Intelligent Document Processing (IDP): Your Ultimate Guide

Most people don't realize just how much data is floating around today. According to research from the IDC, the global datasphere (the total amount of data that exists), is expected to reach an eye-watering 175 Zettabytes by 2025. And some research bodies have this figure even higher, even up to 200 Zettabytes.

Numbers this big can be hard to conceptualize so let's into context just how colossal 175 Zettabytes is. Assuming an average movie file size of 4.7 GB, you could store approximately 37.2 quadrillion movies with 175 ZB of storage. That's more movies than every single person on Earth could watch in their entire lifetime, even if they lived for hundreds of years!

Of course, not all of this data is business data in the form of documents, invoices, chatbots, and emails, but a decent chunk of it is. For example, data professionals report that data volume grows by an average of 63% every month in their companies.

In other words, businesses today have to manage more data than ever before and in more formats than ever before, and this is causing significant challenges on the ground. Luckily, Intelligent Document Processing (IDP) provides a compelling solution. With this in mind, let's dive into everything you need to know about IDP and how it can improve data handling within your company.

What Is Intelligent Document Processing?

Intelligent Document Processing (IDP) is the automated extraction and processing of information from various document formats using artificial intelligence technologies. For example, companies may receive thousands of invoices in different formats and use IDP to automatically extract data such as vendor names, dates, and amounts for processing and analysis. Let's break this down further in the next section.

How Does Intelligent Document Processing Work?

Before we get into the nuts and bolts of the IDP process, it's first important to understand the different types of data and how they impact IDP. There are two primary types of data businesses deal with every day - structured and unstructured.

Structured Data is highly organized and easily searchable by straightforward, algorithmic data models (meaning this data is neatly arranged in a way that computers can easily and quickly understand and find what they're looking for, much like books neatly sorted in a library).

Structured data adheres to a specific format or schema, such as databases, spreadsheets, or forms. Some examples of structured data include financial records like invoices and receipts, and product databases. Financial records have clear categories like date, amount, and product/service details, while product databases have clearly organized information about products like stock levels, pricing, and specifications.

In structured data, the relationship between different pieces of data is clear, and they can be easily entered, queried, and analyzed. For IDP, processing structured data is generally more straightforward because the format is predictable. For example, extracting specific fields from a standardized form where the data locations are known and consistent across documents. Here, any complexity usually comes from varying structured formats - for example, a third-party vendor may use a different format for their invoices, although the data will still be structured.

Things work a little differently with unstructured data. Unstructured data lacks a predefined data model, making it more complex to process and analyze. This data type includes text documents, emails, social media posts, videos, and images - essentially anything that doesn't neatly fit into a pre-defined structure.

As you would expect, extracting unstructured data is much more challenging and this is where more sophisticated IDP approaches come in. What approaches? For unstructured data, natural language processing (NLP), machine learning (ML), and artificial intelligence (AI) play a big role in interpreting the data's meaning, context, and relevance. These technologies enable IDP systems to "understand" the content in a way similar to a human.

With that context out of the way, let's look at how IDP processes work.

1. Pre-processing: This step involves preparing the document for analysis. Technologies such as image enhancement and normalization are used to improve the quality of scanned documents or images, making them suitable for further processing.
   
   
2. Optical Character Recognition (OCR): OCR technology is pivotal in IDP, converting different types of documents, including scanned papers, PDFs, and images, into machine-readable text. This step is essential for extracting textual data from documents.
   
   
3. Natural Language Processing (NLP): NLP allows the system to understand and interpret the extracted text in the way humans do. It involves analyzing the structure and meaning of sentences, which is crucial for understanding the context and extracting relevant information accurately.
   
   
4. Machine Learning (ML) and Artificial Intelligence (AI): ML and AI are the brains behind IDP, enabling the system to learn from data patterns and improve over time. These technologies are used for tasks such as classifying documents, understanding complex patterns and relationships in the data, and making decisions based on learned information. For instance, AI algorithms can learn to identify different document types (invoices, contracts, etc.) and understand their unique structures for more accurate data extraction.
   
   
5. Data Validation and Enrichment: After extraction, the data is validated and possibly enriched with additional information. This might involve cross-referencing extracted data with internal or external databases to ensure accuracy and completeness.
   
   
6. Integration: The final step involves integrating the processed data into other business systems, such as ERP, CRM, or document management systems. This allows for a seamless flow of information and enables automated workflows based on the extracted data.

Older IDP technologies were primarily designed to handle structured data with limited variability. They relied heavily on OCR for data extraction, followed by rule-based systems for processing. These systems worked well for documents that followed a strict format, such as forms where fields are in the same place every time.

However, they struggled with unstructured data, which didn't fit into their rigid frameworks. Unstructured documents, like letters or emails, couldn't be processed accurately because the content's location and format vary widely, and understanding them requires context and flexibility beyond simple pattern recognition.

Modern IDP solutions have evolved to bridge this gap. They still use OCR as a foundational tool but have incorporated AI, ML, and NLP to handle the variability and complexity of unstructured data. These technologies enable modern IDP systems to learn from new document types and continuously improve their accuracy and efficiency over time.

Additionally, they can understand the semantics and context of the text, allowing for more accurate extraction and categorization of information across a wide range of document types, not just those that are neatly structured.

Why Is IDP Important?

The business landscape is now more competitive than ever before, shaped largely by large companies like Amazon and Netflix setting expectations for swift and convenient service. That is to say, customers expect that no matter how they contact your company, their experience will be consistently excellent. However, this undoubtedly puts pressure on companies - how do you ensure that you capture and analyze data just as quickly from a chatbot as you would from an email? With sophisticated IDP software, handling any data type becomes straightforward and allows businesses to stay competitive. Let's dive more into the specific benefits of implementing IDP.

What Are the Key Benefits of Implementing IDP in Your Business?

• Enhances Efficiency and Productivity: Automatically processes thousands of documents daily, freeing up staff to focus on strategic tasks instead of manual data entry.
• Reduces Human Error: Humans make mistakes. IDP cuts down on inaccuracies, leading to cleaner, more reliable data for decision-making.
• Saves Costs: Diminishes the need for large teams dedicated to data entry and document review. Manual processing is both time-consuming and costly. For example, one study found that document automation results in 30-200% ROI in the first year, mainly in labor savings.
• Speeds Up Processing Times: Digital workers are vastly faster than their human equivalent, sometimes up to 20 times faster. This means companies can get more done in less time.
• Allows for Easy Scalability: IDP software can handle growing document volumes without the need to hire additional staff.
• Improves Compliance and Security: Enforces consistent processing and maintains audit trails, crucial for sectors like banking, where adhering to regulatory standards for document handling and protecting customer data is paramount.

How Can IDP Be Applied to Specific Tasks and Processes Within Your Organization?

Exactly what you need your IDP system to do will vary from business to business, but here are some examples of tasks that will apply to most businesses.

• Accounts Payable and Receivable Management: IDP can automate data extraction from invoices, receipts, and bank statements, facilitating faster payment processing and reconciliation. This improves cash flow management, reduces the risk of late payments, and enhances relationships with suppliers and customers.
  
  
• Customer Onboarding: Businesses can use IDP to streamline the onboarding process for new customers by automatically extracting information from identification documents, forms, and contracts. This not only speeds up the process but also ensures accuracy in customer data, leading to better service and compliance with regulatory requirements.
  
  
• Email and Communication Management: IDP can automatically categorize and extract relevant information from incoming emails and other communications. This enables businesses to quickly respond to customer inquiries, orders, and service requests, improving response times and customer satisfaction while reducing the workload on staff.
  
  
• HR: IDP can automate the processing of resumes, applications, and employee forms, reducing manual entry and speeding up the hiring process.

What Strategies Should You Consider for Effectively Implementing IDP?

Once IDP systems are up and running, they offer concrete and repeatable benefits for businesses. However, there are a couple of things you need to do before to ensure you get the most from your IDP solution.

First, companies should conduct a thorough analysis of their document workflows to identify processes that would benefit most from automation. For example, you might find that your accounts payable department spends an excessive amount of time manually entering data from invoices into your financial system - so much so that you're considering hiring more staff. This makes accounts payable an ideal candidate for your first IDP pilot over another team that, while would benefit from lower costs and faster processing, isn't struggling as much.

Next, choosing the right IDP solution that integrates seamlessly with existing IT infrastructure is crucial to ensure compatibility and efficiency. Training is also a key strategy, not just for IT staff but for end-users who will interact with the IDP system, ensuring they understand how to leverage its capabilities fully.

Additionally, businesses should prioritize scalable solutions that can adapt to increasing volumes of documents and evolving business needs. Regular monitoring and evaluating the IDP system’s performance are essential to identify areas for improvement and ensure the system continues to meet your organization's objectives. This step is essential to maximizing ROI and enhancing operational efficiency.

What Are the Best Practices for Managing IDP Systems and Workflows?

Okay, so where do you begin? Here are the critical steps to an effective IDP implementation.

• Comprehensive Needs Assessment: Before implementation, conduct a detailed analysis of document types and processes to ensure the IDP solution addresses specific business needs. Remember, solutions should fix problems.
  
  
• Scalable and Modular Implementation: Start with a pilot project to test the IDP system on a small scale before expanding, allowing for adjustments and scalability based on the initial results.
  
  
• Integration with Existing Systems: Ensure the IDP system integrates seamlessly with current business systems (e.g., CRM, ERP) to leverage existing data and workflows effectively.
  
  
• Continuous Training and Support: Provide ongoing training for users and IT staff to adapt to the IDP system, including understanding its capabilities and troubleshooting common issues.
  
  
• Regular Performance Monitoring: Establish metrics to evaluate the IDP system’s performance regularly, ensuring it meets efficiency and accuracy goals.
  
  
• Feedback Loops for Improvement: Implement feedback mechanisms for users to report issues or suggest improvements. This way, you can continuously optimize your workflows for the best results.
  
  
• Data Security and Compliance: Prioritize data security measures and compliance with regulations to protect sensitive information processed by the IDP system.

What Are the Emerging Trends Shaping the Future of IDP?

As time goes on we'll see the integration of more advanced artificial intelligence (AI) and machine learning (ML) models for deeper understanding and processing of complex documents.

Here, a complex document might be something like a multi-party contract with variable structures, complex legal terminology, and annotations. These documents can vary significantly in format, contain detailed clauses with intricate dependencies, and may include handwritten notes or amendments in the margins. Today, documents like these are typically processed manually, but advanced IDP solutions will be able to handle them with ease in the coming years.

Additionally, there's a growing emphasis on federated learning for data privacy. Federated learning is an approach where machine learning models are trained across multiple decentralized devices or servers holding local data samples, without exchanging them, allowing businesses to keep their sensitive data private and secure.

For example, banks could use IDP-based federated learning to analyze financial documents across various branches without centralizing sensitive customer information, thereby enhancing fraud detection capabilities without compromising client confidentiality.

Lastly, we're seeing blockchain and no-code/low-code platforms become more popular in IDP, democratizing access and allowing users to tailor solutions to specific industry needs.

Final Thoughts

Intelligent Document Processing is transforming businesses today and will continue to be a key driver of digital transformation in the coming years. By investing in IDP now, you can increase efficiency, accuracy, and scalability, and stay competitive in an increasingly automated and data-driven marketplace.

Book a consultation, and let us show you how you can streamline your processes and tackle complex challenges using AI and automation. We guarantee that you will confidently know how to innovate your business and step into the future.