Data extraction is the process of transforming unstructured or semi-structured data into structured information. Structured data provides companies with meaningful insights to be available for reporting and analytics.
Data extraction helps consolidate, process, and refine information so that you can store it in a centralized location for further analysis and record-keeping. Data extraction is the initial step in ETL (extract, transform, load) as well as ELT (extract, load, transform) processes.
Data extraction facilitates companies to migrate data from documents, credentials, and images into their databases. This feature helps avoid having your data siloed by obsolete applications or software licenses. Let's have a look at some use cases of data extraction in different industries:-
Real estate investors analyze historical sales data for a specific property and compare it with similar other properties on distinct parameters to estimate the investment potential. Most property managers extract this historical data from various document types and categorize them in a structured manner before comparison. However, manual extraction is susceptible to all kinds of errors, thus resulting in inaccurate data sets and erroneous estimates.
Logistics service providers extract and analyze heaps of data from invoices, bills of ladings, as well as other documents, and manually feed in updates to the TMS or ERP. Commodity traders, shippers, food producers, and logistics providers are required to process hundreds of Bill of Lading documents every day. With this process being executed manually, it is prone to human errors and delays.
As a property manager, you might have your desk or email inbox flooded with applications for properties that you manage. Weeding through all the paperwork to extract the core information that differs from application to application can get extremely tedious. Such credentials hold the utmost significance, and thus, the sensitive information must be handled scrupulously.
Today, a large number of invoices are sent in PDF format via fax or email. An individual manually inputs the data into their ERP platform, Excel sheets, or any preferred software program.
However, since enterprises send and receive thousands of invoices every day, it becomes unavoidable to have automated accounts payable solutions to alleviate the load of manual entry and make the payable workflow system quicker, boost accuracy, and make it error-free.
The process of data extraction acquires data from source systems and stores the extracted data in a ‘data warehouse’ for further examination.
There are two options for extraction methods -
Establishing a visual integration flow is imperative when extracting data logically. It helps developers devise a physical data extraction plan.
With the logical map in place, you must decide on which extraction approach to choose -
All data gets extracted directly from the source system in its entirety. You don't have to account for any logical data such as timestamps to be associated with source data, since you are copying everything contained in the source system, entire tables in one go.
For instance, assume that your source database has 500 records or more. The process would be faster if you use the SELECT and FROM database commands to copy the table.
If you include the WHERE clause on timestamps, extraction would take more time to begin, according to the size of the table and if the timestamp column is indexed.
Data gets extracted in increments using this approach. This approach extracts data that has been altered or added post a well-defined event in the source database.
Well-defined events mean anything that is trackable within the source system via timestamps, triggers, or a custom extraction logic built within the source system.
In transactional operations, common master tables such as Product and Customer comprise millions of records, making it illogical to perform full extraction every time and analyze the previous extraction with the new copy to mark the changed data.
A physical extraction performs a bit-by-bit copy of the full contents of the flash memory of a mobile device. This extraction technique enables the collection of all live data as well as data that is hidden or has been deleted. By creating a bit-by-bit copy, deleted data can get potentially recovered.
Source systems typically have certain restrictions or limitations. For instance, extracting data from obsolete data storage systems through logical extraction is inconceivable. Data extraction from such systems is only feasible via Physical Extraction, which is classified further into Online and Offline Extraction.
When picking a data extraction solution for your business, you should be careful about different features that different platforms have to offer as something that might work for one company may not work for the other. Therefore, you must have the following parameters in mind when making a purchasing decision:-
The data extraction tool must be able to extract data without losing information from different document types such as contracts, delivery notes, accounts payable, and more, and be able to categorize them in their respective blueprints.
Companies prefer a data extraction tool that delivers swift results; however, it must also be high in terms of accuracy. The extracted output must retain information, and the tool must be able to extract tables, fonts, and crucial parameters without compromising the layout.
Pick a data extraction platform that offers secure storage along with seamless backup options. Cloud-based extraction enables you to extract data from websites seamlessly at any time.
Cloud servers can swiftly extract data relative to a single computer. The quickness of automated web data extraction affects the speed of your reaction to any rapid events that impact your enterprise.
Advanced automated data extraction software must operate on a simplistic UI. The layout of the software interface at launch must be simple enough to navigate you through executing a grinding task. Besides providing an easy-to-use UI experience, the platform must also not compromise on the essential features.
Pricing might not be the most crucial factor, but it is a thoughtful consideration. It might not be a wise decision to invest in exorbitantly expensive software with extravagant features that do not apply to your company or choose the wrong pricing plan. Consider evaluating the features of the software while ensuring that the cost stays within your budget.
Data extraction is a crucial process to automate structured data collection and use them for further analysis. If your business seeks to employ an automated data extraction solution in your system, make sure that it is capable enough to adapt to your use-case yielding a higher impact on the workflow.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
With an automated data extraction solution, loan documents can automatically be processed end-to-end without any human errors and delays. Automation in loan document processing prevents downtimes, eliminates data redundancy, and allows companies to respond faster to client queries. By combining machine learning with deep learning and OCR, companies can eliminate huge costs, derive actionable insights, and streamline loan processing and approvals through efficient data extraction and analysis.
Mortgage lenders receive multiple identity and income verification documents along with different forms from loan applicants in a variety of formats and styles. Traditional OCR solutions fail to extract data from these semi-structured documents and that’s why more and more lenders are adopting intelligent document processing solutions. IDP solutions not only extract data correctly, they are able to validate extracted data against predefined rules in order to improve accuracy.
Intelligent Document Processing is an automation technology that captures information from a myriad of documents and data sources, extract data, and organizes it for further processing. IDP solutions enable businesses to seamlessly integrate with core processes, eliminate manual labour, address challenges faced in reading different document layouts, and meeting legal & compliance requirements. Accurate data is the foundation of every organization, and IDP assists businesses in dealing with the complexity of processing huge volumes of documents, helping them automate manual data entry processes, and move away from traditional semi-automated OCR workflows.