In an age where we are fast transforming into a data-driven economy, mere handling and storage of business data are not enough. Companies today require resources that can help them find relevance in their data. Data extraction Software based on artificial intelligence and machine learning models are not just helpful in converting raw data into digital forms but also helps in extracting meaningful insights out of it.
The idea here is to mimic the functioning of the human brain in intelligently mining important data from an ocean of information but automating it at the same time so that the time constraints related to human intervention are eliminated to a large extent. Seeing the utility and scope of using data extraction software in business operations, we decided to provide a detailed guide to let you know how data extraction software works, and what all it can do for you and your business. Follow along!
Table of Contents
What is Data Extraction Software?
The most basic types of data extraction software are related to the scanning and conversion of data present in PDF files, handwritten documents, images, etc., into digitally usable forms.
Furthermore, when the software is integrated with advanced machine learning models, it also becomes capable of analyzing the relationship between different elements in the extracted text.
For business operations, such an analysis is imperative because it allows the organization to improve its decision-making, customer feedback assessment, and strategic planning activities.
Techniques Used by Data Extraction Software
A data extraction software uses multiple techniques to pull out relevant data from any desired source. The following methods are the most widely used to extract data:
Association Analysis
The Association Analysis technique works by identifying and recognizing recurring entities in a given text and then finding a relationship between them. The whole point of performing Association Analysis is to figure out interesting associations between different elements of the content so that a pattern can be established which eases the task of data extraction and understanding. This technique is based on specific rules called Association Rules.
Association Rules suggest that confidence and support are the parameters that will be used to assess the usefulness in the associated elements that have been identified using the technique. This process of identifying frequent item sets and then generating Association Rules for them is widely used in interactive data extraction and analysis idea software.
● Classification Techniques
The easiest and probably the most useful way of data extraction is by Classification. By developing models for important data classes, features of every character in the given text can be separated from the other using classification techniques. For every piece of data that you need to extract, a learning model has to be created.
Predefined algorithms are applied for detection, and then by using distinct class levels, the classification rules are constructed. Subsequently, when the classification stage arises, the prediction of class labels for the given data has to be performed. The accuracy of the classification depends on the evaluation of the test data. Common PDF data extraction software use such classification techniques very often.
● Clustering Analysis
Another popular technique that works by segregating different elements of data based on their characteristics into clusters of information is called Clustering Analysis. Characteristics that are either similar or different between two parts of the same document are analyzed for creating Clusters.
The utility of Clustering Analysis as a pre-step for the application of other data extraction algorithms is well-established. For data analysis and processing tasks like Outlier detection, attribute subset selection, characterization, etc., Clustering Analysis can be used to create a convenient platform. You can find the application of Clustering Analysis in use cases like web search, image recognition, etc.
● Regression Analysis
The process of analyzing a relationship between variables in a given data set is called regression analysis. It is a predictive modeling technique where a relationship between dependent and independent variables is predicted.
In general, regression analysis is used as a statistical tool to find relationships between data units. By using the same concept, elements in a given document can also be classified based on the relationship between them.
This relationship is evaluated based on a range of numerical values often called Continuous Values. If you plan to use a financial data extraction software that uses Regression Analysis in a corporate setting, then by knowing a few variables related to sales and production, regression analysis can become a great tool for the estimation of costs.
KlearStack Data Extraction Software
Leveraging advances in artificial intelligence and machine learning are key in developing data extraction solutions that are actually industry-relevant and useful. KlearStack understands the need for an updated Data Extraction software in professional spheres, and thus, offers advanced extracting software that converts raw information into highly productive data that can be used to generate insights.
KlearStack AI can process document end-to-end, without human intervention. With an additional layer of automation called Straight-Through Processing, documents can be cross checked and validated automatically with the help of certain set of conditions.
KlearStack’s AI-based Data Extraction solutions use advanced Machine Learning models, through which the task of error detection and correction post the extraction phase is also possible. In all, with KlearStack, you get highly accurate extraction outputs, which contain relevant information for your business to use and build upon.
