What is Data Extraction? [Best Techniques + Examples]

Nowadays, we have access to more data than ever before. The result of which is that data has become one of, if not the world’s biggest commodity

So for many businesses, the biggest challenge has become managing and analysing huge volumes of data effectively, especially with data coming from so many different sources. 

But before this can be analysed, it must first be extracted. That’s where the data extraction process comes in. 

Utilising data extraction techniques can help to transform your business, but if you’re not clued up on the subject, you might not realise the impact this can have. 

In this guide, you’ll learn: 

  • What data extraction is 
  • The different data extraction processes
  • The different types of data extraction tools and techniques 
  • Why data extraction is important
  • Some data extraction examples

Sound good? Then let’s get started. 

What is data extraction?

We’re first going to take a look at what data extraction actually is. In a nutshell, this is the process of collecting data from a variety of sources; these can be structured or unstructured. For example, retrieving and collecting data from online forms, emails, PDFs, etc. 

Data extraction makes it possible to collect, consolidate, process and refine data. This can then be stored in a centralised location depending on the business and function. These locations could be on-site, cloud-based or a combination of the two. 

Data extraction is also the first step in ETL (extract, transform, load) and ELT (extract, load, transform) processes. These two processes are important for creating a complete and effective data integration strategy.

What are the different data extraction processes?

To continue building our understanding of data extraction, we’re now going to look at the different processes. There are two different types of data, structured and unstructured. According to Altexsoft

Structured data stands for information that is highly organised, factual and to the point. It usually comes in the form of letters and numbers that fit nicely into the rows and columns of tables.

Unstructured data doesn’t have any pre-defined structure to it and comes in all its diversity of forms. The examples of unstructured data vary from imagery and text files like PDF documents to video and audio files, to name a few. 

And as an estimated 20% of today’s data is structured, while the remaining 80% is unstructured, it’s important that data extraction processes can handle both types. 

Below, we’ve outlined the different processes that are undertaken when it comes to capturing both structured and unstructured data. 

Structured data

If the data is structured, the data extraction process is generally performed within the source system. It’s common to perform data extraction using one of the following methods: full extraction or incremental extraction. 

Full extraction is when data is captured from the source. Therefore, there is no need to track any changes. The logic itself is much simpler, but the system load is also much greater.

Incremental extraction means that any changes in the source data are tracked since the last successful extraction. This is done so that you don’t have to go through the process of extracting all the same data each time there is a change. 

However, to do this, you might need to create a change table to track changes or check timestamps. This is referred to as change data capture (CDC) functionality.

Some data extraction tools will already have this CDC built-in. And while the logic for incremental extraction is more complex than that of full, the system load itself is less.

Unstructured data

With the majority of data being unstructured, the first challenges for businesses is preparing this data and making it intelligible. To do this, the data extraction process is made up of five important steps. These are: 

  1. Ingesting: The first step is to ingest all of the required data, which means the relevant systems and documents must be identified and prepared to be digitally crawled. 
  2. Converting: Once all the necessary data has been ingested, it needs to be assessed. If the data is not intelligible, it needs to be converted to a readable and searchable format. Often optical character recognition (OCR) is applied to make this happen. 
  1. Classifying: Next, the converted data must be classified. Each piece of data must fall into a logical and accurate category, so these categories must be set up using key identifiers. 
  2. Identifying: The next step in the data extraction process is to apply regular expression (regex) technologies. Once this has been done, the entire data set will become searchable.
  3. Extracting: The final step, once all necessary data has been identified, is to use a rule-based framework to extract the data. What this means is that once rules are set up for the data, when it is found, the desired action can be performed on it. In this case, extraction.

Of course, there is a lot more work and technology that goes into making these processes happen, and in a matter of microseconds, but this is a basic breakdown of how these data extraction processes typically work.

What are the different types of data extraction tools and techniques? 

As well as there being different processes for data extraction, there are also three different types of data processing tools and techniques. The tools you choose will depend on the types and volumes of data you’re processing, as well as the function it plays in your business. 

Looking at the different types below can give you a better insight into the types of data processing tools that could be useful in your business. These include: 

Batch processing tools: 

Batch processing tools pretty much do what they say on the tin. These consolidate your data in batches, usually during the hours your business is closed, to minimise the impact that using large amounts of computer power could have when your business is open. 

As such, this is often best for closed, on-premise environments. It is also better for homogeneous sets of data sources as these are easier to extract in batches. 

Open source tools: 

Open source tools can be provided by a third-party vendor at a lower cost and are therefore a good fit for budget-limited businesses and applications. The only catch is that the business must have the supporting infrastructure and knowledge in place to be able to use these types of tools. 

Cloud-based tools: 

Finally, cloud-based tools are the newest offering in extraction products. 

These are often designed to focus on real-time data extraction as part of an ETL/ELT process (which we briefly touched on earlier). 

These data extraction tools can be more secure and help to take the stress out of data compliance. They also offer a lot more in the way of data storage and analysis than other tools, which is why cloud-based extraction tools are quickly becoming very popular with businesses.  

What are the benefits of data extraction and why is it important? 

Now, it’s all well and good us explaining data extraction and going through the different tools and processes, but we haven’t actually covered why your business even needs data extraction software. 

Well, data extraction has a number of benefits that can help to automate and speed up your workflows. This can have an impressive impact on your company and your teams. Below, we’ve pulled together some more of the top benefits of data extraction and why it is so important: 

Improving accuracy 

Data extraction can help to improve accuracy and reduce the risk of human error. For example, by automating the data extraction and entry processes for repetitive tasks, there is less chance of a  mistake being made by an employee. 

Increasing employee productivity

By removing the need to do lots of repetitive and mundane manual data extraction, employees are free to spend their time on the more important tasks that only humans can do. For example, they can work on building lasting relationships with clients. 

Improving visibility and agility

Data extraction tools help your teams to get their hands on data faster. This means the data is more visible to everyone in the business who may need it, in less than half the time. 

It also means you can consolidate your data into a centralised system that can help promote collaboration and agility across teams and even departments.

Saving money and time 

Time wasted on labour-intensive manual tasks can be costly for businesses. By automating processes like data extraction, the tools can save your business time and allow employees to concentrate on more important issues. 

The overall result being that you can save money and time and perhaps even increase profits as staff focus on bigger issues. 

Gaining control 

Finally, data extraction allows companies to migrate data from a variety of outside sources into their own databases. 

This gives them more control over what data they’re collecting, where they’re storing it and how they are using it to help the business grow.

How can data extraction be used in business and what are some examples? 

At this point in the guide, we’ve spoken a lot about data extraction and why it’s so great – but how can it actually be applied and used in your business? 

Because let’s face it, this is what you really need to know before implementing new technologies. 

That’s why, in this next section, we’re going to look at some data extraction examples. These will cover some of the different but more common ways that data extraction is used in businesses. We’ll then look at some real-world examples of how these tools have made a real difference to companies in the past. 

Some of the top ways that data extraction can be used in businesses include:  

  • For decision making
  • For finding new customers and improving customer service
  • As a way of optimising your company’s costs
  • For streamlining time-consuming business processes 
  • To help predict sales trends

This is made possible because data extraction tools can quickly scan and sort data from a range of structured and unstructured sources. For example, invoice data extraction can make accounting functions much quicker and easier. 

Other ways that data extraction can be used to make the above possible include: 

  • Scanning invoices, purchase orders, contracts, price lists, bank statements, etc. 
  • Assisting with scanning and extracting data from online forms – such as HR forms or applications 
  • Dealing with sales orders, delivery notes and shipping orders 
  • Monitoring and analysing prices and other market place intelligence 
  • Web scraping and extracting data for research purposes 
  • Offering insights for better risk management

As you can see from this, there are a huge number of ways that data extraction can be used in businesses. The benefits of which should now be clear.

But to continue building on our understanding of how data extraction can be used, we’ve pulled together a couple of ways it has solved real-world problems for real businesses. 


Domino’s is the largest pizza company in the world and it receives orders from a range of technologies, including laptops, smartphones, social media and even smartwatches. 

So as you can imagine, this generates an enormous amount of data through sales. To consolidate this, they use a data management platform. 

From extraction to integration, they run a system through their own cloud-native servers, which captures data from point of sales systems and through the various different channels customers use to communicate with the brand. 

Newcastle University

Newcastle University, like so many, gets thousands of students each year – over 17,000 in fact. That generates a lot of data and this is divided between 60 data flows across its various departments. 

In order to bring all this data together into a single and accessible stream, the university uses an open-source tool and comprehensive data management platform to help them extract and process all this information. 

This is a cost-effective and scalable solution that enables them to better support the students. 

In summary

As you can see, data extraction has a huge range of uses and benefits for your business. It can go some way to giving you more control and peace of mind over your business functions, without having to hire extra staff. 

So if you’re hoping to increase productivity, stay ahead of your competitors and cut costs for your business, data extraction technology could be for you. 

And believe us when we say, once you start exploring the possibilities of what data extraction can do for you, you’ll be sure to find a use (or several) for it within your own business.

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