Etymologically, ‘data’ refers to ‘a fact given or granted’. Since 1897, it has evolved to mean ‘numerical facts collected for further reference’. Likewise, extraction means the ‘process of withdrawing or obtaining’ or ‘to draw out’.
We could, therefore, refer to data extraction as ‘drawing out data for further reference’. Close enough for now…
When defining data extraction, we might aim for a more specific meaning. Firstly, it’s important not to describe data extraction. Data extraction varies by method (e.g. manual or automated) and target data (e.g. handwritten documents, financial documents, websites, etc.). Accordingly, using an over-specific definition can be tempting.
Several online definitions of data extraction lean into speculatory language: ‘some of the [extracted] data might be poorly organised’, and so on. It’s important not to overthink what data extraction might be when its core definition is straightforward. Ultimately, data extraction is as it sounds: capturing data and structuring it into the desired output. For example, you might extract data from a web page, document or image and insert it into an Excel spreadsheet.
Of course, definitions will vary based on their context. For instance, the British Medical Journal defines data extraction as ‘...the process of a systematic review that occurs between identifying eligible studies and analysing the data, whether it can be a qualitative synthesis or a quantitative synthesis involving the pooling of data in a meta-analysis.’
Their definition includes its analytical function, where data is systematically extracted from medical studies. Other definitions of data extraction refer to its position as an analytics tool. For example, the SaaS, Stitchdata, refers to data extraction as ‘...the goal of preparing the data for analysis’.
We’ve always considered data extraction a cohesive process, with any extra analytical capabilities as a conditional follow-up. The same goes for the synonyms of data extraction, which include the following:
Data capture is probably the closest synonym to data extraction. Similar to how a camera captures an image, a person or technology can 'capture' data by converting it into a new format (e.g., transforming a PDF into an Excel spreadsheet).
Intelligent Document Processing (IDP) involves automating manual data entry with AI technology. Technically, you can extract data from any source, but IDP is document-specific. The ‘intelligent’ qualifier refers to the presence of AI algorithms.
IDP has become increasingly popular over the years as a relatively simple-to-implement automation. Various integration options – API, connective tools and secure file transfer – make adding IDP tools to legacy systems easy.
As Google Trends illustrates, searches for ‘Intelligent Document Processing’ peaked in October 2024. Unfortunately, there is no relevant data for its abbreviation, as IDP also refers to ‘International Driving Permit’.
You might extract data from an unstructured source, such as a video, image, webpage, etc., and convert it into a structured format. Structured data will contain a predetermined format for the outputted data. In contrast, unstructured data is not organised in this predetermined format.
There is some controversy around what counts as unstructured data. Some sources suggest that invoices can be structured in certain contexts (e.g. electronic). Whereas, other sources argue that invoices are unstructured.
‘Semi-structured’ provides a middle ground, where data is not sorted into a predefined structure but contains some type of tag or other element that hierarchises it. Semi-structured data might also contain unstructured and structured elements. For example, some invoices may adhere to a format (details, line items, etc.), but their layout might vary.
Data entry involves extracting data from one source into an actionable format. A typical use case might involve a hospital administrator logging new patient details into their system. The patient’s information then becomes accessible, searchable and structured.
Therefore, not all data extraction refers to data entry, but all data entry involves an extraction process.
Data extraction is intricately connected with other data processes, like data entry and structuring data. Data extraction is often confused with other data-related processes that follow protocols similar to those used in data extraction. Hence, here are several concepts that data extraction is not.
Data acquisition and collection involves gathering raw data from relevant sources. Yet, it is not synonymous with data extraction.
While data extraction technology can automatically capture specific pieces of information from databases, documents, or websites, it does not inherently possess the ability to evaluate or select the most appropriate sources for extraction. Data selection – the step where relevant and credible sources are chosen – must occur before extraction.
Data mining involves searching through a large dataset to find patterns. You might complete data mining on extracted data, but data mining and data extraction are not synonymous.
Data retrieval involves identifying, extracting and presenting information from databases, storage systems or other data repositories. Because the data has already been stored, it should be structured for database ingestion.
And, possibly the most important thing that data extraction is not…
Another assumption to derail is that the extracted data is always high quality. Quality will depend on the method of extraction. A poor data extraction technology will generate data with 30% accuracy or less. In contrast, a well-trained data extraction solution can generate data of 99.5%+ accuracy.
Extracting data can go wrong in other ways. Slow extraction, incorrectly structured data or file corruption can all make data extraction painfully long rather than efficient.
Manual and automated data extraction methods have different meanings.
Manual data extraction refers to manually extracting data from a source. For instance, a human might enter balance sheet information from a PDF of an annual report into a spreadsheet to complete a financial modelling task.
Data entry operator jobs are still going strong, as manual data extraction is often baked into office administrative or back-office tasks.
Some companies may hire dedicated data entry operators, whilst others may use experienced analysts to extract data. We’ve spoken to companies using $50-per-hour analysts to copy data into their internal databases. Manual data entry is costly for millions of businesses, especially when an easy-to-use automation alternative exists.
Automated data extraction is designed to save on the cost and inconvenience of manual data extraction. Using automation technology allows data extractors to automatically read PDFs and upload the relevant data into the desired repository without manual touchpoints.
A high-quality data extraction method can yield several benefits for most businesses. Let’s explore three of them.
Data extraction is a fundamental operation that many companies require. By automating data extraction, businesses can save time on manual data extraction processes – up to 95%, in some cases.
Businesses can translate such time savings into meaningful time spent examining the output and making strategic observations. A high-quality, preferably automated data extraction method can equate to major cost savings for any business.
Saving time, in this context, also means saving money. Rather than chasing data around a page, employees can turn to activities that directly drive revenue, such as:
Of course, operating a poorly functional data extraction solution (such as faulty OCR or non-scalable manual data entry methods) will eat into operational costs.
Using a robust data extraction method means employees won’t waste their valuable time and expertise identifying and correcting data extraction errors. The result? More productive employees who can focus on sophisticated problem-solving or other client-centred tasks they were trained to do.
Let’s flip the script and discuss the meaning of data extraction in relation to its significance or value. So, what does data extraction mean in 2025? We loosely apply philosophical approaches to unspool its meaning.
The ontology – or the state of data’s being – is complex. On one hand, the data exists in an initial source. Otherwise, how could it be extracted by hand or technology?
Of course, a page of extracted data (the output) doesn’t mean anything until it is interpreted and actioned. Data extraction is generally embedded into an end-to-end process like:
Otherwise, data extraction is a fundamentally meaningless process, even if the data is not.
The consequences of a data breach can be catastrophic. They can lead to $30 million or more in lawsuits. However, breached data can also present a serious ethical issue to the individual, potentially leading to identity theft, fraud and a generally reduced quality of life.
Handling sensitive information might mean some may consider data extraction a risky process. Indeed, sending data outside of an organisation to a data extraction vendor might seem like a potential (data) confidentiality issue.
With the right precautions, data extraction can be a secure process. A well-trained data entry operator will also know how to maintain data confidentiality. Likewise, an automated data extraction solution should have a compliance qualification (e.g. IS027001), which demonstrates the organisation has taken appropriate measures to protect its data. Product features in data extraction technology that also address data security include:
Ensuring you have a secure data extraction method means you can enjoy its benefits worry-free.
Aside from confidentiality and data privacy issues, data extraction could be unethical if misrepresented. A vendor can misrepresent data extraction in a variety of ways, including:
AI-powered data extraction should mean that AI is actually involved. While this specification may seem tautological, AI washing is still a threat.
AI washing refers to describing a product as AI-powered when it doesn’t contain real AI technology.
Capitalising off AI’s buzz is unethical. If data extraction is truly AI-powered, it must have these attributes:
If AI makes a mistake and it is corrected, it should, in theory, learn from the correction why the original data point was erroneous and never repeat it. The accuracy of the performance of a genuine AI product represents an upward curve.
If an AI-powered product does not continuously improve, it’s likely trained on rule-based, inflexible and not genuine AI technology.
Suppose you want to extract from a new source (e.g. a new document type). An AI-powered solution should provide an accurate performance without prior training examples – a phenomenon known as zero-shot learning.
The pragmatic implications of data extraction might focus on how you can use it to make business decisions. For example, extracting financial statement data might help companies make decisions that affect the future of their firms.
Another pragmatic approach might focus on how data extraction can tangibly improve people’s lives. For instance, data extraction can form the spine of decision-making systems, helping underrepresented groups access essential financial resources.
We’ve written before about how firms can use alternative data sources as evidence for approving loans or credit applications. Automated decision-making systems that use this data and take action to bring it into social change are becoming increasingly necessary, where cultural and economic tensions mean technologies should ultimately become people-focused.
We’re Evolution AI, a multiple award-winning data extraction vendor. Working alongside global leaders like NatWest, Deutsche Bank, Hitachi Capital and Dun & Bradstreet, we’ve perfected a technology that extracts data from financial documents and more.
For us, data extraction is our whole existence. To see a demonstration of our technology or learn more, book a demo or email us at hello@evolution.ai.
