What is Data Extraction? Definition, Process & Tools
If your organization is struggling to manage all the information that it receives, you are not alone.
The volume of information that comes into most organizations is soaring. Making matters worse, information is arriving faster, through myriad entry points, and in a variety of formats and file types. The typical organization might need to capture information from scanned documents, PDFs, active PDF forms, Excel spreadsheets, PowerPoint presentations, Word documents, e-forms, and emails.
On top of it all, the information that organizations receive is more complex. According to the Association for Intelligent Information Management (AIIM), 63 percent of the information that the typical organization receives is unstructured and mostly unmanaged. As a result, many organizations cannot identify basic metadata such as the owner of a document or the version of a document.
Without clear visibility into metadata, document retention and disposition are major challenges.
A lack of visibility into critical information also complicates mission-critical processes across the corporate enterprise, including customer correspondence, sales proposals and contracts, case management, sourcing, procurement and contracting, research and development, manufacturing and warehousing, human resources (HR), finance and accounting, and customer service help desk.
The combination of unrelenting pressure to reduce costs, ever-increasing legal and regulatory requirements, and heightened service expectations have made data capture a strategic priority.
Data extraction brings order to the information chaos. Read on to learn how.
What is Data Extraction?
Data extraction captures data from any paper or electronic source for processing or storage.
A big step above antiquated scanning and optical character recognition (OCR) systems, modern data extraction solutions are powered by AI that uses supervised machine learning to intelligently classify and extract metadata from structured and unstructured documents, providing clarity and speed.
The information captured by these solutions is most used for:
- Document classification
- Text data capture
- Routing to processes
- Indexing for archival
- Indexing for retrieval
It is a key component of an information management strategy.
How Does Data Extraction Work?
Here’s how it works:
- Input. Information is input from scanners, email attachments, mobile devices, faxes, image repositories, and stored information.
- Classify. Data extraction solutions classify and understand documents using context and unique words, titles, key anchors, values, patterns, and barcodes.
- Extract. The technology extracts information using a wide variety of technologies, including freeform unstructured extraction, barcode extraction, handwriting extraction, mark sense extraction, “fuzzy” database extraction, PDF and text extraction, and fixed-form extraction.
- Deliver. Information is delivered to downstream systems in any format, including XML, PDF, CSV, JSON, document repositories, or application programming interfaces (APIs).
Some document scanners use built-in data extraction technology to process co-mingled documents, capture data during scanning, out-sort exceptions, and speed document setup. Extracting data during document scanning also eliminates the need to manually index document types and metadata.
Common Uses & Advantages
While data extraction software can streamline any information management application, the technology is ideal for semi-structured and unstructured forms such as invoices, mortgage and tax documents, loan packages, insurance applications, healthcare insurance forms, and medical records.
Consider the back office of a typical bank. The information management team must process credit card applications, online account opening forms, mortgage origination files, property titles and other lending documents, checks, invoices, and remittance documents. Most banks rely on a hodgepodge of standalone systems to capture data from these documents, resulting in inefficient, costly processes.
Mailrooms can use this software to bring order to the ever-increasing volume of documents that they receive. The technology standardizes, centralizes, and automates the classification and batching of documents, the capture of header and line-item data, and digitally export information to downstream systems and processes based on pre-configured business rules.
In all these applications, the solutions capture information at the point of presentment.
What’s more, best-in-class data extraction solutions provide a single platform for capturing data from paper and digital documents. While the volume of paper documents is declining in many segments – such as check processing and explanation of benefits processing – there’s no telling when, or if, paper will disappear. In the meantime, organizations must find efficient ways of operating in a hybrid information management environment, while meeting legal and regulatory requirements.
How an Organization Can Benefit
Data extraction fundamentally changes the way that organizations capture data.
- Reduced costs. Data extraction reduces the need to manually key information that comes into an organization, including unstructured documents such as invoices. Metadata is captured with high accuracy and machine learning improves performance over time. And using a single platform to capture all data enables organizations to consolidate systems.
- Improved staff productivity. All the time that employees waste keying data, shuffling paper and emails, and fixing errors and mistakes is time that they cannot spend on fulfilling, higher-value activities such as analyzing data and collaborating with key stakeholders. Automating processes with data extraction frees staff to focus more time on the things that matter most.
- Improved accuracy. A single typo can create big headaches downstream. Data extraction solutions use AI to capture information with a high degree of accuracy. And captured data can be validated against information residing in an ERP application or other system of record.
- Accelerated cycle times. Leading data capture solutions use robotic process automation (RPA) to seamlessly export data to downstream systems without the need for programming. And extracting data at the point of presentment means documents can be routed faster.
- Streamlined compliance. Data extraction solution track all actions taken on a document. And improved metadata visibility ensures compliance with document retention schedules.
- Greater scalability. Data extraction solutions enable organizations to efficiently scale their operations, without the need to hire additional staff as their volume grows or needs change.
These are some of the reasons that more organizations are deploying data extraction solutions.
Elevate Your Data Capture
In this digital age, information is coming at organizations from a million different directions, in a variety of structured and unstructured formats. Manual indexing and tagging are slow, error-prone, and require too many resources. And antiquated OCR systems don’t deliver the speed or the results that organizations need. Best-in-class data extraction solutions enable organizations to recognize incoming information and extract actionable insights that will drive the business forward.
To learn more, consult one of our data extraction solution experts.