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What is Data Extraction

Sir Doyle might just have been a visionary when he made Sherlock cry out impatiently “Data! data! data! I can’t make bricks without clay.”

With data becoming the lifeblood for businesses worldwide, data extraction is a vital operation that defines the line between success and failure. Not surprisingly, the global data extraction market that was valued at $2.14 billion in 2019 is projected to reach $4.90 billion by 2027.

Data extraction is the process of acquiring and processing raw data of various forms and types to improve the operational paradigms of an organization. It is perhaps the most important operation of the Extract/Translate/Load (ETL) process because it is the foundation for critical analyses and  decision making processes that are vital to organizations.

It enables consolidation, analysis and refining of data so that it can be converted into meaningful information that can be stored for further use and manipulation. The extracted data can help in decision making, customer base expansion, service improvements, predicting sales and optimizing costs, among other things.  Data extraction can thus help improve productivity and safeguard a company’s core competency.

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Types of Data

Data may be classified according to their source:

• Physical sources: Physical sources of data may include one or more of the following – books, journals, magazines, newspapers, brochures, marketing materials, paper-invoices, paper-POs and letters. The extraction of data from these physical sources is typically manual and strenuous since it involves the efforts of human beings to look into the source, extract the data and input it into the destination. These days, simple digital tools such as OCR – optical character recognition – scanners can lighten some of the burden of data extraction from physical sources.   Most scanners these days have OCR functions built in, to convert printed characters into digital text.
• Digital Sources: Data may be present in digital sources such as word processing files, digital spreadsheets, webpages, e-invoices, digital bills, emails, and online and offline databases.  Data scraping or web scraping are activities that can extract relevant data from these digital sources.

Data are also classified based on their structure at the source:

• Structured Data: When the data source already has a logical structure, it becomes convenient for extraction.  An example is the extraction of phone numbers from a digital directory which is already organized based on a logical scheme.  Data that is stored in a structured format such as a relational database management system (RDBMS) is easy to extract using tools such as  Structured Query Language (SQL). Tools such as SQL can also perform some amount of T (Translate) and L (Load) operations from the ETL system that makes it a particularly powerful tool.
• Unstructured Data: This is the form in which most data exist – as disorganized or unorganized bits of information that must be judiciously sifted and sieved for sensible extraction of data.  The sources of unstructured data could be web pages, editable documents, PDFs, emails, scanned text, spool files etc.

Data extraction from unstructured sources is performed by one of three ways,

• Using text pattern matching to identify small or large-scale structure
• Using a table-based approach to identify common sections, for example using a standard set of commonly used headings; and
• Using text analytics to understand the context of the data.

Finally, data may be classified according to its nature:

• Customer Data: Most service and product providers have a customer database that includes their names, phone numbers, email addresses, identification numbers, and purchase history, and in case of online businesses, their social media activity and web searches.
• Financial data:  These are for accounting processes and include information on transactions, such as sales numbers, cost/price, operating margins and even some amount of competitor information. These types of data help monitor performance, improve efficiencies, and make strategic decisions.
• Performance Data: This is a broad category and could include data related to tasks or operations, such as patient outcomes in the healthcare setting, sales logistics for a trading company, etc.

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There are two types of data extraction techniques:

1. Logical.  This type of extraction is again of two sub-types:

• Full extraction : All data is extracted at the same time, directly from the source without need for additional logical/technological information. It is used when data must be extracted and loaded for the first time.  This extraction reflects the current data available in the source system.
• Incremental extraction: The changes in source data are tracked since the last successful extraction given by the time stamp, and the changes are incrementally extracted and loaded.

2. Physical Extraction

When source systems have certain restrictions or limitations such as being outdated, logical extraction is impossible and data can only be extracted by Physical Extractions. There are two kinds of physical extractions:

• Online Extraction:  There is direct data capture from the source system to the warehouse.  This entails direct connection between the source system and the final repository.  The extracted data is more structured than the source data.
• Offline Extraction: Data extraction takes place outside the source system. The data in such processes can either be structured by itself, or structured via extraction routines.

Data Extraction tools are software that automatically extract data from the source. A good data extraction tools will be capable of extracting data off of a variety of sources such as forms, websites, emails, and more. Such tools are used by businesses to generate leads, collect useful information from public documents and webpages of competitors, identify trends, and improve the analysis of otherwise unstructured information.

Data extraction software may be integrated with data quality software and data preparation software to clean and organize data after scraping. It can also be combined with data integration software so that multiple data types and sources can be aggregated in one place.  To qualify for inclusion in the Data Extraction category, a product must be able to:

• Extract structured, poorly structured, and unstructured data.
• Pull data from multiple sources.
• Export extracted data in multiple readable formats.

There are three kinds of tools that are used for data extraction:

1. Batch processing tools extract data in batches.
2. Open source tools are useful with limited budget and provides basic services that may be sufficient for small companies
3. Cloud-based tools focus on streaming extraction of data as part of the  ETL  The capture is done as and when data becomes available and processed right after, which eliminates any time delays that can be caused by batch processes.

The advantages of automated data extraction include:

1. Improvement of accuracy and reduction of human errors:  Automation can eliminate many of the human errors that are brought about by oversight or fatigue.
2. Time savings:  Automation is undoubtedly faster than manual extraction of data.  Time is often money in businesses and a moment saved could be a moment earned in monetary terms.
3. Freedom from repetitive tasks:  Freeing the employee from mundane data extraction tasks can enable use of their skills for more productive activities. This can improve employee morale and the company bottomline.
4. Better control and access to data:  A centralized location of structured data makes it more accessible to all stakeholders and participants in the business, thereby allowing coherence in business activities.
5. Cost benefits: While the initial investment on automation can be daunting, the cost savings through productivity improvements, employment morale and time savings can more than make up for the setting up costs of automated data extraction systems.
6. Scalability:  Automated data extraction systems offer scope for scaling up of the business without worrying about the volumes of data that would correspondingly be scaled.

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The most common challenges to data extraction processes, especially when it is a part of the ETL system are:

• Coherence of data extracted from various sources, especially if the sources are both structured and unstructured.  AI based data extraction tools can be trained to collate data in a sensible manner that make them suitable for post processing operations.
• Data security is another area that can be challenging in data extraction applications.  Financial data, for example, are highly sensitive and data security must be ensured by organizations that use automated data entry tools for data management.

Many data entry tools like Nanonets, come with a robust technical assistance team that can help overcome the challenges and harness the full potential of automated data entry operations.

Nanonets’ intelligent document processing use cases help organisations adopt automation seamlessly. Here are some interesting case studies:

Update June 2021: this post was originally published in June 2021 and has since been updated.

Here’s a slide summarizing the findings in this article. Here’s an alternate version of this post.