In every manufacturing and logistics company, there’s an invisible tax on efficiency — manual data extraction.

Your teams spend hours copying information from PDFs, spreadsheets, and scanned forms. But they find that half of it doesn’t match downstream systems. As a result, shipments get delayed because a purchase order field was missed or payments stall because a supplier invoice was mislabeled.

Very quickly, this problem snowballs into an operational bottleneck. The problem comes down to the tools and processes you use today.

In this guide, we’ll unpack the 11 biggest challenges that make data extraction harder than it should be—and how you can fix them.

1. Datasets aren’t consistent and standardized 

In most logistics and manufacturing firms, data comes from various sources and is handled by multiple users. As a result, datasets often become inconsistent and lack standardization.

Elmo Taddeo, CEO at Parachute describes his experience:

“A significant percentage of documents don't fit standard templates. Forms with handwritten notes, unique customer requests, or industry-specific compliance documents often require manual processing. A rough estimate from speaking with business leaders suggests that anywhere from 20-40% of documents fall outside standard formats.”

Seventy seven percent of organizations acknowledge they have serious data quality issues. And 91% even admitted that it impacts their company’s performance.

2. Accuracy of LLMs in data extraction

Large language models (LLMs) might seem like a powerful solution for extracting data. But their accuracy in real-world extraction tasks is often questionable. 

In a feasibility study of using GPT-4 for data extraction in systematic reviews, the model achieved only ~80 % accuracy, with performance varying by domain.

When you rely on LLMs for extracting data from critical documents, you can’t be fully sure the data is accurate. For instance, the LLM could misread values or misplace fields and one error could cost you thousands of dollars. 

3. Spikes in document volume become hard to manage

In finance and manufacturing, steady months can flip fast, sudden surges of invoices and orders demand immediate, accurate processing. These spikes can quickly overwhelm teams, especially when there aren’t enough resources to manage the extra workload. 

As our CTO, Jishnu NP, explains:

The good news? Docxster can process multiple document types simultaneously with minimal human intervention. Your team only steps in when confidence scores drop or a human review is required, ensuring speed and accuracy even during peak periods. That means when your business grows or experiences sudden spikes in volume, Docxster scales with you.

4. Lack of balance between automation and human oversight

A leap forward with data extraction can also come with a new shadow. You might deploy automation to help scale automation but we cannot ignore the challenges of it. 

While automation helps manage repetitive tasks at scale, an over-reliance on it can amplify errors instead of reducing them. 

Simon Poole, Operations Director at Barrington Freight, explains his experience:

Let’s say you set up an automation tool to extract shipment data from carrier invoices. Then one carrier updates their layout, and suddenly your system starts misreading delivery dates and amounts. Before you notice, those small errors spread across reports and slow everything down.

To avoid this chaos, you need to find the right balance between automation and human oversight.

5. Lack of data ownership

Gartner’s 2024 Data Quality report found that business leaders don’t understand how data quality affects enterprise outcomes. When ownership sits between IT and business, no one truly takes charge. As a result, incorrect output floods your data stream and nobody know what’s happening.—so extraction errors persist and fixes never get deployed.

This happens because business teams assume IT owns data accuracy, while IT believes it’s a business issue. With no shared KPIs or visibility into downstream impact, accountability fades, and quality slips through the cracks.

You can formalize ownership through a RACI matrix that clarifies who is:

• Responsible (data stewards)

• Accountable (business leaders)

• Consulted (IT/engineering)

• Informed (dependent teams)

This structure ensures accountability is shared but never lost.

6. Integrations with automation tools don’t work well

Even when companies invest in automation to simplify data extraction, integration often becomes the real challenge. Seventy-four percent of manufacturers and engineers rely on legacy systems and spreadsheets for critical, daily business tasks including data extraction. 

As Nikita Sherbina, Co-Founder and CEO at AIScreen, explains:

Just like Sherbina did, you could also solve this in these ways:

• Audit and clean legacy data first
• Standardize data exchange formats (like JSON or XML) 
• Involve both IT and business users early

Once those foundations are in place, the right platform can make integrations actually work.

7. Security and trust concerns with cloud-based data extraction 

As organizations move document processing and data extraction to the cloud, concerns around privacy, control, and data security continue to rise. The risk isn’t hypothetical—44% of organizations have experienced a cloud data breach, with 14% suffering one in just 2024.

Even small configuration errors or weak controls can expose your sensitive data, trigger compliance risks, and break client trust.

Docxster is built with these standards at the foundation. We’re GDPR and ISO 27001 certified, offering encryption, access control, and full audit trails by default. That means your extraction workflows stay compliant and secure—without adding operational overhead.

8. Alignment issues within the team and corresponding processes

Your teams and processes need to be aligned to make extraction tools work. For example, if your departments use different file names, formats, or handling methods, automation will break down and errors will multiply.

“When we implemented an automation tool to streamline invoice processing across three departments, the tool functioned as intended. However, each department used different file naming, storage, and structuring methods. This inconsistency caused frequent automation failures and a rapid increase in exceptions.” says Matt Mayo, Owner of Diamond IT

The issue is not the technology but how people and processes adapt to it. Finance and operations teams need to be trained, and existing workflows must be reviewed and standardized before scaling data extraction.

9. Rigid templates break when document formats change 

Most data extraction systems start with rigid templates: you define positions, zones, or fields based on a fixed layout, and extract data accordingly. It’s the norm in many legacy and semi-automated systems because it’s simple. 

But what happens when a document format changes? These templates do not work for you. 

As Jishnu told us in a conversation:

“If you are processing data in your template-based software that you have and then putting that data into your accounting software or Excel, the output might miss out on the many values that you need. So then they have to either fall back and sort or someone has to manually do the job.”

The only way to escape this challenge? Your team needs to break out of template-based systems. Because Docxster is built for real-world variability, it doesn’t rely solely on static templates. It layers AI, heuristics, and validation over templates, so layout changes don’t break the system.

10. Resource gaps limit accuracy and scalability

Many organizations manage to set up data extraction or data quality programs in one department—but can’t scale them. Gartner’s 2024 research notes this happens because of a shortage of skills, experience, and dedicated resources. While one team may run efficient workflows, others often lack the technical expertise or bandwidth to maintain the same standards.

It comes down to your people and processes. Unless you train your staff and expect them to take clear ownership, you won’t be able to scale data extraction processes.

That’s why you need to focus on the following:

• Upskill existing teams on data extraction and governance fundamentals
• Standardize processes so successful models can be reused elsewhere
• Centralize oversight to share tools and resources efficiently

11. The cost of building data extraction systems in-house 

Building a data extraction system internally can quickly become expensive once staffing and maintenance are factored in. The biggest cost driver is talent—engineers who design, build, and maintain extraction pipelines.

According to Indeed (US) and Glassdoor (India):

• Average Data Engineer salary (US): $124,000–$132,000 per year
• Average Data Engineer salary (India): ₹10L per year (base pay)

Here’s a simple breakdown based on that math:

In addition, you can expect to pay for:

• Infrastructure
• Tools
• Maintenance costs

Instead, choose a document automation platform like Docxster that does the heavy lifting of data extraction for you.

Don’t let data extraction challenges stop you from making the most of your data 

It might start with one folder, but as formats multiply and systems evolve, the real challenge is keeping everything connected. Accuracy, scalability, and consistency become harder to maintain—especially when each new source plays by different rules.

That’s where modern extraction tools make a difference. Instead of patching together processes, you can rely on intelligent systems that adapt as your data grows.

That was the first challenge we actually wanted to solve at Docxster. Data extraction shouldn’t be a cumbersome process that takes away your time and focus from the strategic tasks that matter. So, why not automate your data extraction process with just one workflow today?