

PDFs have become the default way businesses exchange information. Whether it is insurance claims, financial statements, contracts, or invoices, every industry runs on document workflows. But when those documents need to be converted into structured data that systems and analysts can use, companies quickly run into a problem: PDFs are designed for humans, not machines.

For years, the go-to answer has been Optical Character Recognition (OCR). OCR promises to “read” PDFs and convert them into text. But for enterprises that need reliable, scalable data pipelines, OCR turns out to be just the beginning of the journey — and often the most brittle part.

This article explores the difference between traditional OCR-based data extraction tools and Nomad Data’s managed approach. We will explain why OCR alone struggles, the hidden costs of building your own pipelines, and how Nomad Data’s automated data extraction platform helps businesses move from documents to insights — without the complexity.

Data Extraction: The Limits of OCR

At its core, OCR is a technology that takes an image of a document and tries to recognize the characters it contains. In theory, this should unlock the ability to extract data from PDF documents at scale.

But in practice, OCR is brittle. It depends on the document's structure. When a form is designed in a standard way, OCR can work well. But most real-world business documents do not follow strict templates.

“One of the biggest challenges with OCR is how brittle it is,” explains Brad Schneider, CEO of Nomad Data. “For example, in the insurance industry, death certificates are designed by every single municipality. They all have the same general information, but the structure is quite different. OCR is typically optimized to read certain structures if you want to get remarkably high quality. And so, with documents such as a death certificate, the quality is poor. The same is true of investor financial statements, bank statements — same general information, but quite different formats. In these cases, OCR tends to break quite easily.”

What starts as a promising way to extract text quickly becomes an exercise in managing exceptions, cleaning messy outputs, and retraining models for each new variation.

The Real Cost of DIY OCR Pipelines

When companies try to solve OCR’s brittleness, they usually go down one of two paths: build their own pipelines with internal engineers or bring in consultants to assemble a patchwork of solutions.

Either way, the outcome is rarely satisfying.

“Typically, what we’ve seen is that the company either has internal resources or they hire external consultants, and they build a solution from scratch,” says Schneider. “They might train different OCR or machine vision models for their forms, but they break a lot. And so, what ends up happening is they need teams of people to maintain these pipelines to ensure that the OCR is working properly. And when it does not, they still must layer in many people to proofread the documents. You get this situation that is half automated, where nobody is happy with the output.”

The consequences are significant:

• High engineering costs. Teams must constantly tweak and retrain models, build pre- and post-processing steps, and monitor performance.
• Operational inefficiency. Every exception that OCR misses must be caught manually, meaning companies still rely on human labor at scale.
• Time-to-value delays. Instead of quickly unlocking insights from documents, companies spend months — sometimes years — perfecting their pipeline.
• Hidden overhead. As source documents evolve, the pipeline breaks. Entire teams can end up dedicated just to keeping things running.

One Nomad Data client illustrates the point starkly.

“We work with a client that’s spending multiple six figures to deal with a document ingestion problem,” Schneider notes. “They have tens of thousands of documents coming in every month. They use a combination of OCR technologies from two different vendors, plus a team of dozens of people to deal with exceptions. Even with that, their backlog of documents is in the hundreds of thousands. Every day, that backlog grows because the process is too brittle. They were spending a lot of money and not even getting something successful. That is why they decided to move the process entirely over to Nomad.”

Why Automated Data Extraction Needs to Go Beyond OCR

At this point, it becomes clear: OCR is not a solution, it is just one ingredient in a much larger recipe. Useful data extraction tools need to handle messy realities: documents with shifting layouts, mixed tables and narratives, handwritten notes, and exceptions that do not fit a clean pattern.

Enter Nomad Data’s automated data extraction platform.

Rather than asking clients to build and maintain their own fragile pipelines, Nomad provides a simple API or even a fully managed pipeline. Businesses simply send PDFs — or let Nomad retrieve them directly — and receive structured, usable data back.

“Nomad’s technology is not dependent on the structure being the same,” Schneider explains. “It can handle enormous variations for the same type of forms. This leads to situations that are far less brittle, whereas these forms are changing over time, the process continues to work. The percentage of documents that successfully go through the entire process is significantly higher. And with Nomad Data, the client does not really have to build anything. They can drag and drop files to our UI or have them sitting in a third-party location where we pull and process them, and then we deliver the data back. If something goes wrong, it is on us to fix the format and deal with the complexities. That is a big deal.”

The message is simple: instead of companies investing millions in infrastructure they are not equipped to manage, Nomad offloads the entire problem.

Automated Data Extraction: From Complexity to Simplicity

The difference for businesses is profound. With traditional OCR-based workflows, companies are forced to become document engineers, dedicating staff, and budget to maintaining brittle systems. With Nomad Data, they skip straight to the insights.

Key benefits include:

• Speed to insights. What once took months to build and refine can now be operational in days.
• Reduced costs. No need for large engineering teams or armies of document proofreaders.
• Scalability. Whether it is thousands or millions of documents, the system adapts without requiring more internal resources.
• Reliability. Exceptions and format changes are handled by Nomad, not by client teams scrambling to patch things.

Business Transformation Through Automated Data Extraction

The implications go beyond efficiency. For industries where documents are the lifeblood of operations — insurance, finance, logistics, healthcare — automated document workflows are transformative.

“For paper-based industries, it’s transformative,” says Schneider. “It dramatically increases processing times, increases customer satisfaction, and lowers costs. It allows companies to focus on the things they are good at and outsource the things they are not. In a lot of industries, reading data accurately from forms is the key input to the business. Just think about the insurance industry — they are getting thousands of documents on an hourly basis, and they must do enormous work to sort, classify, and extract information for the rest of their business to run. That is a huge overhead to manage when it has truly little to do with their actual core competency.”

When companies can extract data from PDF documents reliably and automatically, they can reallocate resources to higher-value tasks, deliver faster service to customers, and stay competitive in industries where speed and accuracy matter.

Why Managed Pipelines Are the Future

The shift away from homegrown OCR pipelines mirrors a broader business trend: companies increasingly outsource infrastructure-heavy, non-differentiating processes to specialized providers. Just as few companies today build their own servers or email systems, fewer will want to own fragile document-extraction pipelines tomorrow.

Nomad Data is at the forefront of this transition, turning what used to be a major source of hidden cost and frustration into a simple, reliable service. For business leaders, the decision becomes clear:

• Keep investing in brittle pipelines and the staff required to maintain them, or
• Adopt a managed solution that delivers clean, structured data without the burden.

Moving Beyond OCR

OCR alone does not solve the PDF problem. At best, it is the first step in a long, costly process that most companies are ill-suited to manage. What businesses truly need is not just text recognition — its usable, structured data delivered reliably and at scale.

Nomad Data’s automated data extraction platform delivers exactly that. By handling all the complexities behind the scenes and providing a simple API or managed pipeline, Nomad transforms document workflows from brittle, resource-heavy headaches into streamlined, scalable assets.

For any company struggling to extract data from PDF documents or drowning in backlogs, the message is simple: OCR is just one piece of the puzzle. With Nomad Data, you get the whole solution.