What is Data Extraction? Definition and Examples

We have access today to more data than ever before. The question is: how do we make the most of it? For many, the biggest challenge lies in finding a data integration tool that can manage and analyze many types of data from an ever-evolving array of sources. But before that data can be analyzed or used, it must first be extracted. In this article, we define the meaning of the term “data extraction” and examine the ETL process in detail to understand the critical role that extraction plays in the data integration process.

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.

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.

Once you’ve decided on the type of information you want to access and analyze, the next steps are 1) figuring out where you can get it and 2) deciding where you want to store it. In most cases, that means moving data from one application, program, or server into another.

A typical migration might involve data from services such as SAP, Workday, Amazon Web Services, MySQL, SQL Server, JSON, SalesForce, Azure, or Google Cloud. These are some examples of widely used applications, but data from virtually any program, application, or server can be migrated.

Data Extraction in Motion

Ready to see how data extraction can solve real-world problems? Here’s how two organizations were able to streamline and organize their data to maximize its value.

Domino’s Big Data

Domino’s is the largest pizza company in the world, and one reason for that is the company’s ability to receive orders via a wide range of technologies, including smart phones, watches, TVs, and even social media. All of these channels generate enormous amounts of data, which Domino’s needs to integrate in order to produce insight into its global operations and customers’ preferences.

To consolidate all of these data sources, Domino’s uses a data management platform to manage its data from extraction to integration. Running on Domino's own cloud-native servers, this system captures and collects data from point of sales systems, 26 supply chain centers, and through channels as varied as text messages, Twitter, Amazon Echo, and even the United States Postal Service. Their data management platform then cleans, enriches and stores data so that it can be easily accessed and used by multiple teams.

Advancing Education with Data Integration

Over 17,000 students attend Newcastle University in the UK each year. That means the school generates 60 data flows across its various departments, divisions, and projects. In order to bring all that data into a single stream, Newcastle maintains an open-source architecture and a comprehensive data management platform to extract and process data from each source of origin. The result is a cost-effective and scalable solution that allows the university to direct more of its resources toward students, and spend less time and money monitoring its data integration process.

The Cloud, IoT, and The Future of Data Extraction

The emergence of cloud storage and cloud computing has had a major impact on the way companies and organizations manage their data. In addition to changes in data security, storage, and processing, the cloud has made the ETL process more efficient and adaptable than ever before. Companies are now able to access data from around the globe and process it in real-time, without having to maintain their own servers or data infrastructure. Through the use of hybrid and cloud-native data options, more companies are beginning to move data away from legacy on-site systems.

The Internet of Things (IoT) is also transforming the data landscape. In addition to cell phones, tablets, and computers, data is now being generated by wearables such as FitBit, automobiles, household appliances, and even medical devices. The result is an ever-increasing amount of data that can be used drive a company’s competitive edge, once the data has been extracted and transformed.

Data Extraction on Your Terms

You’ve made the effort to collect and store vast amounts of data, but if the data isn’t in a readily accessible format or location, you’re missing out on critical insights and business opportunities. And with more and more sources of data appearing every day, the problem won’t be solved without the right strategy and the right tools.

Talend Data Management Platform provides a comprehensive set of data tools including ETL, data integration, data quality, end-to-end monitoring, and security. Adaptable and efficient, Data Management takes the guesswork out of the entire integration process so you can extract your data when you need it to produce business insights when you want them. Deploy anywhere: on-site, hybrid, or cloud-native. Start your free trial today to see how easy it can be to extract your data on your terms.

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