What Is Data Extraction? (With Types and Processes)

By Indeed Editorial Team

Published 23 October 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Extracting data refers to transferring data from a database or software as a service (SaaS) platform to a specified destination, usually a data warehouse or another database. This extraction phase is typically part of a process known as the extract, transform and load (ETL) process. Exploring the extraction phase of this data transformation process can help you extract data efficiently for business intelligence (BI) procedures. In this article, we define data extraction, list the types of data commonly extracted, discuss the extraction process and list the different extraction methods.

What's data extraction?

Data extraction is the first stage of the ETL process where a business obtains data through various sources. These sources are usually databases or application platforms. There are many types of data businesses may extract, such as customer and financial data. There are also varying extraction methods, but the fundamental process of obtaining data typically remains the same. The purpose of extracting data is typically to centralise all the necessary information for analysing business performances and operations from a single database.

Large enterprises may have extensive data spread across different databases and applications. For example, a business may have sales data, online reviews, social media mentions and business-to-business (B2B) transactions. Reviewing these data sets from their original sources, such as the sales application, social media website and reviewing platform, can take a substantial amount of time. If a business extracts these data sets into a centralised location, it can review the data from a single platform, saving time and allowing business analysts to analyse data.

Related: A Guide to Data Analysis (With a Definition and FAQs)

Types of data businesses extract

Below, you can explore the types of data categories businesses commonly extract for analysis:

Customer data

Customer data usually represents information about customer purchase history, contact information and web searches. Businesses may analyse this data to identify their primary demographic and forecast purchasing trends in their market. For example, a company might analyse its customers' average purchasing trends and determine the most popular product in the near future. The business can then market the forecasted product to increase sales.

Related: Satisfied Customers: Their Importance and How to Track Them

Financial data

This is usually internal business data, such as operational costs and revenue. Businesses may analyse internal financial data to forecast future performance and maintain compliance with financial regulations. For example, a business may analyse its internal financial data to ensure the accuracy of its bookkeeping and financial records. It may also analyse its data to identify trends in operational performance, such as relationships between revenue and costs.

Related: What Is Financial Reporting and What Are Its Uses?

Product data

Product data typically refers to all information about a product's development and performance. The data can include specifications, manuals, instructions, performance indicators and materials. This data typically helps a business identify a product's performance and maintain its compliance with industry regulations. For example, a business may analyse a product's production cost and cross-reference it with sales data to determine if it can generate revenue. They may also review the production quality, such as the number of defects and the cost of poor quality (COQP), to determine if the production process is efficient.

Related: A Step-By-Step Guide to the Product Life Cycle (With Examples)

Performance data

Performance data typically comprises key performance indicators (KPIs). You can essentially create a KPI for any process and procedure, so the extent of performance data can depend on a business's performance evaluation methods. There may be KPIs for many departments, such as production, sales, human resources, employee relations and marketing. Performance data can help businesses identify process efficiency and productivity and determine change requirements to improve performance. For example, a business may analyse production KPIs and discover the production process creates too many defects, outlining the need for more quality control measures.

Related: What Is a Performance Indicator? (And How to Create One)

What is the data extracting process?

It's usually helpful to understand that there are several data extracting methods, but they all typically follow a similar process. The extraction process outlines the technical steps required for machines to obtain and prepare data for the ETL process. Extraction methods, though, typically outline the frequency and amount of data extracted. Here are more details on the typical process that most data extracting methods follow:

Extracting data

The extraction process is where you find and obtain relevant data. It can be helpful to understand that you usually use specialised tools for extracting, transforming and loading data. Conducting these duties manually may be inefficient, as you can't automate processes or scan data at high rates. When using extraction tools, you can set rules for the tool to follow, outlining the types of data and sources to extract information. This can ensure you only extract relevant data. For example, you can set tools to scan internal financial data and only obtain the data about costs.

Related: ELT vs. ETL (With Definitions and Advantages)

Preparing data

The final step in the extraction process is to prepare the data for transformation and loading. During this phase, you typically use machine learning tools to reformat data, remove discrepancies and combine data sets. The data extracted during the extraction phase is usually raw, meaning it's typically difficult for humans to comprehend and analyse the data. This preparation phase refines the data, readying it for transformation and loading it into a centralised database. Depending on the extraction process a business uses, this data preparation phase might entirely replace the transformation phase in the ETL integration method.

Related: Data Engineering Tools: Definition, Examples and FAQs

Types of data extracting methods

As mentioned before, most data extracting methods follow the above process. What differentiates these methods from each other is the frequency, amount and type of data extracted. Here are common data extracting methods:

Update notification

This is typically the most dynamic and regular extraction method. The update notification extraction method is where a machine receives a notification every time a data source receives new data or someone alters data. The machine then updates the database by extracting the altered or additional data. For example, if someone changes the price of a product, a machine automatically extracts that data to the business's online store website, changing the price of the product displayed on the website.

Related: Discover Analytics of Data, Its Benefits and How to Use It

Structured extraction

This data extracting method is usually for extracting a single type of data. For example, if you're only extracting data in a table format, you may use this methodology to easily obtain this data in an already comprehensible structure. If you export data in a table format, you can immediately visualise it in a spreadsheet application.

Unstructured extraction

This is essentially the opposite of structured extraction. In unstructured extraction methods, you usually obtain data in varying formats, such as images, tables, videos and text. This extraction method can be helpful when obtaining large variations in data, but it may require extensive preparation and transformation before it can load into a data warehouse.

Related: How to Extract Images from PDF Files: Methods with Steps

Incremental extraction

This extraction style is when you regularly extract data at standardised intervals, such as every week or month. For example, a retail store might update its database every week or fortnight with the latest sales and product information. The purpose of this extraction method is usually because daily or continuous extraction is unnecessary and doesn't benefit the business.

Full extraction

This full extraction method is usually for first-time data transfers between systems and applications. For example, if a business implements a new sales application, it might have no data about the business's sales history. A business may conduct a full extraction to load all relevant data currently stored in the data warehouse onto the application. This can be a time-consuming process, which is why businesses usually conduct this method once. After a full extraction, they may use an incremental or update notification method to update the application with new data.

Related: What Are Data Integration Tools? (With Benefits and Tips)

Online extraction

The online extraction method refers to where a business obtains its data rather than the frequency or amount of data. For example, an online extraction may be a business using the full extraction method to obtain all data from a cloud-based database. When conducting an online extraction, there's usually a direct connection between the source database and the destination database. This means the data extracted is usually in a structured format.

Offline extraction

An offline extraction method is when you retrieve data from an offline source. The data is usually a copy of online data that's accessible from outside the data source. For example, a cloud-based application may only be accessible online, but the application may send copies of its data to an offline source, such as an on-premise database. You can then extract data from this offline source. This extraction method essentially mitigates the need for connection to online databases.

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