Data Extraction Tools: Improving Data Warehouse Performance

With corporate data increasing approximately 40 percent each year, it’s almost impossible for organizations to manually keep track of and collect every data point available. Enterprises that do rely on manual efforts dedicate an inordinate amount of time, resources, and effort to get the data they need—and that’s before cleaning, preparing, and processing them for analytics or other needs.

Modern data extraction tools solve this issue. Data extraction software significantly expedites the collection of relevant data for further analysis by automating the process, giving organizations more control over the information. In this article, we’ll define data extraction, discuss its benefits, and highlight criteria for choosing the right data extraction tools.

Data extraction — The “E” in ETL

As part of the Extract, Transform, Load (ETL) process, data extraction involves gathering and retrieving data from a single source or multiple sources. In this respect, the extraction process is often the first step for loading data into a data warehouse or the cloud for further processing and analysis. This process can be automated with the use of data extraction tools.

Data extraction tools efficiently and effectively read various systems, such as databases, ERPs, and CRMs, and collect the appropriate data found within each source. Most tools have the ability to gather any data, whether structured, semi-structured, or unstructured. Combined with the ability to extract information from infinite big data sources, business users can leverage a collection of sources — such as product databases with real-time e-commerce applications — to produce a more well-rounded and informed business intelligence report.

In the last several years, web scraping has emerged as a technique used by data extraction tools, particularly for the ETL process. Web scraping involves segmenting web pages and extracting relevant information. Often, valuable data, such as customer information, is obtained from web scraping, which relies on various automation technologies including Robotic Process Automation (RPA), Artificial intelligence (AI), and machine learning.

A comprehensive data extraction tool should have the ability to gather pertinent data from webpage — based sources like e-commerce sites, emails, blogs, and mainstream news services, as well as internal and external systems.

6 benefits of data extraction tools

Whether deployed as a standalone solution or incorporated into ETL or Extract, Load, and Transform (ELT) offerings, there are many reasons why data extraction tools are a necessary component of data management. They not only help streamline the process of retrieving the raw data that will eventually inform application or analytics use, data extraction software makes this process repeatable, automated, and sustainable. Using data extraction tools in a data warehouse is a pivotal move for modernizing these repositories, enabling data warehouses to include web-based sources as well as traditional, on-premise sources. The benefits of data extraction tools include:


Data extraction software is critical for helping organizations collect data at scale. Without these tools, users would have to manually parse through sources to collect this information. Regardless of how much data an organization ingests, its ability to leverage collected data is limited by manual processing. By automating extraction, organizations increase the amount of data that can be deployed for specific use cases.


The automation of data extraction tools contributes to greater efficiency, especially when considering the time involved in collecting data. Data extraction software utilizing options for RPA, AI, and ML considerably hasten identifying and collecting relevant data. Organizations that do leverage data extraction tools substantially reduce the time for data-driven processes, leading to more time for extracting valuable insights out of data.

Business process management

Data extraction software leveraging RPA or different aspects of AI can do more than simply identify and gather relevant data. These options are also useful for inputting that data into downstream processes. For instance, certain RPA options can extract customer data such as phone numbers, emails, or addresses, and initiate processes whereby that information is placed in the appropriate fields on insurance forms. In this respect, data extraction software underpins data entry and business process management.


Data extraction tools are the key to actually identifying which data is necessary and then gathering that data from disparate sources. Organizations understanding this functionality can migrate data from any number of sources into their target systems, reducing reliance on data silos and increasing meaningful interaction with data.  


Data extraction tools often provide a more advanced preparation process that lends its hand to managing complex data streams. This capability combined with the removal of human error and user bias results in increased accuracy and high quality data.


Last but not least, the most obvious benefit relies on data extraction tools’ ease of use. These tools provide business users with a user interface that is not only intuitive, but provides a visual view of the data processes and rules in place. Additionally, the need to hand code data extraction processes are eliminated—allowing for individuals without a programming skill set to extract insights.

One of the most convincing use cases for data extraction software involves tracking performance based on financial data. Extraction software can gather data for metrics such as sales, competitors’ prices, operational costs, and other expenses from an assortment of sources internal and external to the enterprise. Once that data is appropriately transformed and loaded into analytics tools, users can run business intelligence to monitor the performance of specific products, services, business units, or employees.

Data extraction, simplified

Data extraction tools are a vital component of data management. Using an automated tool enables organizations to efficiently control and retrieve data from various origin systems into one central system for future use in single applications and higher-level analytics. More importantly, however, data extraction software provides the essential first step in downstream integration efforts.

Stitch Data Loader provides a fast, fault-tolerant path to data extraction from more than 90+ sources.

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