Streamline Your Business Processes With The Power Of Automated Data Extraction

Introduction 

“The global data extraction market was valued at $2.14 billion in 2019, and is projected to reach $4.90 billion by 2027, growing at a CAGR of 11.8% from 2020 to 2027”. 

Source: https://www.alliedmarketresearch.com/data-extraction-market-A06797 

Considering the humongous drive for digital transformation, data and analytics have become essential components of all operations. The demand for high-quality data is increasing, allowing for more informed decisions and a competitive advantage. As a result, there is a strong incentive to develop automated data extraction solutions to streamline the process and eliminate errors. 

In this article, we will deep dive to understand data extraction from documents, discuss Automated Data Extraction, learn about different data extraction techniques, and assist you in determining which technique is best for you. 

Automated data extraction: Meaning 

Data extraction is the procedure of converting unstructured or semi-structured data into structured information. This structured data presents companies with valuable information that can be used for analysis and reporting purposes. 

Essentially in data extraction, raw data is obtained from any source and is replicated at another device. The raw data can come from a database, an Excel spreadsheet, a SaaS platform, web scraping, or other sources. It can then be replicated to a data warehouse designed to support online analytical processing (OLAP), such as a data warehouse. 

Automatic Data Extraction: How important is it? 

Enterprises should reconsider their data management strategies in order to remain current in the modern business world. Automatic data extraction tools with advanced capabilities can evaluate documents, extract data, and analyze it quickly and accurately without error or human bias. Also, most of these programs support multiple data formats and come with user-friendly interfaces. 

Automated data extraction: Fast forward the legacy approach 

It is no surprise that data is critical for any business organization looking to scale up. However, raw data is typically presented in an unstructured or semi-structured format that is useless unless insights are extracted from the datasets. 

Source: https://www.alliedmarketresearch.com/data-extraction-market-A06797 

It is a daunting task for organizations to gather data from multiple sources. Emails, PDFs, images, invoices, paper files, contracts, financial statements, and so on are examples. Every bit of data shared in these documents is valuable enterprise data, only when it is extracted, processed, analyzed, and made available in real time. This task is beyond the capabilities of the average person. Regardless of how efficient they are, completing the process in record time is almost unrealistic. 

Automated data extraction: Benefits 

Automated solutions are intended to reduce time, labor, and error factors while also speeding up the process so that quality and accurate data are available when needed. Data extraction automation is a technologically advanced solution that can benefit businesses in a variety of ways. Given the increasing market competition, AI and ML based data extraction and processing platforms have become critical. 

•  Saving time and resources 

•  Reducing errors and inaccuracies 

•  Improving data quality and consistency 

•  Enabling data-driven decision making 

•  Streamlining business processes 

• Operational efficiency 

• Compliance with deadlines 

Understanding Automated Data Extraction and ETL 

The first step in the ETL (Extract, Transform, and Load) is data extraction. 

Extraction gathers data from one or more sources and is just one component of the overall ETL process. It locates and identifies relevant data before preparing it to be transformed and loaded. 

Transformation is the process of sorting and organizing data. This step also includes cleaning, such as removing missing values. Depending on the data destination, data transformation may include data typing, object names, and time zones to ensure compatibility. 

Loading is the final step in which the transformed data is made available to a central data warehouse for current or long-term analysis. 

Data Extraction Automation Process 

Data extraction from documents is a complex process that involves mining raw data from documents for further analysis. 

Extraction tools import documents into the digital platform of choice in order to create digital versions of the same documents as well as scan and capture the required data. 

Data collected is typically stored in shared cloud storage, allowing for easy retrieval as needed. 

Furthermore, data can be easily uploaded and annotated, and AI models can be trained for better data understanding. 

Depending on the type of data sought, there are two types of data extraction. 

Incremental extraction entails using complex logic to account for changes in datasets. And this function necessitates the addition of timestamps to datasets. This application, for example, assists in tracking inventory changes since the last extraction. 

Complete extraction denotes data extraction from its source without the addition of variables. Of course, baseline data is required for the tool to search for similar patterns and refine the extracted datasets. Some auto-data extraction tools include mechanisms that notify users of potential changes in the data after the previous extraction. In such cases, incremental extraction is unnecessary. 

Data Extraction Automation: Challenges and Solution 

The primary challenge for automated data extraction is extracting data from various document types. The context and structure of such documents vary greatly. Basic OCR tools are incapable of extracting information from unstructured documents, which are mostly enterprise documents. Furthermore, the layout structure varies in structured or semi-structured documents. Then there are visually-rich documents to deal with, where the layout and images contain critical data related to understanding the document's overall context. 

Fortunately, advanced technologies such as computer vision can help overcome these obstacles. These tools collect relevant information regardless of where it appears in the document, whether it is in the form of words or images. 

Another concern for businesses using automated data extraction tools is data security. Enterprise documents, for example, may contain financial statistics or confidential information about clients or partners. These sensitive data should be safeguarded by a strong security infrastructure and a technical assistance team. 

How to choose the right tool for your business needs 

Companies can use Data extraction tools for a variety of purposes, such as marketing plans or data analysis. As many options are available, it can be difficult to choose. Several factors must be considered to ensure that you choose the correct tool. 

The expensive tool may not always be the right one. Choosing the right tool entails investing in your company or organization. You want to ensure that every dollar invested in data extraction yields a return on investment. 

Different automated data extraction tools are designed for different types of information. Some specialize in extracting data from PDFs or other more complex documents that are difficult to read with other tools. 

Data extraction and parsing speed - Because your time is valuable, you will an automated data extraction tool that can quickly process large amounts of will be the right choice for your business. 

Usefulness - If your business needs extraction of voluminous data from a single database, you should look into heavyweight tools that help you perform job quickly. Lightweight devices are better suited for extracting a restricted quantity data from multiple sources. 

Automated Data Extraction: Use Cases 

Data Extraction Automation has far-reaching implications for all organizations, regardless of sector or industry. 

KYC and customer onboarding: It takes time to process customer documents for KYC validation. With AI-enabled tools, document classification, capture, and processing take less time, resulting in faster customer onboarding. 

Claims and EOB processing: These tools aid in the accurate classification and extraction of unstructured data from emails, claim forms, carrier custom forms, explanation of benefits (EoB), and supplementary documents. 

Invoice processing: Invoice processing is a recurring task that is essential to every business organization. Automated invoice capture and extraction saves thousands of hours of processing time. 

Improved customer service: To provide personalized service, contact centre agents require immediate access to customer data. The required insights are made available as and when required by AI data extraction tools, resulting in incredible customer experiences. 

Final Words 

The potential of automated data extraction looks brighter as new AI capabilities are discovered to make the process more streamlined and faster. As previously stated, data is essential to every business process; its timely availability can improve business strategies while resources are better utilized to gain maximum value. Furthermore, nearly every industry face data management challenges. As a result, data automation and AI solutions have a broader range of applications, ranging from manufacturing to healthcare, banking to retail, and so on.