What is Data Extraction? Definition, How it Works, and Techniques

You collected all sorts of data for your business, but now it’s trapped! It is lying in your social media accounts, POS systems, locked PDFs, contact lists, and other databases.

So, how do you feed this data into your analytics platform, and that too, in a timely manner? As important as it is to combine data sources, what matters more is how quickly and accurately you can extract data from them so it’s ready for analysis.

Did you know that 68% of business data is not utilized at all? One of the main reasons for this is that the needed data is never extracted, which highlights the importance of data extraction in any data-driven organization.  If you can get this first step right, you can lay a strong foundation for the rest of your data pipeline.

What is Data Extraction?

Data extraction is the process of retrieving or pulling data from various sources and converting it into a usable and meaningful format for further analysis, reporting, or storage. It is one of the most crucial steps in data management, allowing you to feed data into databases, applications, or data analytics platforms downstream.

Data can come from various sources, including databases, spreadsheets, websites, application programming interfaces (APIs), log files, sensor data, and more. These sources may be structured (organized into tables or records) or unstructured (textual or non-tabular data).

Data extraction also serves as the first step in extract, transform, load (ELT) and extract, transform, load (ETL) processes, which organizations rely on for data preparation, analysis, and business intelligence (BI).

Data extraction is relatively easy when dealing with structured data, such as tabular data in Excel files or relational databases. However, it’s better to use specialized data extraction software when dealing with unstructured data sources, such as PDFs, emails, images, and videos.

The Importance of Extracting Data

As discussed, extraction is the first step in both ETL and ELT processes, which themselves are crucial for data integration strategies. Let’s look at some other reasons data extraction is important for all data-related activities:

It Improves Data Accessibility

Data extraction addresses a significant challenge by improving data accessibility, which leads to more empowered users who can use data without relying on IT resources. Each organization deals with disparate data sources, and all the data is in different formats. Data extraction pulls all the data together, converts it into a standardized format, and then puts it in a centralized source for everyone to use as and when needed.

It Ensures Effective Data Utilization

Data extraction serves as a critical first step in data integration and management as the foundation for data analysis, data transformation, and effective data utilization. Organizations can consolidate information into a unified, centralized system for further processing by extracting data from diverse sources, such as databases, APIs, or unstructured formats like PDFs and web pages.

It Improves Decision-Making

Accurate and efficient data extraction ensures timely access to reliable information, offering decision-makers a unified view of their operations. This is crucial for strategic planning, identifying trends, and improving performance. Without accurate and efficient data extraction, downstream processes like analytics, reporting, and business intelligence (BI) platforms would lack reliable inputs, leading to suboptimal outcomes.

It Facilitates Seamless Integration

Data extraction facilitates seamless integration across platforms and systems, bridging the gap between legacy systems and modern solutions while ensuring data interoperability and consistency. For example, in enterprise resource planning (ERP) or customer relationship management (CRM) systems, effective data extraction ensures that all relevant information is synchronized, reducing redundancies and errors.

Data Extraction in Action: Real Life Examples

Ciena x Astera: How a networking company automated data extraction

Ciena Corporation, a networking industry pioneer, receives purchase orders in PDF format and was facing delays in order fulfillment due to the manual effort required to transcribe and verify order details. To automate data extraction and save time, Ciena evaluated various solutions and found Astera to be the best fit. As a result, Ciena is now fulfilling customer requests 15x faster and can process purchase orders in just 2 minutes instead of several hours.

Garnet Enterprises x Astera: How a hardware supplier automated data extraction

Garnet Enterprises, a hardware wholesaler and retailer based in Australia, relied on manual data entry, a time-consuming and labor-intensive process. The manual process also limited their ability to generate reports. In Astera, Garnet found a PDF data extraction tool that was not just cost-efficient but effective too. With Astera, Garnet Enterprises was able to reduce time and cost significantly by automating its entire data extraction process.

Aclaimant x Astera: How a risk management platform cut manual data entry time

Aclaimant is a risk reduction and incident management platform that was facing the challenge of manually extracting data from claim forms in PDF format and converting it into a report in Excel format for a centralized view of the claims’ progress. With Astera’s data extraction capabilities, Aclaimant reduced the data extraction time considerably and saved as much as 50% in data extraction and report preparation time.

How does Data Extraction work?

Identifying Data Sources

The data extraction process starts with identifying data sources. You need to be clear on what data you need and where your data is located. It can be in documents, databases, or social media apps.

Once you have identified your data sources, you need to select the appropriate method for each source. For images, you might need OCR; for websites, you might need web scraping software, and so on and so forth.

Source Connection

After that, you need to establish a connection to selected data sources. The connection method may vary depending on the source type. For databases, you may use a database connection string, username, and password. For web-based sources, you may need to use APIs. Some data extraction software solutions offer a complete solution with various inbuilt connectors so you can connect to all sources simultaneously.

Query or Retrieval

You can use SQL queries to retrieve specific data from tables for databases. Documents may require text extraction using OCR or specific document parsers. However, most data extraction tools are now AI-powered and code-free, which means all you need to do is just drag and drop a connector and connect to any data source without learning extensive SQL queries or programming languages.

Data Transformation and Loading

Once the data is extracted, it often doesn’t comply with the format required by the end destination or even for analysis. For example, you could have data in XML or JSON, and you might need to convert it into Excel for analysis. There could be multiple scenarios, which is why data transformation is essential.

Some common transformation tasks include:

• Cleaning data to remove duplicates, handle missing values, and correct errors.
• Normalizing data by converting date formats or standardizing units of measurement.
• Enriching data by adding external information or calculated fields.

The transformed data is then fed into a destination, which varies according to the objective of the data.

The Role of Data Extraction in ETL and Data Warehousing

ETL (Extract, Transform, Load), is a comprehensive data integration process that includes extracting data from source systems, transforming it into a suitable format, and loading it into a target destination (e.g., data warehouse). Data extraction plays a crucial role in ETL pipelines.

Efficient and accurate data extraction is essential for maintaining data integrity and ensuring that the downstream ETL stages can effectively process and utilize the extracted information for reporting, analytics, and other data-driven activities.

Organizations in practically every sector utilize the ETL process for data integration for purposes like reporting, BI, and analytics. Although extraction is the first step, it’s also the most important one as it lays the foundation for seamless and effective data integration.

For instance, a healthcare company needs to pull different types of data from various local and cloud sources to streamline its operations. Accurate data extraction makes it possible to consolidate and integrate all patient data from different sources.

Enhance Accuracy and Efficiency in Data Extraction

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Data Extraction Vs. Data Mining

Data extraction and data mining are often used interchangeably but are different concepts. As discussed earlier, data extraction is collecting data from different sources and preparing it for analysis or storage in a structured database. Data mining, on the other hand, is the process of discovering patterns, trends, insights, or valuable knowledge from a dataset.

It is all about applying various statistical, machine learning, and data analysis techniques to extract useful information from data. The primary goal of data mining is to uncover hidden patterns or relationships within data and then use them for decision-making or predictive modeling.

What Are The Data Extraction Techniques?

There are various data extraction techniques; however, the most suitable technique for your organization depends on your particular use case. Here are some of the primary methods:

Web Scraping 

Web scraping is used to collect data from various online sources, such as e-commerce websites, news sites, and social media platforms. Web scraping software access web pages, parse HTML or XML content, and extract specific data elements.

API-Based Extraction

Many web services provide APIs that allow developers to retrieve data from apps in a structured format. API-based extraction involves sending HTTP requests to these APIs and then retrieving data. It’s a reliable and structured way to extract data from online sources, such as social media platforms, weather services, or financial data providers.

Text Extraction (Natural Language Processing – NLP)

Text extraction techniques often use natural language processing (NLP) to extract information from unstructured text data, such as documents, emails, or social media posts. NLP techniques include named entity recognition (NER) for extracting entities like names, dates, and locations, sentiment analysis, and text classification for extracting insights from text.

OCR

Optical Character Recognition (OCR) converts printed or handwritten text from documents, images, or scanned pages into machine-readable and editable text data. An OCR software analyzes processed images to recognize and convert text content into machine-readable characters. OCR engines use various techniques to identify feelings, including pattern recognition, feature extraction, and machine learning algorithms.

Document Parsing 

Document parsing is when a computer program or system extracts structured information from unstructured or semi-structured documents. These documents can be in various formats, such as PDFs, Word files, HTML pages, emails, or handwritten notes. The parsing system identifies the document’s structure. Then, it extracts the relevant data elements, including names, addresses, dates, invoice numbers, and product descriptions, based on specific keywords, regular expressions, or other pattern-matching methods.

AI-Powered Data Extraction

AI data extraction refers to the use of AI technologies to extract data from various data sources. AI data extraction is particularly useful for extracting data from unstructured data, whether it’s in the form of text, images, or other non-tabular formats. While the exact use of AI technologies differs between data extraction solutions, technologies like machine learning (ML), large language models (LLMs), and retrieval-augmented generation (RAG) are typically leveraged to automate manual tasks, improve accuracy, and increase overall efficiency.

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Data Extraction Types

Once you have your data sources in place and you have decided which technique or techniques work, you need to set a system for your data extraction to work. You can choose from either manual data extraction, full data extraction, or incremental data extraction. Let’s see the pros and cons of each type of data extraction:

Full Extraction:

Full extraction, or a full load or refresh, extracts all data from a source system in a single operation. You can use this technique when the source data doesn’t change frequently, and a complete and up-to-date copy of the data is essential. Full data extraction, however, can be resource-intensive, especially for large datasets, as it retrieves all data regardless of whether the data has changed since the previous extraction. It is often the best choice as an initial step in data warehousing or data migration projects.

Incremental Extraction:

Incremental extraction, also called delta extraction or change data capture (CDC), is used to extract only the data that has changed since the last extraction. It is the best choice when dealing with frequently changing data sources, such as transactional databases. Also, it’s more efficient than full extraction because it reduces the amount of data transferred and processed. Common methods for incremental extraction include timestamp-based tracking, version numbers, or using flags to mark updated records.

Manual Extraction:

In the past, most organizations used to extract data manually. Some still copy and paste data from documents, spreadsheets, or web pages into another application or database. However, manual extraction is time-consuming, error-prone, and inevitably unsuitable for large-scale data extraction tasks. Still, it can be helpful for occasional or ad-hoc data retrieval when automation is difficult.

Common Data Extraction Challenges

You would think that with advancements in technology, data extraction might have become easier. However, businesses still need help with data extraction challenges. Here are some common challenges that you should keep in mind while implementing data extraction processes:

Data Source Variety

Do you know that a business draws data from 400 sources on average? All these sources have a different format, structure, and access method, which makes it challenging to extract data and that too on time. According to a survey conducted by IDG, this explosion in data sources creates a complex environment that stalls projects; in fact, 32% of the people surveyed pointed out that they need help connecting to the data sources.

Data Volume

64% of organizations today manage at least one petabyte of data, with up to 41% of organizations managing up to 500 petabytes of data. So, it is not just the variety of data sources that is a challenge, but data volume as well.

Moving large volumes of data from source systems to a central repository can take time, mainly if the organization’s network bandwidth is limited. Moreover, managing large volumes of data also means potential data governance issues.

Data Complexity

We have talked about high volumes of data and a variety of data sources, but it doesn’t end there—data today is more complex than ever. Gone are the days when it was just stored in two tables in Excel. Today, you will find hierarchical data, JSON files, images, PDFs, etc. On top of it, all of this data is interconnected.

For example, in social network data, individuals are connected through various types of relationships, such as friendships, follows, likes, and comments. These relationships create a web of interconnected data points. Now imagine extracting these data points, and then fitting them in a schema.

Error Handling and Monitoring

Error handling and monitoring are crucial aspects of data extraction, as they ensure the reliability and quality of extracted data. It is even more critical in real-time data extraction when data requires immediate error detection and handling.

Scalability

Many organizations require real-time or near-real-time data extraction and analysis. As data streams continuously, the systems must keep up with the pace of data ingestion, which is why scalability is essential. When setting up your infrastructure, you need to ensure that it can handle any growth in data volume.

Automation Through AI: The Need of the Hour

Given that data has become more complex, the way to solve data extraction challenges is to employ a data extraction tool that can automate most of the tasks. That’s where AI comes into the picture. Here are some of the benefits of using an AI-powered data extraction tool over manual data extraction:

1. Handle Multiple Data Sources: Data extraction tools come with built-in connectors, which make it easier to connect to all data sources at once. Plus, today’s tools are equipped with AI capabilities that can extract data from unstructured documents within seconds.
2. AI-Powered OCR: While OCR has been in use for quite some time, combining it with AI allows modern data extraction tools to not only increase efficiency but also improves accuracy considerably, regardless of the file type or format.
3. Scalability: The best part about data extraction tools is that they can scale to handle large volumes of data efficiently without requiring extra resources. They can extract and process data in batches or continuously to accommodate the needs of businesses with growing data requirements.
4. Data Quality: Many data extraction tools include data quality features, such as data validation and cleansing, which help identify and correct errors or inconsistencies in the extracted data.
5. Automation: Data extraction tools can be scheduled to run at specified intervals or triggered by specific events, which reduces the need for manual intervention and ensures that data is consistently updated.
6. AI Mapping: With AI data mapping, modern data extraction solutions like Astera can help enterprises extract and map data accurately and effortlessly.

Seamlessly Extract Your Valuable Data with Astera

Data extraction is the fundamental step of the entire data management cycle. As technology advances and data sources grow in complexity and volume, the field of data extraction is also evolving.

So, it is essential to keep up with new tools and industry best practices.

That’s where Astera comes in with its no-code AI-powered data extraction solution, allowing you to extract data effortlessly without a) spending hours on repetitive tasks, b) requiring any coding knowledge, and c) repeating extraction tasks every time a new doc comes in.

Astera’s next-gen AI-powered technology enables up to 90% faster data extraction, 8 times faster document processing, and a 97% reduction in extraction errors.

Want to get started with AI-powered data extraction? Download free trial or contact us for a customized demo today and let AI extract data for you within seconds.