Enterprise documents and emails are treasury-chest for crucial information enabling data-driven game-changing business decisions. Unfortunately, most of the data generated in these sources are unstructured or semi-structured. Therefore, manually extracting them is not feasible as the process is time-intensive and error-prone. Thus, it becomes a barrier to automating business processes dealing with such data-heavy documents, consequently resulting in resource overuse. Therefore, document data extraction using advanced technologies like AI and OCR are extensively leveraged to unlock unlimited business opportunities for the stakeholders.
Key Challenges of Document Data Extraction
Organizations deal with a colossal number of documents regularly, in the shape of invoices, contracts, emails and file attachments, chats, images, videos, and even audio clips. These valuable sources carry essential insights related to the company and its stakeholders. However, manually identifying the respective sources and extracting related data is humongous. Hence, resources connected with document data extraction confront varying challenges, a cruel reality of their professional lives. Some of the common challenges related to data extraction would be:
Document complexity: Complex document layouts and varying templates, intricate elements like tables, signatures, handwritten text, and non-textual content such as images and logos make data scanning an arduous endeavor.
Domain specificity: Different enterprises have different document domains and types for storing business-related information. For example, bills, loan applications, tax forms, invoices, and so on follow other formats. Hence, digitizing documents for storage in a centralized repository and extracting granular data is time and labor-intensive.
Disjoined approach: A document data extraction solution is typically disjointed; hence, assembling data using manual methods only adds to enterprise document problems.
Significance of Automation and AI in Data Extraction
According to market research experts, data-driven companies are 23 times more likely to add customers, 19 times more likely to increase business profit, and experience 7seventimes faster growth than global GDP. But data locked in complex enterprise documents can easily make this a distant dream. As a result, incredible business opportunities lay untouched and unused. And, if we quantify the number of documents entering an organization’s system daily, we might be surprised to see oceans of data, each driving value for the business. For instance, a company ideally receives around 129 billion business emails per day, with attachments in the shape of PDFs, spreadsheets, context, images, and even video clips. Business emails are also a powerhouse of information waiting to be optimized and leveraged.
Undoubtedly, such documents hold so much value for businesses. But, in the absence of tech-enabled intelligent solutions, much of this unstructured data remains hidden. Even when manual efforts are put to unearth them, extracting empirical information is humanly impossible.
Manually extracting data is challenging, as discussed above. Therefore, automation, AI, and extended capabilities are searched to simplify the entire process and override document data extraction challenges.
Automation and AI can efficiently perform the task in less time, reaching tiny corners to pull out minor data and analyzing them for further use. As a result, AI-powered software solutions help enterprises tap into the hidden value in documents and scale unlimited profitable opportunities for their organizations.
What is Automated Document Data Extraction?
Automated data extraction from documents translates into an optimized approach to converting unstructured/semi-structured data into structured information. Structured data is machine-ready, providing meaningful insights for reporting, analytics, and other business purposes. With reliable and intelligent software solutions, quality data is retrieved in real-time from documents of varying formats and sources of origin.
Benefits of Automation for Document Data Extraction
There is neither a silver bullet to solve every document-related challenge nor a one-size-fits-all solution. However, customizable intelligent document processing platforms leverage the power of automation and AI-related capabilities to streamline document-related workflows, regardless of industry-specific complexities. As a result, document data extraction becomes faster and more accurate eventually. The following is a list of primary benefits obtained from document data extraction tools:
- Extracting and interpreting data from documents comprising handwritten texts, signatures, and other elements.
- Quickly obtaining data from semi-structured documents featuring tables, numbers, and other variables.
- Analyzing data for empirical insights and sharing inputs in real-time for informed decision-making.
- Substituting human efforts and consequent errors to elevate extracted data quality.
- Ensuring data security and compliance for organizations.
- Improving searchability of critical terms and phrases from a document repository.
- Handling big data at scale and giving access to insights as needed.
How does Document Data Extraction Improve Accuracy and Speed?
As stated earlier, document data extraction is a time-intensive laborious task. With the bulk of documents entering the organization’s system daily, it becomes nearly impossible for employees to keep track of all information. Hence, extracting those data when needed only delays the process. For instance, information shared in the lengthy email trail consumes much time during extraction. And, manually, data extraction is subjected to errors, loss, or data damage. More often than not, granular insights remain invisible to the naked eye. Adding to this is the humongous task of digitizing and storing essential documents and updating the centralized system with newly shared information. Overall, the document management workflow and data extraction tasks hamper the speed of operations, delay decision-making, and damage the quality of data extracted.
Keeping these challenges in mind, document data extraction platforms leverage cutting-edge technology like automation, AI, and their extended capabilities to address time- and labor-intensive factors effectively.
Intelligent document data extraction benefits enterprises in the following ways:
- Eliminate human errors
- Improve operation efficiency
- Elevate employee productivity
- Optimize cost and resources
Ten Document Data Extraction Best Practices
Revise architecture for data management: Evaluate existing architecture to ensure seamless integration of future enterprise objectives and new data extraction capabilities.
Build a single database: A data repository supports a seamless data extraction process, offering a single source for enterprise information and eliminating existing siloes.
Map datasets: Gain a clear and concise idea about the types of data stored by various departments, their locations across the network, data age, and format.
Democratize data: Data democratization provides the proper intel to the right person at the right time, thereby building data trust and supporting stellar strategies for higher outcomes.
Invest in technology: Enterprises should invest in innovative technology like AI and Automation to bridge the data gap and effectively utilize the information for competitive advantage.
Data governance: Data accountability or data governance ensures the availability, usability, integrity, and security of the data in enterprise systems.
Build a robust security infrastructure: This ensures the safety and security of stored data and maintains the confidentiality of sensitive information.
Test for sensitive information: Test datasets for sensitive information to ensure compliance with industry standards and regulatory requirements such as HIPAA and GDPR.
Enhance data quality: Validate and add external information to the process using AI-powered tools to improve data quality and accuracy.
Archive data: Maintain historical records of important information and store them digitally for longer and easy access.
The Final Takeaway Document data extraction solution is highly beneficial to enterprises, as demonstrated above. In addition, the aforementioned best practices for data extraction improve the quality and accuracy of information and scale workflows for higher efficiency and profits.