Data Extraction Types & Techniques: A Complete Guide
Data extraction is the first and perhaps most important step of the Extract/Transform/Load (ETL) process. Through properly extracted data, organizations can gain valuable insights, make informed decisions, and drive efficiency within all workflows.
Data extraction is crucial for almost all organizations since there are multiple different sources generating large amounts of unstructured data. Therefore, if the right data extraction techniques are not applied, organizations not only miss out on opportunities but also end up wasting valuable time, money, and resources.
In this guide, we will dive into the different types of data extraction and the techniques that can be used for data extraction.
Techniques for Data Extraction
Data extraction can be divided into four techniques. The selection of which technique is to be used is done primarily based on the type of data source. The four data extraction techniques are:
Association data extraction technique operates and extracts data based on the relationships and patterns between items in a dataset. It works by identifying frequently occurring combinations of items within a dataset. These relationships, in turn, help create patterns in the data.
Furthermore, this method uses “support” and “confidence” parameters to identify patterns within the dataset and make it easier for extraction. The most frequent use cases for association techniques would be invoices or receipts data extraction.
Classification-based data extraction techniques are the most widely accepted, easiest, and efficient methods of data extraction. In this technique, data is categorized into predefined classes or labels with the help of predictive algorithms. Based on this labeled data, models are created and trained for classification-based extraction.
A common use case for classification-based data extraction techniques would be in managing digital mortgage or banking systems.
Clustering data extraction techniques apply algorithms to group similar data points into clusters based on their characteristics. This is an unsupervised learning technique and does not require prior labeling of the data.
Clustering is often used as a prerequisite for other data extraction algorithms to function properly. The most common use case for clustering is when extracting visual data, from images or posts, where there can be many similarities and differences between data elements.
Each dataset consists of data with different variables. Regression data extraction techniques are used to model relationships between one or more independent variables and a dependent variable.
Regressive data extraction applies different sets of values or “continuous values” that define the variables of the entities associated with the data. Most commonly, organizations use regression data extraction for identifying dependent and independent variables with datasets.
Types of Data Extraction
Organizations use multiple different types of data extraction such as Manual, Traditional OCR-based, Web scraping, etc. Each data extraction method uses a particular data extraction technique that we read earlier.
Manual data extraction
As the name suggests, manual data extraction method involves the collection of data manually from different data sources and storing it in a single location. This data collection is done without the help of any software or tools.
Although manual data extraction is extremely time-consuming and prone to errors, it is still widely used across businesses.
Web scraping refers to the extraction of data from a website. This data is then exported and collected in a format more useful for the user, be it a spreadsheet or an API. Although web scraping can be done manually, in most cases it is done with the help of automated bots or crawlers as they can be less costly and work faster.
However, in most cases, web scraping is not a straightforward task. Websites come in many different formats and can have challenges such as captchas, etc. to avoid as well.
OCR-based data extraction
Optical Character Recognition or OCR refers to the extraction of data from printed or written text, scanned documents, or images containing text and converting it into machine-readable format. OCR-based data extraction methods require little to no manual intervention and have a wide variety of uses across industries.
OCR tools work by preprocessing the image or scanned document and then identifying the individual character or symbol by using pattern matching or feature recognition. With the help of deep learning, OCR tools today can read 97% of the text correctly regardless of the font or size and can also extract data from unstructured documents.
Template-based data extraction
Template-based data extraction relies on the use of pre-defined templates to extract data from a particular data set the format for which largely remains the same. For example, when an AP department needs to process multiple invoices of the same format, template-based data extraction may be used since the data that needs to be extracted will largely remain the same across invoices.
This method of data extraction is extremely accurate as long as the format remains the same. The problem arises when there are changes in the format of the data set. This can cause issues in template-based data extraction and may require manual intervention.
AI-enabled data extraction
AI-enabled data extraction technique is the most efficient way to extract data while reducing errors. This automates the entire extraction process requiring little to no manual intervention while also reducing the time and resources invested in this process.
AI-based document processing utilizes intelligent data interpretation to understand the context of the data before extracting it. It also cleans up noisy data, removes irrelevant information, and converts data into a suitable format. AI in data extraction largely refers to the use of Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR) technologies to extract and process the data.
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API integration is one of the most efficient methods of extracting and transferring large amounts of data. An API enables fast and smooth extraction of data from different types of data sources and consolidation of the extracted data in a centralized system.
One of the biggest advantages of API is that the integration can be done between almost any type of data system and the extracted data can be used for multiple different activities such as analysis, generating insights, or creating reports.
Text pattern matching
Text pattern matching or text extraction refers to the finding and retrieving of specific patterns within a given data set. A specific sequence of characters or patterns needs to be predefined which will then be searched for within the provided data set.
This data extraction type is useful for validating data by finding specific keywords, phrases, or patterns within a document.
Database querying is the process of requesting and retrieving specific information or data from a database management system (DBMS) using a query language. It allows users to interact with databases to extract, manipulate, and analyze data based on their specific needs.
Structured query language (SQL) is the most commonly used query language for relational databases. Users can specify criteria, such as conditions, and filters, to fetch specific records from the database. Database querying is essential for making informed decisions and building data-driven businesses.
In conclusion, data extraction is crucial for all businesses to be able to effectively retrieve, store, and manage their data. It is essential for businesses to effectively manage their data, gain valuable insights, and create efficient workflows.
The technique and type of data extraction that is used by any organization depends on the input sources and the specific needs of the business and needs to be carefully evaluated before implementation. Otherwise, it can lead to unnecessary wastage of both time and resources.
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