Automated data extraction – Everything you need to know

Data extraction is one of the fundamental functions of any organization. Previously, enterprises dedicated one or two resources to execute simple yet intricate data mining and extraction tasks. Manual data entry and extraction is time-consuming, prone to human errors, and affects productivity which is costly for the enterprise. Adding to that is the hard-to-read information, which, together, become the perfect recipe for errors.

Due to the rising digital transformation needs, data and analytics have become central to every operation. The demand for quality data is rising, supporting informed decisions for competitive advantage. Hence, the compulsion to induce automated data extraction solutions for simplifying the process and making it error-free.

Automated data extraction – A step beyond the legacy approach

Did you know that 80-90% of enterprise data is unstructured? It’s no wonder that data is essential for any business organization to scale growth. But raw data is usually presented in an unstructured or semi-structured format, which serves no value unless insights are extracted from those datasets.

Enterprises struggle when they have to collate data from multi-sources. That can be emails, PDFs, images, invoices, paper files, contracts, financial statements, etc. Every piece of information shared in these documents is valuable enterprise data. But they are valued only when such information is extracted, processed, analyzed, and made available in real-time. This task is beyond the capabilities of an average human being. Completing the process at record speed is borderline unrealistic, regardless of how efficient they might be.

Automated solutions are designed to reduce the time, labor, and error factors and speed up the process so quality and accurate data are available as and when needed. Data extraction automation is a tech-enabled solution that can benefit organizations in numerous ways. Given the growing market competition, AI-based data extraction and processing platforms have become the need of the hour.

Role of documents in business processes

Now, enterprise documents do not necessarily mean paper-based files. Documents can include invoices, emails, email attachments, PDFs, and even images or essential chats about work shared between team members. They function like physical or tangible evidence of what has been passed between two parties. And these documents are a treasure chest of data in raw form. Such information supports all kinds of decisions and strategies made to improve the operations and objectives of companies. Hence, any form of a physical or digital document is essential, whether it is a simple image or pages of a contract.

A significant factor is not the type of document shared but its contents. However, the main challenge arises when such documents are digitized and information extracted manually. Then, the volume of documents becomes a massive challenge, and the chances of a few documents getting misplaced or lost are always high when handled manually. The risks are more apparent with paper-based documents because these files do not have any backup. Therefore, the crucial data present in them are lost forever. Though a few can be retrieved, the whole process of back-and-forth communication would eventually delay the subsequent processes that were dependent on the same data.

Data extraction with AI and Automation ensures any information or document entering or leaving the organizational system has a backup. That reduces the extra effort and loss of time associated with the document or data retrieval process. It further ensures the regular workflow doesn’t get hampered or delayed.

Significance of automated data extraction

To keep up with the fast-paced business world, enterprises should rethink how to manage data. As stated earlier, enterprise data is fundamental in every operation and strategy-building. The significance of such platforms is immense, considering the fact that most of these software solutions support multi-data formats with user-friendly interfaces that are compatible with several enterprise applications. With advanced capabilities, automated data extraction tools can evaluate documents and extract and analyze data at record speed, delivering accurate results devoid of error or human biases.

So, when enterprises need market trends in real-time to forecast demand accurately, the correct data is available at their fingertips. And if any variations occur in the meantime, the AI/ML tools can process and generate insights covering all possible conditions. Hence, enterprises are always ready with their plan and contingencies to stay ahead of the market and competition.

Current trends in automated data extraction

Although automated data extraction has been one of the biggest game-changers of the current, digitally transforming market, still the processes are confronting several challenges due to the exponentially mounting data and documents. The mismatch in the data intake and analytic output remains high because of disparate systems and existing siloes. The complexity of document types and formats also works as a major hindrance, causing a massive amount of data to stay dormant.

This, in turn, impacts enterprises’ understanding of the data extracted, leading to poor or inefficient decisions. Basic AI and Automation tools might just fall short of keeping up with enterprise data complexities. However, with advancements made in the same field, the situation is fast changing.

Sophisticated data management techniques combining AI and ML technologies convert enterprise documents (files, images, emails, bank statements, etc.) into standardized, machine-readable text. Embedded Machine Learning capabilities allow the platform to learn from patterns by continually refining datasets. A more comprehensive analytics platform uses tools like Natural Language Processing and Computer Vision to understand human language as it is, interpret meaning like humans, and capture more strategic activities from broader interaction. Hence, the resultant insights are cleaner and more accurate. In addition, voice recognition tools are leveraged to convert speech/audio files into data.

Hence, advanced data extraction automation tools are available for enterprises to derive more value from their enterprise documents.

How to automate data extraction?

Data extraction from documents is an intricate process involving mining documents of raw data for further analysis.
In order to do so, extraction tools import documents into the digital platform of choice to create digital versions of the same documents and scan and capture the required data. Data collected are usually stored in shared cloud storage, allowing easy retrieval as and when needed. Furthermore, data is easily uploaded and annotated, wherein the AI models are trained for more accurate data understanding.

There are two types of data extraction, depending on the kind of data one is looking for.

Incremental extraction: This involves applying complex logic to account for shifts in datasets. And this function requires adding timestamps to datasets. For example, this application helps track inventory changes since the last extraction.

Complete extraction: This implies data extraction from its source without adding variables. Of course, it does require baseline information for the tool to search for similar patterns to refine the extracted datasets. Some auto-data extraction tools contain added mechanisms to update users about possible changes in the data after the previous extraction. In such instances, incremental extraction is not needed at all.

Typically, an automated data extraction software solution follows three stages, namely:

Data extraction automation challenges and remedies

Automated data extraction faces one primary challenge, i.e., extracting data from various document types. The context and structure of such documents differ significantly. Basic OCR tools are incapable of extracting information from unstructured documents, and most enterprise documents are unstructured. Further, in structured or semi-structured documents, the layout structure varies. Then, there are visually-rich documents to handle, where the layout and images contain crucial data associated with understanding the whole context of the document.

Thankfully, advanced technologies such as Computer Vision help override these challenges. These tools collect relevant information regardless of their position in the document, be it in the form of words or images.

Another challenge worrying enterprises with automated data extraction tools is data security. For example, enterprise documents might carry financial statistics or confidential information about clients or partners. These sensitive data should be protected with the help of a robust security infrastructure and technical assistance team. Document AI platforms like XtractEdge respect the privacy concerns of enterprises while helping them harness the full potential of automation.

Automated data extraction – Benefits and use cases

Data extraction automation benefits enterprises immensely. Here is a list of different ways such automation tools can drive value for your business.

As mentioned before, automation has a far-reaching impact on all organizations, regardless of their sectors and industries.

KYC and customer onboarding: Processing customer documents for KYC validation is a time-intensive task. With AI-enabled tools, the classification, capture, and processing of documents occur in lesser time, resulting in faster customer onboarding.

LIBOR transition solution: In order to ensure a smooth LIBOR transition for financial institutions, automated document review, and data extraction tools are effectively leveraged. These tools identify parties, risks, clauses, trigger events, fallback language, and LIBOR exposure to take necessary remediation action.

Claims and EOB processing: These tools help with unstructured data classification and extraction from emails, claim forms, carrier custom forms, explanation of benefits (EoB), and supplementary documents with high accuracy.

Invoice processing: Processing invoices is a recurring task central to every business organization. Automated capture and extraction of invoices free up processing time by thousands of hours.

Faster customer service: Contact center agents need immediate access to customer data to provide personalized service. With AI data extraction tools, the required insights are made available as and when needed, thereby crafting incredible customer experiences.

Conclusion

The future of automated data extraction is more promising as new AI capabilities are discovered to make the process more streamlined and faster. As mentioned earlier, data is integral to every business process; their availability on time can improve business strategies while resources are better utilized to gain maximum value. Moreover, nearly every industry faces data management challenges. Hence, automation and AI solutions for data have broader use cases, from manufacturing to healthcare, banking to retail, and so on.