Data Extraction: Explained and Automated!

Why is Data Extraction Crucial for Businesses?

As part of their business operations, companies receive and generate a variety of different documents. Customers, suppliers, and partners send documents containing vital information to the company. This information needs to be integrated into the company's database or decision-making system.

Data extraction takes place at the doorstep of the company. Unfortunately, this data is often unstructured or semi-structured, leading to the need for a back-office team to verify the information on the documents, such as the products listed on an order, and enter this information into digital systems.

This is a costly, time-consuming, and labor-intensive task, but it doesn't necessarily have to be that way. Contracts, orders, invoices, delivery notes, and more all land in the realm of input management, at the company's entrance. To remain competitive in the market, swift, high-quality, and efficient business operations are essential. Errors that occur in capturing information at the company's entrance result in increased costs in subsequent processes.

Incorrect processes are triggered, documents are misclassified, reports are corrected retroactively to make the data plausible, or, in the worst case, errors go entirely unnoticed. In addition, extensive forensic work is required to rectify the errors, leading to double costs. Revenue is reduced or delayed, and additional expenses accrue.

Therefore, efficient data extraction means finding the optimal balance between the accuracy of the captured data and the lowest possible error rate. Companies heavily reliant on automation measure capture errors consistently.

Modern systems often offer solutions here as well. In addition to automated extraction using Artificial Intelligence, AI can also assess the correctness of the extracted data itself and make a statement about its accuracy before involving a human.

Data Extraction: The Foundation for Successful Scaling

Every business model requires document processing as part of its operations. The more detailed or extensive the business, the more critical automated data processing, and thus data extraction becomes. In other words, the more documents that need to be captured for business operations, the greater the manual effort in administration. However, if you want to expand or scale your business, you can only achieve this through additional personnel, and this resource pool is finite.

Automation: Data Extraction vs. Processes

Automation means carrying out workflows without human interaction. Therefore, the approach of digital process automation is evident. However, the fruits of digital processes cannot be harvested without correct, structured data. Workflows are often triggered by the receipt of an unstructured document. Unfortunately, interpreting unstructured data (PDFs, images) from a technical perspective is one of the most challenging tasks. However, since the dawn of the AI era, this field has evolved rapidly, and the latest systems achieve much better results with less effort.

What Methods are Distinguished in Modern Data Extraction?

1. Manual Data Extraction: This is the simplest method, where human workers manually capture data from physical or digital documents. This can include tasks such as typing information from printed forms or copying and pasting text from websites into a database. This method is time-consuming and error-prone but is still used in many cases.
2. Optical Character Recognition (OCR): OCR software is used to extract text from images or scanned documents. This is especially useful when you need to convert information from printed material into digital formats. However, it does not consider semantic aspects. For data extraction from simple and recurring formats (e.g., forms where the fields are always in the same positions), this may be sufficient. A template mechanism can also be used to help automate recurring form structures (this was the most common approach to data extraction before the AI era).
3. Text Analysis and NLP (Natural Language Processing): This method uses machine learning and artificial intelligence to analyze text data (e.g., from OCR) and extract information. NLP can be used to extract relevant information from unstructured texts such as PDF documents, emails, social media, or customer reviews.
4. AI Image Processing: In some cases, images are analyzed to directly extract information. Artificial intelligence converts the image directly into the desired information. This is particularly applied where essential information is given by the context of the image. This method is preferred even when text is embedded in image data (e.g., a stop sign at an intersection). In research, there are also approaches to interpret images, including text, without the intermediate step of OCR. Such use cases are often found in medicine, engineering, or quality control.
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Modern Data Extraction with AI and OCR

Modern technology has brought about a revolutionary change in the world of data extraction. Artificial Intelligence (AI) and Optical Character Recognition (OCR) are two key components driving this development, enabling companies to extract data faster, more accurately, and more efficiently than ever before.

Artificial Intelligence (AI) and Its Role in Data Extraction

AI propels many technological innovations, and data extraction is no exception. AI-powered systems use complex algorithms and neural networks to transform unstructured data into structured information. Here are some ways AI is revolutionizing data extraction:

1. Automatic Classification: AI can automatically classify and categorize documents and files. This is especially useful when dealing with large volumes of documents, as it saves time and enhances organization.
2. Text Recognition and Extraction: AI-powered OCR technologies can extract printed text from images or scanned documents. This enables the conversion of paper documents into digital formats and the automatic extraction of information.
3. Intelligent Data Capture: AI can capture data from various sources, including unstructured texts like emails or social media. It can identify relevant information and structure it in a format suitable for analysis.
4. Error Reduction: By automating data extraction, human errors can be minimized. AI systems are precise and consistent, enhancing the quality of extracted data.
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Artificial Intelligence Harmonizes Your Data

One often overlooked point, but one that offers significant benefits, is that AI captures data consistently. Unstructured data often leaves room for interpretations by data capturers. For example, while data capturer A always looks in the footer of a document for the sender's VAT ID, data capturer B always accesses the header area. However, this is not always permissible and can later lead to significant inconsistencies in financial reporting that can only be rectified with considerable effort.

Data Extraction with BLU DELTA

The BLU DELTA AI platform uses Artificial Intelligence for data extraction. It incorporates state-of-the-art technologies from the field of NLP and image processing. Additionally, recognition can continuously learn through an automated training approach.

This enables immediate data extraction of more than 50 data fields from receipts and similar semi-structured documents.

If you'd like to learn more about data extraction with BLU DELTA AI, we look forward to hearing from you.