Data Extraction for Enterprises: A Practical Guide

Organizations now collect more data than ever, but they are massively underutilizing it. Most simply don’t perform tasks like data extraction as often as they should (or at all).

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According to Zippia, for example, average companies analyze only 37-40% of their data. They’re facing two main problems: 

  • They can’t or don’t know how to effectively perform data-related tasks. According to one survey, the lack of necessary staff, in-house expertise, and collaboration are the biggest barriers to data management excellence.
  • They can’t or don’t want to invest ample resources into data-related tasks. Data tasks can require large amounts of resources, including money, time, and expertise.

This article aims to provide insights that will help your enterprise overcome both issues. 

  • We’ll shed light on what data extraction is, how it works in practice, and how it can help organizations in various industries. 
  • We’ll then discuss how automating data extraction using specialized tools, especially AI Agents, can help you minimize your investment while ensuring high accuracy. 

Let’s start with the basics.

What Is Data Extraction? 

Data extraction is the process of extracting useful data from different sources and storing it in a structured format. It’s primarily beneficial to organizations that want to analyze and use collected data more efficiently — or need to do so in order to make core business decisions. 

For example, loan companies need to extract applicants’ data from documents in order to approve or deny loans and store clients’ data for later.

It’s important to note that data extraction is not synonymous with data analysis. It is, however, the first step in the data analysis process; further processing can’t be done until data is extracted.

To efficiently extract data, every organization should take two preparatory steps:

  • Identify relevant data sources — i.e., understand which systems or documents hold usable data. This can include anything from customer-submitted documents to internal databases, website forms, or PDFs. Most organizations will need to extract data from multiple data sources.
  • Define data requirements — i.e., specify the types of data that are valuable to the organization, such as customer names, income amounts, or contract dates.

What Are the Benefits of Data Extraction?

Data extraction helps organizations get more value from their existing information assets, such as documents and databases. It can help them access previously untapped data, make better, data-based decisions, and improve customer and employee experiences. 

The exact benefits will, of course, largely depend on how enterprises use data and why they’re extracting it. However, in either case, data extraction can help organizations improve their internal knowledge and obtain better business outcomes. 

What Does a Data Extraction Process Look Like?

A typical data extraction process consists of two major steps: 

  • First comes planning. As mentioned, this involves defining data requirements, scope, sources, etc. 
  • Then comes the actual data extraction. This task can be done either manually or automatically via a specialized data extraction tool. Organizations with a lot of data usually choose the latter option.

As mentioned, data extraction involves retrieving data from a source. This source can be internal, i.e., native to an organization, or external, such as a website, third-party database, or third-party document. 

The data extraction process is completed once you’ve, well, extracted data. However, as we’ve already said, extraction is only the first step in a data analysis process. There are other steps you’ll need to take in order to actually make your data valuable and usable — such as: 

  • Checking data quality — i.e., validating aspects like completeness, accuracy, and data type constraints. This helps identify data issues, such as inaccuracies, missing information, and incorrect formats, as early as possible.
  • Data cleaning — i.e., applying techniques like data deduplication and normalization to remove duplicate entries, standardize data formats, and ensure that organizations are dealing with consistent and accurate data.
  • Data transformation — i.e., transforming data to ensure it aligns with specific business or technical requirements via complex processes like data aggregation. It ensures that the data is in a usable and meaningful format before it’s presented to decision-makers.
  • Loading — i.e., loading the extracted data into a central repository, such as a single database or data warehouse, for storage, easy querying, and easy analysis.

In summary — quality checking and data cleaning make the raw data usable; transformation structures it; and loading into a central system makes it easily accessible. This process is usually a part of a larger data integration strategy.

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Data Extraction Example: Finance

Financial companies can (and often must) extract financial data to make core business decisions more quickly and maintain accurate records. For example:
They can extract transaction details from bank statements to analyze spending patterns and detect irregularities.
They can extract income information from paystubs to verify applicants' employment and salary details during loan and mortgage application processing.
They can extract expense totals from receipts to assess customers' ability to repay debts, optimize credit limits, or detect potential risks for cardholders.
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Data Extraction Example: Healthcare

Healthcare companies can extract data that helps them speed up administration, patient care, and their other processes. For example:
They can extract patients’ medical conditions from medical records to quickly detect any risks or chronic issues.
They can extract patients’ vital signs or treatment plans from patient notes to quickly identify medical issues, recommend appropriate care, and flag overdue screenings or follow-ups.
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Data Extraction Example: Legal

Law firms can extract data that helps them streamline legal research, drafting, and other legal tasks. For example:
They can extract contract terms to catch any discrepancies or issues.
They can extract property details from leases to perform rent reviews, manage assets, and resolve disputes smoothly.

See Data Extraction Live

Contact our AI experts to see how Document AI works in action and explore the benefits for your enterprise.

What Are (Automated) Data Extraction Tools?

Data extraction tools are tools that automate the data extraction process, i.e., eliminate the need for manual data entry and data copying. Some tools can only extract data; others provide more capabilities. 

For example, web scraping tools can extract large amounts of structured and unstructured data from web pages, sites, and online forms. However, they rarely perform other data tasks. 

Other tools may perform additional tasks and, thus, automate bigger chunks of the data integration process. Document AI, for example, performs the following tasks:

  • It converts scanned images or paper documents to digital formats. This facilitates document management and data security. 
  • It classifies documents into broad or more specific categories. For example, it can classify financial documents according to the topic (e.g., tax returns, bank statements, and paystubs) or more granular data (e.g., vendor and client names).
  • It extracts different types of data from these documents. Organizations specify which data needs to be extracted by defining their own key-value pairs.
  • It normalizes the data to allow organizations to integrate it with other systems or just compare it more easily. For example, it converts diverse currency values (e.g., USD, AUD, and EUR) into one consistent format (e.g., USD only).

Benefits of Data Extraction

The benefits of data extraction tools can be summed up in one sentence: they automate otherwise time-consuming and costly manual processes while offering exceptional accuracy. Here’s how they compare to manual data extraction.
Manual data extraction
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Can take hours upon hours of tedious work every week
Is more expensive
Is subject to human error

All of these benefits help increase the security, reliability, and scalability of data within organizations, as well as increase revenues. Clients who had adopted Document AI, for example, reported being able to serve many more customers in much less time, as their employees were no longer bogged down with repetitive data tasks.

In a nutshell: data extraction tools offer incredible ROI to organizations, especially those with document- and data-heavy workflows.

Types of Data Extraction

There are many different types of data extraction, but we’ll focus on the differences between extracting data from unstructured and structured data sources.

1. Structured Data

Structured data extraction involves retrieving data from organized data sources, such as databases, spreadsheets, and structured documents (like bank statements). Such sources have predefined fields and data structures that make data extraction easier.

  • Example of structured data extraction: extracting vendor names, transaction dates, and total amounts from receipts.

2. Unstructured Data

Unstructured data sources don’t follow a pre-defined format, at least not entirely. They’re more free-form and text-heavy, which makes data extraction more complicated and not as straightforward. Most tools struggle with unstructured data extraction; Generative AI excels in this area. 

  • Example of unstructured data extraction: extracting text content, emojis, and hashtags from social media posts to conduct sentiment analysis.

Two Common Data Extraction Methods

There are also various data extraction methods. For example, some distinguish logical extraction from physical extraction.

  • Logical data extraction preserves logic in data, i.e., the relationships and integrity of the data while extracting it.
  • Physical data extraction is copying raw data without preserving the relationships and the integrity of data.

We’ll focus on incremental and full data extraction below.

Incremental Extraction

Incremental extraction is a method of extracting only the data that has changed or been added since the last extraction. Instead of pulling the entire dataset every time, incremental extraction focuses on identifying and extracting new or modified data.

Incremental data extraction reduces the amount of transferred and processed data, which minimizes the time, resources, and system overhead required for extraction. It also enables organizations to maintain up-to-date information by regularly extracting and integrating new data as it becomes available.

Use Cases

  • An e-commerce platform may use incremental extraction to update its product inventory with new listings and changes in product availability.
  • Financial institutions can employ incremental (stream) extraction to retrieve the latest transaction data for account updates and reporting.

Full Extraction

Full extraction involves extracting the entire dataset from a source regardless of which data (if any) has changed. 

Full data extraction ensures that the entire dataset is consistent and up to date, as it retrieves all available data each time. It is also relatively straightforward to implement since it doesn't require complex change-tracking mechanisms.

Use Cases

  • Organizations may use full extraction to create periodic snapshots of their data for historical analysis and reporting.
  • Industries with stringent compliance requirements may use full extraction to maintain complete records for auditing purposes.

Extract Data On Your Terms: Get Custom Document AI for Your Enterprise

Most data extraction tools limit you to generic key-value pairs. Document AI is trained on your custom schema, letting you extract the data you need in exactly the fields you want. That way, you never have to spend time manually extracting or editing the data yourself.

On top of that, Document AI is trained on your enterprise’s documents, which ensures it understands your industry, business goals, and use case. Order it today and we’ll implement it for you in less than 2 months.

See Data Extraction Live

Contact our AI experts to see how Document AI works in action and explore the benefits for your enterprise.


From which sources can data be extracted?

Generally speaking, data can be extracted from any source, including a simple database, a complex database management system, and unstructured documents. However, in practice, this will highly depend on the data extraction tool you’re using. Document AI, for example, can extract data from any document your enterprise is dealing with, as we train it specifically for your use case.

What is the difference between data extraction and data mining?

Data extraction is the process of retrieving data from various sources and storing it, while data mining involves analyzing and discovering patterns, trends, and insights within data. For example, data extraction might involve collecting customer information from online forms, while data mining could be used to identify purchasing patterns within that data to improve marketing strategies. If you’re interested in automating data mining, Workflow AI will be a better fit.