Some industries generate thousands of documents every day which have important information to be captured and can be used for analysis and other purposes. With the advent of Optical Character Recognition(OCR) software in the 1990s, it became quite easy to extract and store information from multiple files and sources reducing the manual efforts of the industries.
As technology has advanced over the years, the focus has shifted to Intelligent Data Extraction which involves not just capturing the data from the sources but carefully analyzing it and creating meaning out of it.
With the help of Intelligent Data Extraction, text can be extracted from digital assets like documents, emails, text files, scanned images, and structured and unstructured data, and made available in usable formats with the help of defined rules and data extraction templates.
What is Intelligent Data Extraction?
Intelligent Data Extraction refers to the process of automatically extracting structured data from unstructured or semi-structured data sources such as text, images, or audio files, using machine learning algorithms, natural language processing (NLP), and other AI techniques. It involves analyzing large volumes of data to identify patterns and extract meaningful insights that can be used to inform business decisions.
Traditional OCR Vs Intelligent Data Extraction
Intelligent data extraction differs from traditional data extraction methods in that it utilizes machine learning algorithms and NLP to extract structured data from unstructured or semi-structured sources. Other data extraction methods may rely on rule-based approaches or manual data entry, which can be time-consuming, error-prone, and less accurate.
For example, traditional data extraction methods might involve manual data entry of information from paper documents or manual entry of data into a database. This process can be tedious, time-consuming, and error-prone, especially when dealing with large volumes of data.
In contrast, intelligent data extraction uses advanced algorithms to automatically identify and extract data from unstructured or semi-structured sources, such as invoices or contracts. These algorithms can learn from past examples, improving accuracy over time, and can handle a wide variety of document formats and languages.
Overall, intelligent data extraction is faster, more accurate, and more efficient than traditional data extraction methods, making it a powerful tool for organizations looking to extract insights from large volumes of data.
How Does Intelligent Data Extraction Work?
Intelligent data extraction works by using advanced algorithms and machine learning techniques to identify and extract data from unstructured or semi-structured sources, such as invoices, contracts, or emails. Here are the steps involved in the intelligent data extraction process:
Data Ingestion:
The first step in the intelligent data extraction process is to ingest the unstructured or semi-structured data into the system. This data can come from a variety of sources, such as email attachments, scanned documents, or uploaded files.
Pre-Processing:
The data is then pre-processed to prepare it for extraction. This may involve cleaning up the data, converting it into a standard format, or segmenting the data into different fields.
Training the Algorithm:
The next step is to train the algorithm to recognize specific data fields. This involves providing the system with examples of the data fields that need to be extracted and the locations where they can be found in the document.
Extraction:
Once the algorithm has been trained, it can be used to extract data from unstructured or semi-structured sources. The algorithm analyzes the data and identifies patterns and features that match the trained examples. The extracted data is then outputted in a structured format, such as a CSV file or a database.
Validation:
After extraction, the system checks the accuracy of the extracted data. If the data does not meet the required accuracy level, it is sent back for reprocessing.
Continuous Improvement:
Over time, the algorithm can learn from its mistakes and improve its accuracy. This is done by feeding back the extracted data into the system and using it to retrain the algorithm.
Benefits of Intelligent Data Extraction for Industries
Intelligent data extraction provides several benefits to organizations, including:
#1. Increased Efficiency:
By automating the data extraction process, organizations can save time and increase efficiency. This is because intelligent data extraction can quickly and accurately extract data from unstructured or semi-structured sources, such as invoices or contracts, reducing the need for manual data entry.
#2. Improved Accuracy:
Intelligent data extraction uses advanced algorithms and machine learning techniques to extract data from documents. This leads to higher accuracy rates than traditional data extraction methods, which rely on manual data entry.
#3. Reduced Errors:
By reducing the need for manual data entry, intelligent data extraction can also reduce errors. This is because manual data entry can be prone to mistakes, such as typos or incorrect data entry. With intelligent data extraction, the risk of these errors is significantly reduced.
#4. Cost Savings:
By automating the data extraction process, organizations can reduce costs associated with manual data entry, such as labor costs. Additionally, intelligent data extraction can help identify cost savings opportunities by analyzing data to identify inefficiencies and areas for improvement.
#4. Improved Decision Making:
By providing accurate and timely data insights, intelligent data extraction can help organizations make better-informed decisions. This is because organizations can analyze the data extracted by intelligent data extraction to identify trends and patterns, leading to insights that can inform strategic decisions.
Overall, intelligent data extraction is a powerful tool for organizations looking to extract valuable insights from large volumes of data. Improving efficiency, accuracy, and decision-making, it can help organizations save time and money while gaining a competitive advantage.
Applications of Intelligent Data Extraction in Different Industries
There is a range of industries that can utilize the benefits of intelligent data extraction and can upscale with the help of optimized data extraction processes. Some of the industrial applications of Intelligent data extraction and processing are listed below:
Healthcare:
It is one of the industries that has a heavy reliance on data and generates thousands of documents every day. As the EHR (Electronic health records) and EMR (Electronic medical records) are becoming much more important in the context of keeping the health records of the patients, data extraction using artificial intelligence can be of prime importance in deciphering the patient medical records and making them available instantly with the help of intelligent document processing. It can help in providing personalized care to the patients by providing immediate access to the health records of the patient to the specialists.
In addition, the EMR/EHR data can be handy for insurance claim assessment and during healthcare insurance litigation.
Legal Service Providers
The legal industry is document-driven and generates a deluge of documents like litigation filing, first information reports, documents about mergers and acquisitions, articles of association, previous court orders, various kinds of agreements/ contracts and other documents of importance. Storing and retrieving this information can be a tedious process considering the number of documents that arrive every day.
Using Intelligent data extraction for extracting the information can be highly valuable as it can minimize the errors and discrepancies that cause greater trouble in legal work.
Supply Chain Management
The supply chain industry typically involved in procuring and industry buying faces the challenges of invoice processing and purchase order maintenance as the documents can be in multiple formats with hard-to-read texts. A lot of human hours are utilized in deciphering the semi-structured documents and feeding them to the ERPs. There are chances of human errors in document processing leading to delays in payments and low-quality work as well.
Using Intelligent Data Extraction with OCR can aid in capturing the data from the invoices without much human interference and further assist in purchase order automation. The processes can be streamlined and the human hours saved can be used for other productive purposes.
Accounting and Taxation
Most of the tax work and accounting practices still rely heavily on documents and paperwork. A large number of documents lead to less productivity and reduced efficiency of workers causing more errors in documents processing. The department handles documents like invoices, bills, account receivables, payment information, and export-import details as well. Errors in processing such documents create a risk of late payments and hamper the relationship with the clients.
During the end of financial years, the chances of errors, workload and the associated cost of mistakes become even more critical with the added burden of tax and GST returns filing. The advanced technology of Data extraction can be used by the accountants to automatically process the documents for invoice data extraction thus reducing the errors. The advanced receipt data extraction further optimizes the process and helps in storing the payment records safely.
Banking & Finance
BFSI firms are moving towards digital document processing and utilizing the benefits of paperless work but some departments use physical paperwork and require constant checks and audits to maintain the quality of work. There is a constant inflow and outflow of invoices and purchase orders from the vendors that are to be entered into the system and with the help of Intelligent data extraction technology, the system can be channelized for maximum output and minimum errors.
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Intelligence data extraction is an advanced technology that can reduce the workload of industries that are heavily reliant on paperwork and spend a large number of hours processing documents. With IDE in place, industries can focus on better opportunities in creating streamlined and optimized work processes.
KearStack is a technology leader and has developed advanced solutions for invoice and payment orders automation and is helping industries leverage the power of intelligent data extraction with OCR.
Future of intelligent data extraction and how it is likely to evolve over the next few years
The future of intelligent data extraction looks very promising, as advancements in artificial intelligence, machine learning, and natural language processing continue to drive innovation in the field.
Here are some of the ways that intelligent data extraction is likely to evolve over the next few years:
#1. Increased Automation:
As the technology continues to mature, intelligent data extraction systems will become even more automated, requiring less human intervention to train algorithms and validate results.
#2. Improved Accuracy:
Machine learning algorithms will continue to improve, leading to higher accuracy rates for data extraction. This will enable businesses to rely on intelligent data extraction for more critical tasks, such as financial reporting or compliance.
#3. Expansion of Use Cases:
As the technology becomes more widespread and accessible, businesses will begin to explore new use cases for intelligent data extraction, such as predictive analytics, fraud detection, or customer experience analysis.
#4. Integration with Other Technologies:
Intelligent data extraction will increasingly be integrated with other technologies, such as robotic process automation (RPA), chatbots, and virtual assistants, to provide more seamless and intuitive experiences for users.
#5. Cloud-based Solutions:
Cloud-based solutions for intelligent data extraction will become more common, making the technology more accessible and affordable for businesses of all sizes.
#6. Improved User Interfaces:
As the technology becomes more user-friendly, non-technical users will be able to use intelligent data extraction tools to extract insights from unstructured data sources.
Overall, the future of intelligent data extraction looks very promising, with the potential to revolutionize the way that businesses extract insights from unstructured data sources. As the technology continues to evolve, businesses that embrace intelligent data extraction will be well-positioned to gain a competitive advantage and drive innovation in their industries.
Conclusion
Intelligent data extraction can help businesses like finance, banking, and legal with loads of paperwork and invoices to streamline their processes and save resources on manual invoicing.
KlearStack’s artificial intelligence-led solutions are created to solve the challenges of the industry and equip them to undertake a technological leap to upgrade the invoicing process and reap the benefits of the highly productive system.
