Traditional data handling and data entry methods could not stand the test of time because they are tedious, time-consuming, and full of errors. While there has been a paradigm shift in the mindsets of companies to move towards accepting models for the automation of this process, most people are still unaware of this remarkable invention called Data Extraction. 21st-century business operations are completely data-driven. While information plays such a crucial role in the growth of businesses today, the rising amount of unstructured data remains a big cause of concern.
Lack of structure and formatting acts as a major obstacle in retrieving beneficial content from a huge repository of documents. Data extraction methods based on Artificial Intelligence are eliminating this problem, by taking out relevant and desirable information from even the most unstructured of documents, without the user having to work tirelessly for it.
I am sure it already seems like a highly intriguing concept, isn’t it? So let’s learn more about data extraction tools, and how you can use them to make your job easy.
An automated process of retrieving data of a variety of types and formats, from PDF documents, scanned files, images, etc., and being able to load them onto other portals or platforms is called Data Extraction. Highly elaborate yet easy-to-use data extraction tools are used for this process, which can provide the required data that is further utilized by the user to prepare data analytics reports to derive actionable insights.
Unstructured data is practically useless for systems and computers. Humans can make sense out of unstructured data, but the process where a user comprehends such haphazardly arranged data and then goes about correcting its formatting from scratch would need a lot of time which is quite difficult to spare in a fast-paced business environment. It is here that data extraction tools come in handy. The benefits of data extraction tools in data warehouse performance enhancement are:
If a data extraction tool utilizes appropriate machine learning models, it can help in deriving reliable and relevant data which could generate analytics and insights that are highly beneficial for specific business operations. With artificial intelligence, the data extraction tool becomes empowered to combine the extracted data with existing records such as sales reports, marketing content, customer feedback, etc., which will allow you to make more sense out of each of these records as it puts everything in perspective.
So far we have discussed how tedious and time-consuming manual processing of unstructured data can be for any employee. However, it is equally important to note that while doing this cumbersome task, there are chances of concentration lapses and errors, simply owing to the nature and quantum of work. Ultimately, it proves to be a major compromise in the quality of data which your company will utilize further to strategize and plan its operations.
An artificial intelligence-based data extraction tool is not just limited to the retrieval of information from any desired document. It also cross-checks the information that it extracts, so that the human intervention needed for error correction gets minimized. Thus, you not only end up saving some precious time but also save your efforts from getting wasted on correcting the data which is to be uploaded or used for crucial business purposes.
The manual processing and correction of unstructured documents mean that the well-formatted and relevant information becomes limited to the person or the department where this task was performed. In most cases, this proves to be the number one reason for the formation of Data Silos in a company. This way, data remains inaccessible to other team members, and therefore, the efficiency of the collective work efforts reduces drastically.
Data Extraction tools automate the retrieval of important information and then convert this data into digitally usable forms. The information can be stored in convenient formats and files, which can further be shared seamlessly within the departments. Ultimately, the accessibility of important information increases and all team members remain aware of the project insights.
Snowflake offers tools to extract data from source systems. It supports the ETL process by working with a range of data extraction tools like Matillion, Talend, Informatica, and Tableau.
Oracle provides ETL benefits for data warehouse management projects. Its data extraction tool pulls out data from a source system, which then undergoes a series of transformations, before finally being loaded in a data warehouse.
When it comes to data extraction tools for document understanding, KlearStack has a reputation like none other. By combining Artificial Intelligence concepts like Computer Vision, NLP, Deep Learning, etc., KlearStack allows users to extract information from unstructured and semi-structured documents with ease and high precision. Most importantly, it is a template-independent tool, which means that data from any document whatsoever can be retrieved using KlearStack with excellent accuracy.