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The complete process of using intelligent tools to extract data from documents and process it to derive meaning and relevance is termed Intelligent Document Processing (IDP). In this article, we will differentiate between data extraction and data extraction from documents, learn about Automated Data Extraction, discuss use cases of data extraction in industries like lending, insurance, CRE, and logistics, learn about different data extraction techniques, and help you find which technique might be for you.
So, let’s jump right into it:-
What is Data Extraction?
Data extraction can be defined as the process of transforming unstructured or semi-structured data into structured information. This structured information provides companies with meaningful insights to be available for reporting and analytics.
Data extraction helps consolidate, process, and refine information so that you can store it in a centralized location for further analysis and record-keeping. Data extraction is the initial step in ETL (extract, transform, load) as well as ELT (extract, load, transform) processes.
Automated data extraction is the process of extracting data from unstructured or semi-structured data without manual intervention. AI/ML-based data extraction technique is used for automated data extraction. Intelligent Document Processing (IDP) is an automated pipeline with components like document classification, data extraction, and data analytics. Data extraction, the most important component of IDP, is responsible for extracting key-value pairs and tables from the document.
Key-value pair is a type of data extracted from documents. A key-value pair consists of two related data elements: a key, which is a constant that defines the data set (e.g., Invoice number, Seller address, Total amount), and a value, which is a variable that belongs to the set (e.g. INXXXT65532, 240 Washington St, Boston, MA 02108, United States, $68637). Fully formed, a key-value pair could look like these:
Invoice number = INXXXT65532
Seller address = 240 Washington St, Boston, MA 02108, United States
Total amount = $68637
Fig: Key-value pair extraction
Generally, a table provides useful structural representation that organizes data into rows and columns and aims to capture the relationships between different elements and attributes in the data. Another type of table is nested tables, which are generally present in documents like rent-roll and are hard to extract.
Tables also vary in layout based on the type of documents. Financial statements, rent rolls, invoices all have different layouts present.
Data extraction from Documents
Usually, the data is stored cleanly structured tables as rows and columns within a database. This is how this data looks:-
Over time, systems started dealing with long, textual data which was made of long strings of typed characters. This was slowly complemented with images, videos, spreadsheets, audio files, and other multimedia content. This data was collectively referred to as unstructured data because it did not have any fixed format.
When we look at documents from this lens, all documents collectively can be categorized into the unstructured data category. This is the first point of confusion - unstructured and structured data do not map to structured and unstructured documents.
All documents are unstructured data. But within these documents, we can further classify them into three categories based on how they appear:
1. Structured Documents
Structured documents contain a set of information where the formatting, number, and layout are completely static from one document instance to the next. E.g the name of the person will be at the same location. These documents are also called fixed forms. For example, W2 forms, W9 forms, Accord forms, and most questionnaires are fixed forms. These forms are usually distributed as blank forms ideally with constrained text boxes and "fill-in-the-bubble" responses. Other structured documents will be payment slips and utility bills from a provider. Driver’s License, Passport, and Citizenship are some other Structured Documents.
2. Semi-Structured Documents
Semi-structured Documents have a fixed set of data but no fixed format for this data. In some documents, the date appears in the top right corner, in another variation, it is at the center of the document, and in yet another, you’ll find it in the bottom left corner. Another added complication is that different names qualify the same data. In one variation, a field may be called ‘Purchase Order Number’, in another - ‘PO Number’, and a few others may call it “PO #”, “PO No.” or “Order Number”. Invoices, Rent Rolls, and Financial Statements are some of the Semi-Structured Documents
3. Unstructured Documents
Unstructured documents contain information presented in a free format, without any specific layout or organization of content. This means that the information in an unstructured document is not organized in a specific way or separated into specific sections or fields, and there is no standard method for extracting or processing the information. Examples of unstructured documents include emails, letters, and reports.
Data Extraction Techniques and Algorithms
The process of data extraction acquires data from source systems and stores the extracted data in a ‘data warehouse’ for further examination.
There are two options for extraction methods -
1. Logical Extraction
Establishing a visual integration flow is imperative when extracting data logically. It helps developers devise a physical data extraction plan.
With the logical map in place, you must decide on which extraction approach to choose -
All data gets extracted directly from the source system in its entirety. You don't have to account for any logical data such as timestamps to be associated with source data, since you are copying everything contained in the source system, entire tables in one go.
For instance, assume that your source database has 500 records or more. The process would be faster if you use the SELECT and FROM database commands to copy the table.
If you include the WHERE clause on timestamps, extraction would take more time to begin, according to the size of the table and if the timestamp column is indexed.
Data gets extracted in increments using this approach. This approach extracts data that has been altered or added post a well-defined event in the source database.
Well-defined events mean anything that is trackable within the source system via timestamps, triggers, or a custom extraction logic built within the source system.
In transactional operations, common master tables such as Product and Customer comprise millions of records, making it illogical to perform full extraction every time and analyze the previous extraction with the new copy to mark the changed data.
2. Physical Extraction
A physical extraction performs a bit-by-bit copy of the full contents of the flash memory of a mobile device. This extraction technique enables the collection of all live data as well as data that is hidden or has been deleted. By creating a bit-by-bit copy, deleted data can get potentially recovered.
Source systems typically have certain restrictions or limitations. For instance, extracting data from obsolete data storage systems through logical extraction is inconceivable. Data extraction from such systems is only feasible via Physical Extraction, which is classified further into Online and Offline Extraction.
There are three methods to extract data from documents:
1. Manual Data Entry
Humans read the document and manually enter the data into the systems. Manual data entry can be a simple and easy-to-use method for entering small amounts of data. Still, it can be time-consuming, prone to errors, inefficient, and expensive for businesses that need to process large volumes of data regularly.
2. Rules/template-based extraction
The method first uses Optical Character Recognition (OCR) to convert images of text into machine-readable text. The OCR information is sent to the next steps of the pipeline. The next steps use hard-coded rules and workflows varying for each type of document. Both image and text-based patterns in each document type are used to write custom rules.
Straight Through Processing (STP), a metric used by Document AI companies can be defined as the percentage of documents processed/extracted without needing any manual human correction.
Rules/template-based extraction provides perfect STP for data extraction from structured documents. But it is not a reliable data extraction solution for semi-structured documents because different rules need to be written for different formats of different document types. Furthermore, these rules need to be updated even for minor changes to the structure. The documents may come from third-party sources, so their format is out of our organization’s control. Hence, they can be very diverse. For instance, the average mortgage application today exceeds 350 pages and over 60 major document types. This solution can’t deal with such variety and complexity of documents coming from diverse sources, and it struggles to provide consistency in the process.
3. AI/ML-based extraction
AI/ML-based extraction is used for automated Data extraction. This method also first uses Optical Character Recognition (OCR). Along with the text information, layout and style information is vital for document image understanding. Today with the advancement of Artificial Intelligence, more specifically the innovation of MultiModal learning for data extraction, we get highly accurate State of the Art (SOTA) results.
AI/ML Based extraction has made significant progress in the document AI area. This method makes it possible to extract data from documents with varying content and structure. It can deal with the variety and complexity of documents from diverse sources. It can further adapt to changing structures by finetuning or pretraining the model on the updated data structure. Hence, it can be a reliable data extraction solution for both semi-structured and unstructured documents.
Document AI companies have generic AI models for different document types like W2 forms, W9 forms, Accord forms, Bank Statements, Invoices, Financial Statements, Rent rolls, etc. For each document type, models are trained on a huge volume of data consisting of varying content and structure. We can simply use those models as they provide great accuracy and high STP. For even higher accuracy, we can easily finetune the model on our data.
Comparison of different methods:
Manual Data Entry
Manually entering data from a document into a computer or other system by typing or copy-pasting.
Simple and easy to use
Time-consuming, prone to errors, inefficient, and expensive
Rules / Template-Based Extraction
Using a predefined template or structure to extract data from a document type.
Can extract data from structured documents with good accuracy
Limited to extracting data from documents with a specific structure
AI/ML based Intelligent Extraction
Using ML and DL algorithms to recognize and extract data from documents.
Can extract data from structured and unstructured documents
Requires specialized software and may require pre-training or fine-tuning to work accurately
Suppose you need to extract data from a high volume of semi-structured and unstructured documents. In that case, your obvious choice will be to use AI/ML-based data extraction as it is more flexible and accurate than both the other methods. Let’s further discuss the advantages of ML-based extraction over rules/template-based extraction:
Handling unstructured data
Machine learning-based data extraction can handle unstructured data, which does not have a predefined format or structure. Rule/template-based extraction methods are typically limited to extracting data from structured documents with a specific format or layout.
Adapting to changing data
Machine learning-based data extraction can adapt to changing data over time. Even if the format or structure of the data changes drastically or new types of format are introduced, the extraction algorithms can be retrained or finetuned to continue extracting the data accurately. Rule/template-based extraction methods may need to be updated manually to handle changes in the format.
As we train or finetune the model on more data, Machine learning-based data extraction can become more accurate. This can result in more reliable data extraction than rules/template-based methods, which are prone to errors if the rules or templates do not accurately reflect the data.
Handling multiple languages
Since the model can be trained on documents of different languages, machine learning-based data extraction can handle multiple languages. Rules/template-based extraction methods may be limited to a single language or may require separate rules or templates for each language.
Overall, AI/ML based data extraction is more flexible, adaptable, and accurate than rule/template-based methods, making it a better tool for extracting data from various sources.
Data Extraction - Use-Cases and the call for automation
Data extraction is useful for businesses in many industries, including lending, insurance, commercial real estate (CRE), and logistics. Some examples of how data extraction is used in these industries include:-
The extracted data helps logistics companies track and manage shipments, identify cost savings opportunities, and improve their supply chain efficiency..
Data extraction facilitates companies to migrate data from documents, credentials, and images into their databases. This feature helps avoid having your data siloed by obsolete applications or software licenses. Let's have a look at some use cases of data extraction in different industries in detail:-
1. Commercial real estate data extraction
Real estate investors analyze historical sales data for a specific property and compare it with similar other properties on distinct parameters to estimate the investment potential. Most property managers extract this historical data from various document types and categorize them in a structured manner before comparison. However, manual extraction is susceptible to all kinds of errors, thus resulting in inaccurate data sets and erroneous estimates.
Perks of Automation
Automated data extraction helps you extract historical sales data from various non-standard property documents and streamline sales comparisons. You can process CRE Models in real-time and receive error-free reports.
You can extract standard fields such as property details, building details, as well as adjustment details with the convenience of adding, deleting, or moving any field.
2. Logistics document processing
Logistics service providers extract and analyze heaps of data from invoices, bills of ladings, as well as other documents, and manually feed in updates to the TMS or ERP. Commodity traders, shippers, food producers, and logistics providers are required to process hundreds of Bill of Lading documents every day. With this process being executed manually, it is prone to human errors and delays.
Perks of Automation
Automated data extraction software processes bill of lading and other logistics documents in real-time yielding over 99% accuracy.
Process shipping details, purchase details, as well as other additional information with the advantage of reduced cost, faster processing time, and error-free results.
3. Agreement parsing and rental application for property managers
As a property manager, you might have your desk or email inbox flooded with applications for properties that you manage. Weeding through all the paperwork to extract the core information that differs from application to application can get extremely tedious. Such credentials hold the utmost significance, and thus, the sensitive information must be handled scrupulously.
Perks of Automation
Automated data extraction provides you with the necessary data downloaded in Excel, XML, CSV, or JSON format, or use Salesforce and Google Sheets integrations.
Data extraction software pulls the differences from different rental applications and sends that information to precisely the place you need it.
4. Accounts payable processing
Today, a large number of invoices are sent in PDF format via fax or email. An individual manually inputs the data into their ERP platform, Excel sheets, or any preferred software program.
However, since enterprises send and receive thousands of invoices every day, it becomes unavoidable to have automated accounts payable solutions to alleviate the load of manual entry and make the payable workflow system quicker, boost accuracy, and make it error-free.
Perks of Automation
Automated data extraction locates and extracts the fine-grained data figures present inside the digital invoices. It also pulls intricate patterns such as invoice line items.
If a business gets bombarded with hundreds of invoices from various suppliers, then automated data extraction can help streamline these invoices in varied formats and deliver error-free reports.
Choose an automated data extraction solution that complies with your company's needs
When picking a data extraction solution for your business, you should be careful about different features that different platforms have to offer as something that might work for one company may not work for the other. Therefore, you must have the following parameters in mind when making a purchasing decision:-
1. Intelligent data capturing
The data extraction tool must be able to extract data without losing information from different document types such as contracts, delivery notes, accounts payable, and more, and be able to categorize them in their respective blueprints.
2. Accuracy in results
Companies prefer a data extraction tool that delivers swift results; however, it must also be high in terms of accuracy. The extracted output must retain information, and the tool must be able to extract tables, fonts, and crucial parameters without compromising the layout.
3. Storage options
Pick a data extraction platform that offers secure storage along with seamless backup options. Cloud-based extraction enables you to extract data from websites seamlessly at any time.
Cloud servers can swiftly extract data relative to a single computer. The quickness of automated web data extraction affects the speed of your reaction to any rapid events that impact your enterprise.
4. Simplistic UI and robust features
Advanced automated data extraction software must operate on a simplistic UI. The layout of the software interface at launch must be simple enough to navigate you through executing a grinding task. Besides providing an easy-to-use UI experience, the platform must also not compromise on the essential features.
Pricing might not be the most crucial factor, but it is a thoughtful consideration. It might not be a wise decision to invest in exorbitantly expensive software with extravagant features that do not apply to your company or choose the wrong pricing plan. Consider evaluating the features of the software while ensuring that the cost stays within your budget.
Most companies are sitting on a document goldmine. Companies in different industries have been using the information on these documents for their day-to-day business needs. The employees manually extract data from these documents, spending countless hours. Automated data extraction is faster than manual extraction, reduces processing costs, and increases employee efficiency.
Data extraction is a crucial process to automate structured data collection and use them for further analysis. If your business seeks to employ an automated data extraction solution in your system, make sure that it is capable enough to adapt to your use-case yielding a higher impact on the workflow.