Six Ways To Ensure Data Security During Extraction Process

Data extraction is usually one of the early steps in any data-driven process. As a business, you require a wide range of information to keep the company running. Access to data is currently at an all-time high. However, before data can be analyzed and used to get valuable insight and make informed decisions, it has to be extracted.

Data extraction involves the retrieval or collection of various types of raw data, which are often poorly organized or unstructured. It is the first step in the ELT (Extraction, Load, Transform) process and the ETL (Extraction, Transform, Load) process - all part of the data integration strategy.

Data security is crucial in every data processing stage, including data extraction. Cybercriminals and other bad actors constantly seek ways to steal valuable information from businesses. Therefore, organizations must take data security seriously at every stage of handling it. This article will discuss data extraction and the different ways to ensure data security while extracting data.

Data extraction is the process of procuring data from a data source, such as a cloud server or physical hardware. It involves obtaining raw data from one source and transferring it to another destination for further use. The raw data can come from anywhere, including web scraping, databases, Excel spreadsheets, or SaaS platforms. 

Data extraction enables the merging, analysis, and refining of data for processing into useful information that you can store for future use or manipulation.

Unless you extract data for archival reasons, it is normally the first step in the ETL process. This implies that, following the initial retrieval, data will always undergo extra processing to convert it to a usable form for future analysis. 

Data extraction is the most essential operation of the ETL process because it is the foundation for crucial analyses and decision-making processes that are important to organizations.

You can classify data according to their sources. We explain such sources below.

Physical data sources are print or physical media. Examples include books, journals, magazines, newspapers, brochures, marketing materials, and paper invoices. However, data extraction from physical sources is manual and tiring. In addition, it is error-prone since it requires human efforts to access the data source, extract the data and transfer it to the final destination.

Digital sources are the most common form of data sources available presently. They include any kind of data set present on a file either online or in a device’s local storage. Examples include websites, e-invoices, spreadsheets, emails, and online and offline databases. Data scraping and web scraping are different ways to extract relevant data from these digital sources.

You can also classify data sources according to structure, including:

This data source is already formatted in a logical structure that fits the needs of your project. This means you don’t have to manipulate the data further before using it. The extraction process is typically done within the source system. 

This is the most common form of data available for extraction - disorganized bits of information that you need to carefully sift through and organize. They lack structure and must be reviewed and formatted before extraction.

The good news is that data extraction need not be a strenuous process for you or your organization. You can use a no code data extraction AI platform that enables organizations to transfer both structured and unstructured sources of data seamlessly without writing or maintaining code.

Other forms of data businesses extract include:

It has become common for businesses of every size to collect customer data. This is essential to delivering excellent service. Examples of customer data include names, phone numbers, email addresses, health data, age,  purchase history, etc. 

These forms of data are necessary for accounting purposes. They include sales numbers, purchasing costs, operating margins, competition prices, etc. Financial data helps organizations track performance and plan strategically.

This category includes data related to specific tasks or operations, such as patient results in the healthcare setting, sales logistics for a trading company, etc.

There are generally two types of data extraction. We will explain them below.

Logical data extraction is the process of procuring data through software. This means that you don’t require a physical connection between devices during logical extraction. Instead, during logical data extraction, data is extracted from the device through its interaction with the operating system and access to the file system. There are two forms of logical extraction.

Source systems sometimes possess some restrictions or limitations, such as being outdated. Logical extraction is impossible in this case, and data can only be extracted through physical extractions. There are two forms of physical extraction.

During data extraction, you should handle data with sensitive or personal information (think health data, political affiliations, addresses, and financial information) with care and treat it as a priority. Any error during extraction could lead to cyber attacks, breaches, or non-compliance with data privacy laws. 

A famous example of cyber attacks is credential stuffing. It is an automated threat that uses malicious bots to “stuff” known usernames and passwords (typically sourced from data breaches) into online login pages. Once they gain access to an account, they can do whatever they want as though they were the owner. You can read this article to gain insights by DataDome on credential stuffing.

Cyber attacks will not only tarnish the image of your organization, but they will also attract heavy fines and legal trouble. Therefore, businesses must prioritize data security during extraction to avoid these issues.

Below are some practical ways to ensure data security while extracting data:

Sensitive data is highly confidential information that requires protection from unauthorized access. Cardholder details, biometric data, and healthcare data are common examples of sensitive data.

To effectively secure your data, you must precisely know what types of data you have. Therefore, you must first scan through the entire data repository. Then, you can organize the data into categories using a data classification process. Once organized, protecting sensitive data from external threats is easier because you know where to concentrate your data security efforts, while complying with privacy regulations.

Encrypting your data during extraction further secures it. Encryption is a computer process that converts data into unreadable formats using mathematical algorithms. So when an unauthorized party tries to steal encrypted data, they cannot interpret its content. 

An advantage of encryption as a data security measure is that it is easy to implement. There are several encryption software available online that you can download for free. And even if a data breach occurs, your data will be safe.

Any computer or server can be vulnerable to internal and external cyber threats. So, you must implement tough security measures at your database's physical location. Protect access points, lock doors, and make them inaccessible to unauthorized people. Furthermore, enforce stringent visiting procedures, and restrict access to critical data. 

These are necessary because a cybercriminal with physical access to your database server can steal data, corrupt it, or add malicious software to get remote access.

It is crucial to test your database security regularly. You can do this by utilizing a security service like CloudFlare. This will help protect your web application and database from online attacks.

A firewall is the first line of defense against attacks on a company's computer systems. Database firewalls shield databases against unauthorized access, while web application firewalls shield web applications from malicious traffic. To offer complete protection for a company's computer systems, web applications and database firewalls should work together. 

Operating systems (Windows and MacOS) and SaaS software require updates to maintain security. These updates are rolled out to fix vulnerabilities discovered in the system. Therefore, it is vital to keep your software up-to-date by promptly installing these updates. Failing to update your systems will expose your databases to attacks.

Sensitive and personal data are constantly at risk from internal and external threats. Therefore, you must exercise extra care during data extraction processes to avoid data leaks or breaches. This article explained some ways to ensure data security during data extraction. If you implement them, you would not have to suffer data breaches during extraction.