Intelligent Document Processing

The guide to process automation for unstructured documents

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The guide to process automation for unstructured documents

Multiple analyst estimates suggest that 80% to 90% of data comprises unstructured information such as text, video, audio, web server logs, contracts, letters, emails, and social media content. This vast data collection represents an untapped resource with immense potential for businesses to gain a competitive edge in their respective industries. 

However, companies face significant challenges when attempting to process unstructured documents. The main obstacle lies in their inherent variability. Unlike structured data, which is systematically organized in a searchable format, unstructured data does not conform to traditional data models.

While organizations have relied on human intervention to handle unstructured document processing, the approach is error-prone, unscalable, and time-consuming.

Organizations have thus turned to advancements in artificial intelligence and natural language processing to overcome this challenge. By leveraging automated systems powered by ML algorithms, companies can extract relevant information from unstructured documents more efficiently and accurately. These systems can automatically analyze and interpret textual content, identify critical data points, and populate the desired destination systems.

In this article, we will understand the benefits of automation and the best practices to implement the same.

The role of process automation in handling unstructured documents

Process automation plays a vital role in efficiently handling unstructured documents. By leveraging automation technologies like natural language processing (NLP), optical character recognition (OCR), and machine learning, companies can streamline the analysis and extraction of valuable information from text files, PDFs, emails, and more.

These technologies enable the extraction of relevant data points, text classification, sentiment analysis, entity recognition, and other valuable insights from unstructured documents.

Automation workflows can also be designed to extract specific information, transform it into structured formats, and integrate it with downstream systems or databases for further analysis or decision-making. They also allow for scalability and speed, enabling organizations to efficiently handle large volumes of unstructured documents. By automating repetitive tasks, companies can improve productivity, reduce costs, and unlock the untapped potential hidden within unstructured data.

The characteristics and formats of unstructured data

Unstructured data is characterized by a lack of organization, making it challenging to locate and analyze. Unlike structured data, it does not follow a consistent or standardized format. Examples include free-form text, multimedia files, social media posts, emails, web pages, sensor data, and more. Such data often contains subjective or contextual information that requires interpretation and analysis to derive meaningful insights. It may include opinions, sentiments, narratives, or descriptive text.

Following are the most common formats for unstructured data:

  • Text: This is one of the most common formats of unstructured data, which includes documents, reports, articles, emails, chat transcripts, social media posts, and any textual information without a specific structure.
  • Images: Unstructured data can comprise various image formats, such as JPEG, PNG, GIF, or TIFF files. These images range from photographs, scanned documents, and screenshots to diagrams and charts.
  • Audio: Unstructured data can also manifest in audio formats, including recordings, podcasts, voicemails, interviews, or audio content without a predefined structure.
  • Video: They also exist as video files, such as MP4, AVI, or MOV formats. Videos can include surveillance footage, webinars, or visual content lacking a structured data model. Additionally, you can convert a file into a video format using a video converter.
  • Social media data: This includes data from social media platforms like Twitter, Facebook, LinkedIn, or Instagram, including posts, comments, likes, shares, and multimedia content users share.
  • Web server logs: These include server logs generated by websites or applications, capturing information about user activities, errors, requests, and other system-level details.
  • Sensor data: Unstructured data also originate from sensors or Internet of Things (IoT) devices, providing measurements, readings, or raw data from various sources such as environmental sensors, wearables, or industrial equipment.

Unstructured document processing challenges

Processing unstructured documents poses several challenges due to their inherent complexity. Here's an overview:

1. Lack of structure and consistency

Processing unstructured documents involves handling diverse formats, such as text files, PDFs, emails, images, etc. These documents do not follow a predefined format or schema, making it difficult to extract relevant information.

Moreover, unstructured documents often contain text or content that is not easily understood or interpreted without context. The absence of structured elements hampers the ability to derive meaning from the data.

2. Noisy data

Unstructured documents may contain errors, duplications, incomplete information, or outdated content, negatively impacting the analysis and decision-making processes. Ensuring data quality can be particularly challenging due to the lack of a predefined structure and format. 

3. Data search and retrieval

Unstructured documents pose challenges for searchability and indexing compared to structured data. Locating specific information within unstructured documents becomes time-consuming and less efficient, hampering productivity.

Moreover, the inability to quickly retrieve relevant documents can hinder decision-making processes and impede collaboration among team members. Critical information may be scattered across different platforms, file formats, or physical storage, making accessibility difficult.

4. Risk of non-compliance

Unstructured data, such as untagged or unorganized documents, can contain sensitive or regulated information. Failure to adhere to data privacy regulations, industry standards, or internal policies during data processing can have severe consequences. This includes data breaches, legal penalties, reputational damage, and loss of customer trust. 

5. Increased threat of cyber attacks

Uncontrolled data growth and disorganization significantly raise the risk of cyber threats. This risk is particularly heightened with unstructured data, as it is more susceptible to mismanagement and is often stored in isolated data systems, creating data silos.

6. Lack of scalability

Unstructured documents can be abundant, resulting in a vast amount of data to process and analyze. They come in various formats and contain complex information like natural language text, multi-layered data, and contextual dependencies. Thus, scalability issues arise when attempting to efficiently handle large volumes of unstructured data.

Traditional systems fall short in handling the sheer scale of unstructured documents, leading to performance degradation and increased processing times. Addressing these challenges requires a combination of scalable infrastructure, distributed computing technologies, and advanced algorithms.

7. Workflow integration

Integrating unstructured document processing into existing workflows and systems can be complex. The process typically involves multiple stages, including data extraction, transformation, analysis, and storage. Ensuring seamless coordination between these stages and existing systems requires meticulous planning and integration efforts. 

Benefits of leveraging process automation for unstructured documents

Leveraging process automation for unstructured documents can provide several benefits. Here are some key advantages:

1. Increased efficiency and accuracy

Process automation for unstructured documents streamlines tasks, enabling faster, more accurate information extraction. It eliminates time-consuming manual data entry and repetitive tasks, allowing organizations to handle larger document volumes efficiently. By minimizing human error, automation ensures consistent and reliable data extraction, leading to improved data quality and better decision-making capabilities.

2. Faster turnaround time

Automation expedites document processing by minimizing manual processing time and removing bottlenecks. It facilitates rapid document ingestion, extraction, and analysis, reducing turnaround times. For instance, in loan applications, automation can swiftly extract relevant information from unstructured documents such as bank statements, pay stubs, and tax records. This enables financial institutions to accelerate loan approval, providing faster responses and enhancing operational efficiency.

3. Increased cost savings

Process automation reduces costs associated with manual data entry, document classification, and verification. Organizations can redirect resources to higher-value activities by automating repetitive and time-consuming tasks, leading to cost savings and increased productivity. 

4. Enhanced scalability

Organizations can efficiently handle larger document volumes by automating data extraction, transformation, and analysis tasks. With the ability to distribute workloads across multiple processing units and leverage cloud-based services, automated systems can scale horizontally to accommodate increased document volumes.

Additionally, automation allows for centralized control and management, enabling organizations to define workflows, monitor performance, and track metrics in a streamlined manner.

5. Data insights and analytics

Automation simplifies the extraction of structured data from unstructured documents. It enables more straightforward analysis and derives actionable insights. By automating the extraction of critical information, organizations can evaluate customer sentiment, identify trends, and assess risks. These insights contribute to data-driven decision-making and enhance overall business intelligence.

6. Better compliance management

By automating compliance checks, organizations can ensure that all documents undergo thorough and standardized compliance assessments. This includes regulatory requirements, verification, identifying sensitive information, and flagging potential risks. It facilitates the creation of comprehensive audit trails and documentation. Automated systems can also track and log all actions, decisions, and changes made while processing documents, providing a clear and transparent record. 

7. Increased risk mitigation

Automated systems can efficiently identify and flag risky patterns and irregularities within the documents by leveraging predefined rules, machine learning algorithms, and anomaly detection techniques.

Key technologies for automation

Several technologies are utilized in automating unstructured document processing. Here are some prominent ones:

1. Optical character recognition (OCR) for text extraction

Optical Character Recognition (OCR) is a technology that converts scanned documents, images, or PDFs into machine-readable text. It involves recognizing and extracting characters, words, and text elements from the document images and transforming unstructured content into structured and analyzable text data.

2. Natural language processing (NLP) for semantic understanding

Natural Language Processing (NLP) techniques understand, analyze, and extract meaningful information from unstructured text data. They are used for entity recognition, sentiment analysis, topic modeling, language translation, etc. By applying NLP algorithms, unstructured documents can be processed, organized, and transformed into structured data, enabling automated analysis and interpretation of valuable insights from text content.

3. Machine learning algorithms (ML) for pattern recognition

Machine learning (ML) algorithms are pivotal in automating diverse tasks in unstructured document processing. Supervised learning algorithms are applied to tasks like document classification, where documents are labeled and used to train models for accurately categorizing new documents. Unsupervised learning methods, such as clustering or topic modeling, enable the organization and categorization of unstructured documents based on similarities or latent topics within the content.

Steps to implement automation for unstructured documents

Automating unstructured document processing comprises the following key stages:

1. Document ingestion

The initial stage is to bring the unstructured documents into the automated processing system or single cloud-based storage. Document sources include file systems, databases, email servers, or external APIs. Implement secure and efficient mechanisms to fetch the documents for further processing.

2. Pre-processing

Preprocessing encompasses cleaning and refining the documents to prepare them for advanced analysis. This stage involves various tasks, such as eliminating irrelevant information, standardizing document formats, and converting file types using OCR and NLP techniques.

3. Data extraction

Extract relevant information from the documents using techniques like OCR, natural language processing (NLP), or pattern matching. Extracted data may include entities, keywords, dates, or other structured elements within the unstructured content.

4. Text analysis and understanding

In this stage, various techniques are employed to comprehend the content of the documents and extract valuable insights. These include sentiment analysis, which determines the sentiment expressed in the text, and provides information on whether it is positive, negative, or neutral. This can be valuable for gaining insights into customer feedback or public perception. 

Similarly, entity relationship extraction is employed to identify connections between entities mentioned in the text, such as linking a person's name with the organization they work for. This aids in uncovering essential relationships within the document's content.

5. Document classification and sorting

ML and NLP techniques are used to categorize and organize documents based on their content and characteristics.

A machine learning model is trained on a labeled dataset derived from extraction and annotation. The labeled dataset consists of documents with their corresponding categories or classes. Once trained, the model can classify new, unseen documents into predefined categories or classes. These categories can be based on document types (e.g., invoices, contracts, emails), topics (e.g., finance, healthcare, technology), or other relevant criteria.

After classification, the documents are automatically sorted and routed to the appropriate processing workflows or departments based on their assigned categories.

Industry best practices for successful process automation

Successful process automation of unstructured documents requires careful planning, implementation, and adherence to industry best practices. Here are some essential points to remember.

1. Document analysis and process mapping

Conducting a thorough document analysis and process mapping is a systematic approach to preparing for the automation of unstructured documents. It involves collecting and analyzing diverse document samples to understand their content, layout, and variability. Organizations can pinpoint automation opportunities by mapping the existing manual processes and integration points and identifying data extraction requirements. The analysis helps select appropriate technologies like NLP, OCR, and machine learning while ensuring compliance with regulatory requirements.

2. Data quality and preprocessing

The accuracy and efficiency of ML models largely depend on the quality of datasets. And thus, it is essential to implement efficient preprocessing techniques. These techniques involve cleaning and normalizing the extracted text, including noise removal, format standardization, and effectively handling misspellings. 

3. Choose the right technology

Look for software that offers robust Natural Language Processing (NLP) and machine learning capabilities. This allows the software to understand the context and meaning of unstructured text, enabling features like entity recognition, sentiment analysis, and summarization. 

Consider the software's scalability and performance capabilities. It should handle large volumes of documents efficiently without compromising processing speed or accuracy. 

4. Data security and compliance

Thoroughly prioritize data security and compliance, mainly when dealing with sensitive or confidential information. Implement robust encryption techniques, enforce strict access controls, and establish compliance measures to safeguard data privacy effectively. Adhere to industry regulations like GDPR, CCPA, and HIPAA, ensuring full compliance with their stipulations. 

5. Review and monitor performance

Monitoring performance metrics is essential to ensure the system's effectiveness and efficiency. Measure the accuracy and precision of the document processing system. Assess its recall throughput, processing time, and error rate. Continuous monitoring of these performance metrics allows for iterative improvements and fine-tuning of the document processing automation system, ensuring it consistently meets the desired accuracy, efficiency, and compliance levels.

6. Integration 

Before integration, a comprehensive evaluation of an organization's legacy system is vital to identify potential risks and compatibility issues. Integrating with legacy systems can be complex due to data incompatibility, lack of standardization, security vulnerabilities, and resistance to change.

To address these challenges, implementing middleware is a common approach to bridge the gap between the new system and legacy infrastructure. Additionally, starting with a small-scale implementation before a full rollout across the organization can prove beneficial. 


In conclusion, automating unstructured document processing brings numerous benefits to organizations. It helps handle large document volumes efficiently, improve data accuracy, and derive valuable insights by leveraging technologies like OCR, NLP, and ML algorithms. Process automation streamlines tasks, reduces manual intervention, and enables faster turnaround times, leading to cost savings and increased productivity. Moreover, it enhances risk assessment and compliance by applying consistent checks, identifying potential risks, and ensuring real-time monitoring. 

Suggested Case Study
Automating Portfolio Management for Westland Real Estate Group
The portfolio includes 14,000 units across all divisions across Los Angeles County, Orange County, and Inland Empire.
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Pankaj Tripathi
Written by
Pankaj Tripathi

Helping enterprises capture data for analytics and decisioning

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