Different technologies are used: depending on the data basis – be it a database, a PDF or a Word document, etc. – and depending on the goal and focus.
Definition of data extraction
In colloquial language, extracting data can be described as reading it out.
Data extraction consists of three main steps.
- Extraction of the data,
- Consolidation (standardization)
- Data provision (data storage)
Problem: The only thing standing in the way of companies is the various media and formats from which data is to be extracted.
on the reef of unstructured documents
An example: PDFs and Office documents (Word, Excel, PowerPoint files) differ in structure and are unstructured. This means that as a pearl diver, we cannot rely on any structure here if we were to read these documents automatically.
3 ways to extract data
There are three different approaches available to us on the way to the data:
- Manually: Someone looks at the database. However, this is time-consuming and error-prone, even with more than five documents.
- Automated: Several documents are passed to a program routine. For example, a Word document is converted into a PDF, including OCR (Optical Character Recognition). Another routine can then automatically extract the recognized text elements.
- Human-in-the-loop: After data extraction, a human often checks the extracted data: whenever program routines deliver uncertain results and communicate this to the person checking the data.
Advantages of automated data extraction
Manual data extraction is time-consuming and tedious. Companies want to save time and money, and automated extraction offers great advantages here. At the same time, this extraction process relieves employees of tedious tasks.
- Increased efficiency: Automated data extraction speeds up the process and reduces manual intervention. This leads to faster processing.
- Accuracy: Modern solutions use artificial intelligence (AI) and machine learning to increase the accuracy of the extracted data and minimize errors.
- Cost reduction: Through automation, companies can save costs that would otherwise be incurred for manual data entry.
Application examples in Enterprise Content Management
- Invoice processing: Automated data extraction plays a decisive role in incoming invoice processing in document entry. Incoming invoices are scanned and the relevant data such as invoice number, date, amount and supplier are automatically extracted and processed as a voucher. This was the standard procedure for a long time. With the e-invoice, this has changed: here, only the XML of the invoice needs to be read – and the required data is available. The e-invoice process therefore dispenses with scan-in and OCR. This enables fast and error-free processing of invoices, which leads to faster payment processing and better control over finances.
- Contract management: Here, extraction helps to record important contract data such as contracting parties, terms, notice periods and payment terms. This information is extracted from the contracts and transferred to a central contract management system. This considerably simplifies the management and tracking of contracts, resulting in better compliance with deadlines and more efficient administration.
Challenges and solutions
Data extraction faces a number of challenges, but these can be overcome with the right approaches and technologies.
Diversity of data sources
- Challenge: Different data sources such as databases, PDFs and Office documents make standardized extraction and processing difficult.
- Solution: Flexible data extraction tools that support different formats and sources can help. The use of AI and machine learning in particular makes it possible to adapt to different data structures and simplify extraction.
Data complexity
- Challenge: Unstructured data, such as handwritten notes or complex tables, pose a particular challenge for information extraction.
- Solution approach: The use of advanced OCR (Optical Character Recognition) technologies and Natural Language Processing (NLP) can help to extract and process even complex and unstructured data.
Error handling and monitoring
- Challenge: Errors in information extraction often lead to inaccurate or incomplete data. This impairs decision-making.
- Solution: Implement monitoring and error handling mechanisms that automatically highlight issues and initiate corrective actions. Human-in-the-loop approaches can also help to increase accuracy.
Conclusion
For information pearl divers, data extraction is an essential process that helps companies gain valuable information from various sources. By automating this process, efficiency, accuracy and cost savings can be significantly increased. Despite the challenges, such as the variety of data sources and the complexity of the data, modern technologies and flexible tools offer effective solutions. Overall, data extraction enables faster and more accurate data processing, leading to better decisions and optimized business processes.