By 2025, experts predict that we will produce 463 exabytes of data daily. Data generation in increasing volumes continues to inundate the data landscape without any sign of slowing down.
However, with enterprises receiving high volumes of unstructured data, traditional data extraction methods are no longer feasible. Instead, using AI to extract unstructured data is a necessity.
This article will cover the difference between structured and unstructured data, how AI extracts unstructured data from PDFs, and the benefits and features to consider in a top-tier data extraction solution.
Structured data means it is systematically organised into predefined structures, facilitating retrieval through databases.
Unstructured data, on the other hand, lacks a predetermined arrangement, which makes applying traditional querying and analysis methods challenging. Unstructured data can manifest in diverse formats, including images, videos and audio files.
Why Does This Matter for Enterprises?
Global enterprises are always looking for innovative ways to unlock insights from the quantities of unstructured data they receive. Using automated AI to capture the relevant information they need is one of the most effective strategies.
Case in point, according to a survey by the Intelligent Automation Network, 50% of RPA and digital transformation leaders use intelligent document automation to manage unstructured data. As for the future, a further 42% have it on their radar.
Modern AI-powered automation solutions deploy deep learning, natural language processing and Optical Character Recognition (OCR) to achieve a slick, intuitive mode of information capture.
Let's consider invoices as an example. While invoices often contain unstructured financial data, certain sections might conform to predefined structures, such as line items and totals. As a result, conventional extraction technologies might struggle to process invoices in diverse formats.
Automated data extraction from invoices follows a straightforward, step-by-step process:
The user uploads the invoice PDF.
The AI data extraction technology identifies and extracts pertinent fields (i.e., invoice or order number, VAT total, etc.).
Within a few seconds, the invoice is converted from an unstructured PDF into an Excel, CSV or JSON file. The data is then normalised and deposited into the chosen repository.
The technology underpinning invoice extraction is multifaceted. OCR facilitates the conversion of the PDF into machine-readable text. Subsequently, natural language processing contextualises the content, pinpointing required data. Incorporating deep learning empowers the machine to learn from its errors.
Speed & Efficiency - Time is money, especially when it comes to financial documents. Intelligent data extraction can confer a significant advantage in AI loan underwriting.
For instance, business lenders can now instantaneously approve or decline loan applications, navigating intricate due diligence processes. Across multiple industries, automated data extraction from PDFs speeds up and simplifies operations and workflow.
Economy - Some employers may believe that manual data extraction only costs as much as the twenty minutes or so it takes for their employees to capture the data from a PDF file. Those twenty minutes, however, represent the lost potential of high-value activities and costly human errors.
Automated data capture works in the background to instantly and accurately output structured data. Additionally, most AI data extraction providers price their documents per page, rendering it a transparent, low-cost solution for enterprises and small businesses alike.
Selecting a provider that continues to develop its technology is essential.
Every month, AI technology is taking massive strides in its ability to generate, extract and analyse unstructured data. Consequently, a vendor should take a research-driven approach to avoid obsolescence.
It is important to be realistic about how long it will take to train the model for your enterprise’s needs. For example, if extracting data from bank statements, the model won’t likely require training. For unique and complex documents, the AI model will need to process examples before it can seamlessly capture data.
AI-powered data extraction technology continues to lead the charge with diverse applications. Let’s zero in on using automation for financial statements.
Financial statements are technically-structured documents that typically adhere to accounting regulations and standards. However, they also can contain disclosures and notes that prevent traditional data extraction tools from effective capture. AI can extract from financial statements, matching or even surpassing human capacity.
Financial reporting is another area where automated data extraction from PDF files is in high demand. Most AI data extraction technology is capable of supporting post-processing rules. For example, fraud detection might flag changes in the formatting of the PDF to indicate tampering. However, integrating more intricate technologies (i.e. sophisticated automated analysis) into legacy company architecture may present challenges.
Automatic data extraction from PDF files has come a long way in a short time. AI technology can effectively filter, normalise and output unstructured data — effectively converting unstructured data into structured data.
To learn more about the applications of our offering, we invite you to book a demo with Evolution AI or contact our team at hello@evolution.ai.
By 2025, experts predict that we will produce 463 exabytes of data daily. Data generation in increasing volumes continues to inundate the data landscape without any sign of slowing down.
However, with enterprises receiving high volumes of unstructured data, traditional data extraction methods are no longer feasible. Instead, using artificial intelligence to extract unstructured data is a necessity.
This article will cover the difference between structured and unstructured data, how AI extracts unstructured data from PDFs, and the benefits and features to consider in a top-tier data extraction solution.
Structured data means it is systematically organised into predefined structures, facilitating retrieval through databases.
Unstructured data, on the other hand, lacks a predetermined arrangement, which makes applying traditional querying and analysis methods challenging. Unstructured data can manifest in diverse formats, including images, videos and audio files.
Why Does This Matter for Enterprises?
Global enterprises are always looking for innovative ways to unlock insights from the quantities of unstructured data they receive. Using automated AI to capture the relevant information they need is one of the most effective strategies.
Case in point, according to a survey by the Intelligent Automation Network, 50% of RPA and digital transformation leaders use intelligent document automation to manage unstructured data. As for the future, a further 42% have it on their radar.
Modern AI-powered automation solutions deploy deep learning, natural language processing and Optical Character Recognition (OCR) to achieve a slick, intuitive mode of information capture.
Let's consider invoices as an example. While invoices often contain unstructured financial data, certain sections might conform to predefined structures, such as line items and totals. As a result, conventional extraction technologies might struggle to process invoices in diverse formats.
Automated data extraction from invoices follows a straightforward, step-by-step process:
The user uploads the invoice PDF.
The AI data extraction technology identifies and extracts pertinent fields (i.e., invoice or order number, VAT total, etc.).
Within a few seconds, the invoice is converted from an unstructured PDF into an Excel, CSV or JSON file. The data is then normalised and deposited into the chosen repository.
The technology underpinning invoice extraction is multifaceted. OCR facilitates the conversion of the PDF into machine-readable text. Subsequently, natural language processing contextualises the content, pinpointing required data. Incorporating deep learning empowers the machine to learn from its errors.
Time is money, especially when it comes to financial documents. Intelligent data extraction can confer a significant advantage in AI loan underwriting.
For instance, business lenders can now instantaneously approve or decline loan applications, navigating intricate due diligence processes. Across multiple industries, automated data extraction from PDFs speeds up and simplifies operations and workflow.
Some employers may believe that manual data extraction only costs as much as the twenty minutes or so it takes for their employees to capture the data from a PDF file. Those twenty minutes, however, represent the lost potential of high-value activities and costly human errors.
Automated data capture works in the background to instantly and accurately output structured data. Additionally, most AI data extraction providers price their documents per page, rendering it a transparent, low-cost solution for enterprises and small businesses alike.
Selecting a provider that continues to develop its technology is essential.
Every month, AI technology is taking massive strides in its ability to generate, extract and analyse unstructured data. Consequently, a vendor should take a research-driven approach to avoid obsolescence.
It is important to be realistic about how long it will take to train the model for your enterprise’s needs. For example, if extracting data from bank statements, the model won’t likely require training. For unique and complex documents, the AI model will need to process examples before it can seamlessly capture data.
AI-powered data extraction technology continues to lead the charge with diverse applications. Let’s zero in on using automation for financial statements.
Financial statements are technically-structured documents that typically adhere to accounting regulations and standards. However, they also can contain disclosures and notes that prevent traditional data extraction tools from effective capture. AI can extract from financial statements, matching or even surpassing human capacity.
Financial reporting is another area where automated data extraction from PDF files is in high demand. Most AI data extraction technology is capable of supporting post-processing rules. For example, fraud detection might flag changes in the formatting of the PDF to indicate tampering. However, integrating more intricate technologies (i.e. sophisticated automated analysis) into legacy company architecture may present challenges.
Automatic data extraction from PDF files has come a long way in a short time. AI technology can effectively filter, normalise and output unstructured data — effectively converting unstructured data into structured data.
To learn more about the applications of our offering, we invite you to book a demo with Evolution AI or contact our team at hello@evolution.ai.
