Data Extraction

Data extraction is the process of collecting or retrieving disparate types of data from a variety of sources, many of which may be poorly organized or completely unstructured. Data extraction makes it possible to consolidate, process, and refine data so that it can be stored in a centralized location in order to be transformed. These locations may be on-site, cloud-based, or a hybrid of the two. Data extraction is the first step in both Data extraction is the first step in both ETL (extract, transform, load) and ELT (extract, load, transform) processes. ETL/ELT are themselves part of a complete data integration strategy.

Benefits of Using an Extraction Tool

Companies and organizations in virtually every industry and sector will need to extract data at some point. For some, the need will arise when it’s time to upgrade legacy databases or transition to cloud native storage. For others, the motive may be the desire to consolidate databases after a merger or acquisition. It’s also common for companies to want to streamline internal processes by merging data sources from different divisions or departments.

If the prospect of extracting data sounds like a daunting task, it doesn’t have to be. In fact, most companies and organizations now take advantage of data extraction tools to manage the extraction process from end-to-end. Using an ETL tool automates and simplifies the extraction process so that resources can be deployed toward other priorities. The benefits of using a data extraction tool include:

• More control. Data extraction allows companies to migrate data from outside sources into their own databases. As a result, you can avoid having your data siloed by outdated applications or software licenses. It’s your data, and extraction let’s you do what you want with it.
• Increased agility. As companies grow, they often find themselves working with different types of data in separate systems. Data extraction allows you to consolidate that information into a centralized system in order to unify multiple data sets.
• Simplified sharing. For organizations who want to share some, but not all, of their data with external partners, data extraction can be an easy way to provide helpful but limited data access. Extraction also allows you to share data in a common, usable format.
• Accuracy and precision. Manual processes and hand-coding increase opportunities for errors, and the requirements of entering, editing, and re-enter large volumes of data take their toll on data integrity. Data extraction automates processes to reduce errors and avoid time spent on resolving them.

Types of Data Extraction

Data extraction is a powerful and adaptable process that can help you gather many types of information relevant to your business. The first step in putting data extraction to work for you is to identify the kinds of data you’ll need. Types of data that are commonly extracted include:

• Customer Data: This is the kind of data that helps businesses and organizations understand their customers and donors. It can include names, phone numbers, email addresses, unique identifying numbers, purchase histories, social media activity, and web searches, to name a few.
• Financial Data: These types of metrics include sales numbers, purchasing costs, operating margins, and even your competitor’s prices. This type of data helps companies track performance, improve efficiencies, and plan strategically.
• Use, Task, or Process Performance Data: This broad category of data includes information related to specific tasks or operations. For example, a retail company may seek information on its shipping logistics, or a hospital may want to monitor post-surgical outcomes or patient feedback.