DATA EXTRACTION

What is 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 ETL (extract, transform, load) and ELT (extract, load, transform) processes. ETL/ELT are themselves part of a complete data integration strategy.

Data Extraction and ETL

To put the importance of data extraction in context, it’s helpful to briefly consider the ETL process as a whole. In essence, ETL allows companies and organizations to 1) consolidate data from different sources into a centralized location and 2) assimilate different types of data into a common format. There are three steps in the ETL process:

1. Extraction: Data is taken from one or more sources or systems. The extraction locates and identifies relevant data, then prepares it for processing or transformation. Extraction allows many different kinds of data to be combined and ultimately mined for business intelligence.
2. Transformation: Once the data has been successfully extracted, it is ready to be refined. During the transformation phase, data is sorted, organized, and cleansed. For example, duplicate entries will be deleted, missing values removed or enriched, and audits will be performed to produce data that is reliable, consistent, and usable.
3. Loading: The transformed, high quality data is then delivered to a single, unified target location for storage and analysis.

The ETL process is used by companies and organizations in virtually every industry for many purposes. For example, GE Healthcare needed to pull many types of data from a range of local and cloud-native sources in order to streamline processes and support compliance efforts. Data extraction was made it possible to consolidate and integrate data related to patient care, healthcare providers, and insurance claims.

Similarly, retailers such as Office Depot may able to collect customer information through mobile apps, websites, and in-store transactions. But without a way to migrate and merge all of that data, it’s potential may be limited. Here again, data extraction is the key.

Data Extraction without ETL

Can data extraction take place outside of ETL? The short answer is yes. However, it’s important to keep in mind the limitations of data extraction outside of a more complete data integration process. Raw data which is extracted but not transformed or loaded properly will likely be difficult to organize or analyze, and may be incompatible with newer programs and applications. As a result, the data may be useful for archival purposes, but little else. If you’re planning to move data from a legacy databases into a newer or cloud-native system, you’ll be better off extracting your data with a complete data integration tool.

Another consequence of extracting data as a stand alone process will be sacrificing efficiency, especially if you’re planning to execute the extraction manually. Hand-coding can be a painstaking process that is prone to errors and difficult to replicate across multiple extractions. In other words, the code itself may have to be rebuilt from scratch each time an extraction takes place.

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.

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