CEO's Guide to Data Extraction

Data extraction is at the core of many different businesses: from finance to e-commerce companies, and everything in between. Data extraction tools can help automate various tasks and save companies money, time, and human resources. In this article, we will discuss the importance of data extraction, have a look at the use cases, review different tools, and go through the challenges of this process. 

If you are wondering whether your company should extract data, here you will find all the necessary information.

What is data extraction?

It is part of the Extract, Transform and Load (ETL) process that is the core of data ingestion. Data ingestion is an important part of business strategy, since it allows accessing collected information for immediate use or importing it to be stored in a database. 

What is data extraction used for?

As the data extraction definition suggests, this process is used in order to consolidate and refine information, so it could be stored in a centralized location and later transformed into the required format.  

Many companies extract data and use it in their business. In this article, we will cover various use cases in more detail, but first, let’s find out more about data structures and extraction methods. Please note that in this article we only talk about public data extraction. The data that is not public can only be scraped with a clear consent of the owner, or if this data belongs to you.

Types of data structures and extraction methods

Unstructured data refers to information that lacks basic structure. In order to extract such data, it has to be formatted or reviewed before the extraction. This may include cleaning up, for example – deleting duplicate results, removing unnecessary symbols and whitespaces.

Structured data is usually already formatted for use and does not have to be additionally manipulated.

• Full extraction allows completely extracting data from the source, and is used when acquiring information for the first time. Some sources cannot identify changes, so in order to receive up-to-date information, the entire dataset has to be reloaded. 

• Incremental extraction involves tracking information changes and does not require extracting all the data from the target each time there is a change. However, this method may not detect deleted records.

Data extraction process

So how is data extraction done? In most cases, the data extraction process from a database or a software-as-a-service (SaaS) platform involves three main steps:

1. Looking for structure changes, i.e. new tables, fields, or columns.

2. Retrieving target tables (or fields, or columns) from the records specified by the integration’s replication scheme.

3. Extracting appropriate data, if it is found.

Collected information is then uploaded to a data warehouse.

If you would like to learn more about the data extraction process, read how to extract data from any website? where we covered every step in detail.

Why is data extraction important?

According to McKinsey Digital’s report, CEOs spend more than 20% of their time on tasks that could be automated, for example, collecting information for status reports and analyzing operational data. Data extraction can help systematize information and automate time consuming tasks as well as help companies achieve other benefits, for example:

• Improve accuracy and reduce human error

Data extraction tools automate repetitive data entry processes. Automation can improve accuracy of data inputs and reduce human errors.

• Increase productivity

Manually entering large amounts of data is a daunting task for many, since it is repetitive and hardly stimulating. Removing the task of manually entering data means employees can spend more time on other duties that they find more motivating.

• Increase data availability

Extracted and stored data can be visible and available to anyone in your team who needs it much faster. Information can be accessed whenever needed, without having to wait for someone to upload it into the system manually.

• Save businesses time and money

All of the above points add up to one of the most important benefits of data extraction: it helps companies save time and money. Automated processes require less human resources and allow the staff to focus on data analysing and other tasks.