Automated data extraction is the process of automatically capturing and extracting data from various sources, such as documents, images or websites.
Data extraction automation aims to eliminate the need for manual data entry. Though manual data entry has progressed from typewriters and keypunch machines, it’s still a ubiquitous administrative task. Even in technologically forward firms, employees are accustomed to copying data from PDFs to Excel spreadsheets or manually entering information from invoices into internal databases.
Triggered by the upload of a document, algorithms should seamlessly extract data and deposit it into the specified repository, as if by magic. The workflow should be smooth, accurate and low-maintenance. So, who would automated data extraction appeal to, and why?
The main motivations for automating data extraction fall into two camps: saving labour and saving costs.
Saving labour is an important motivation, particularly in 2024. Many industries are experiencing unprecedentedly high levels of burnout. For example:
Though obviously, manual data extraction isn’t directly responsible for this wave of burnout, the pressure stemming from a lack of resources, economic burnout and crammed schedules can contribute significantly to it.
Squeezing tedious administrative tasks into employees’ workloads adds additional stress, particularly for trained professionals whose skills are underutilised. Rather than applying their financial analytical skills, employees are forced to complete repetitive tasks, which is dispiriting.
AI-led automation offers a way for people to use their skills, which then rewards companies by saving costs. Though the saved costs will vary, they can be substantial, as automated data extraction ensures:
Therefore, companies looking to take a technologically forward-thinking, employee-centred, operational and cost-conserving approach should look at data extraction automation.
The ability to automate data extraction has been a hard-won battle. As a low-skilled manual job, it might seem relatively straightforward to automate. If you break it down into stages, however, it is a multi-step process with many opportunities to break or malfunction. In general, here’s how it works:
1. Receive a poorly structured or unstructured document (e.g. a contract or financial statement) – for example, the document might be uploaded to a database or received via email.
2. Algorithms then ‘read’ the document, discriminating between synonymous or homographic language. The algorithms must be able to discriminate between the meaning of ‘check’ (meaning to validate or a receipt), ‘fund’ (meaning ‘to fund’ or ‘a fund’) or balance (meaning a financial statement or a remaining amount).
3. The algorithms perform summation checks to identify internal inconsistencies and ultimately avoid errors. If they find an error, they must revert to Step 2.
4. Finally, the data must be uploaded and ready for the next step (e.g. data storage or processing).
That we can now automate the above steps into a process that is almost (totally) accurate and only takes a few seconds, is the result of years of struggle and challenges with development.
The main culprits for this struggle include:
Data extraction automation’s biggest friend and foe, OCR, converts images into machine-readable text. The problem is that training OCR is time-consuming and results in a rigid technology that struggles with unstructured documents – like financial statements.
Converting batches of hundreds of thousands of documents into data requires technologies like high-end graphic processing units (GPUs), large amounts of random access memory (RAM) and high-speed storage. These technologies require considerable power, which wasn’t available until the 2000s. The widespread availability of multi-core processors, increased memory capacities and improvements in energy infrastructure made it possible.
Automation becomes faster and more precise with advances in large language models (LLMs) and other emerging AI technologies. AI can understand words in the same way as a human and recognise when it may be extracting information incorrectly.
Of course, deploying the various intelligent automation tools in practice requires a specific, output-focused approach.
There are several options available for making data extraction automatic. Which option is right for you depends on how many documents you want to extract data from and your tolerance for errors. Let’s break down three of the most popular options.
You can prompt an LLM like ChatGPT, Gemini or Claude with something like ‘Can you extract the data from this page?’ or ‘Extract the line items from this document’.
It’s free and convenient.
Inaccuracy – according to our tests, LLMs hallucinate at a rate of one error per page.
‘Ask an LLM’ may work for you if you have one or two documents, plus the time to quality-check the LLMs’ output. Read more about the limitations of using LLMs to extract data here.
By using a tool like Zapier or Workato, you can establish a workflow that allows you to activate a trigger and achieve a desired result (i.e. extracted data in your desired format and location).
Read about how Novuna Business Finance connected to our data extraction with Workato.
Integration platforms excel at rapid processing and are built for convenient integration (for non-IT professionals).
The real-time automated data usually isn’t checked, meaning that errors can fall through the net undetected.
If you’re looking to extract from a large batch of documents in real-time and don’t need complete accuracy, then this may be the right fit. Plus, it’s also ideal if you have the time to test and establish this workflow.
Working with a data extraction technology vendor is helpful for custom requirements that require a specialist’s touch.
Some data extraction vendors offer a managed service. In a managed service, the user will manually verify the data for complete accuracy.
Success will depend on the quality of the vendor. Some newer vendors likely haven’t refined their technology for reliable extraction.
This option is best if you have large batches of documents, custom requests or would like complete accuracy.
When we reviewed a small sample of data extraction automation vendors, we noted that their pricing models were generally 50/50 between licence-based (i.e. paying a fixed amount per month) or per page pricing. Some vendors use an intricate mixture of both, i.e. licence-based with surplus per-page pricing.
Though there is no best way to price, it’s important from the user’s perspective that costs are transparent and predictable. Your document demands will likely change over time, so budget accordingly.
Explore Evolution AI’s per-page pricing.
While automated data extraction is highly efficient due to the presence of AI, AI’s continued evolution means that automation could become:
The increasing context window of LLMs means that AI can process more information faster.
Due to AI's competitive landscape, the cost of tokens will likely fall. Accordingly, the cost of data extraction per page will also decrease, meaning data extraction providers will likely offer more competitive rates.
Initial data extraction automation solutions were packaged into clunky, slow programs that often had to be manually installed into company hardware. Today, automated data extraction takes the form of sleek SaaS platforms that will only get faster and more accurate with time.
Extracted data as the end result of an automated data extraction workflow almost feels like a missed opportunity. Newer technologies can not only automate data extraction, but they can also automate the step that comes after it, whatever that may be.
Our tool, Financial Statements AI, directly leverages the extracted data to calculate key financial ratios. The tool is designed to save analysts and accountants a few minutes, which manifests as hours and days in the long run. Giving this time back allows these professionals to think about what the data really means rather than just aggregating it into one place.
Data extraction automation is a technologically sophisticated approach to a traditional administrative process. Automation while preserving accuracy is a huge achievement of AI. In the future, we may see the rise of analytics tools that can quickly automate the data extraction process and manipulate the data as required.
Interested in what automating data extraction could achieve for your company? Book a demo with our financial data team to learn more – or email hello@evolution.ai.
