Data is the new oil. This phrase has been used a lot for the past few years now and it still holds very much true. The biggest reason is that data is a direct source of measurement and quantifies growth. But in this context, we are talking about data of your everyday transactions that will help you streamline your accounts processes and through automation, implement end to end automation for data extraction from various types of documents.
Data extraction is a process of identifying, classifying and capturing data from different types of documents and then storing it in the cloud or offline. It is the most important part of the Extraction, Transformation and Loading (ETL) process. Data extraction allows organizations to critically analyze datasets and make impactful data-driven decisions based on them.
What are the different methods of data extraction?
Data extraction is possible in two main ways, Logical and Physical.
1. Logical Extraction:
Logical Extraction can be further divided into two sub-types of extraction:
- Full Extraction: Entire data is extracted at the same time, from the source. It does not require any extra logical or technological information. This is used only when data has to be extracted and loaded for the very first time. This set of data that has been extracted reflects the existing data that is available at of source of the system.
- Incremental Extraction: Changes in the datasets is dynamic and they are tracked since the last successful extraction. The timestamp is leveraged as to when the last time data was successfully extracted. Changes are incrementally extracted and loaded into the system.
2. Physical Extraction:
Physical Extraction can be further divided into two sub-types of extraction:
- Online Extraction: The extracted data is more structured than the source data in the online extraction method of physical extraction of data. This is directly captured from the source system to the warehouse. This leads to a direct connection between the source system and the final destination of the data being stored.
- Offline Extraction: In offline extraction, data extraction takes place outside of the source system. In this method, data is either structured by itself or structured via extraction routines.
What types of data can be extracted?
Data can be extracted from various types of documents. Images, Texts, PDFs, and E-mails are some of the many document types from which data can be extracted easily.
Unstructured invoices, receipts, and purchase orders are some of the challenging documents to extract data from as these do not documents do not have any standardized universal template.
What are the available tools for data extraction?
Import.io is mainly meant for industries related to stock research, e-commerce and retail, sales and marketing intelligence, and risk management. The main USP is that it can assist businesses in achieving success through the use of smart data as well as other data visualization and reporting tools.
Octoparse is a data extraction tool that is super user-friendly. The entire process takes only three steps to complete the process. The user does not need to code anything and the extraction can is just done by entering the website URL. Scraping the website is made easy with Octoparse.
Apify Store offers ready-made scrapping tolls for social media websites such as Instagram, Facebook, Twitter as well as Google Maps. They provide website scrapping solutions for all sizes of websites. The data can be downloaded in structured after it is extracted.
OutWitHub is one of the most frequently used web scrapping platforms available. It divides web pages into different parts before moving from page to page to extract information from the website. The application has an extension on Mozilla Firefox and Chrome, thus, making it easy to use and it is mainly used to extract links, email addresses, pictures and so on.
4. KlearStack AI
The main USP of KlearStack AI is that it can extract data even from unstructured documents and standardize it. The ML models help to ensure that the platform upgrades itself with more documents being processed through the system.
What are the use cases for data extraction?
Manufacturing & Logistics
Invoices, receipts, and purchase orders are some of the many documents that need to be automated by the accounts payable team of manufacturing and logistics firms. Apart from that, documents like bills of lading, shipping related documents and other similar unstructured documents can be scanned and digitized.
Invoices and receipts along with medical bills and identification documents can be digitized and data can be extracted to process payments of patients swiftly. At times, there may be bank loans for patient payments and those documents can be automated as well.
Loan documents and credit notes apart from other invoices and receipts are documents from which data needs to be extracted to ensure faster processing times for loans and processing other transactions.
What is the difference between data extraction and data scraping?
Data extraction is the process of extracting and classifying data from physical documents. Data Scrapping or web scrapping rather means that the data is extracted from websites.
The data extraction process helps to contextually understand data using OCR on the physical documents and then extract data whereas web scrapping extracts information using scrappers and crawlers web pages and converts the information into spreadsheets.
Data extraction for multiple documents is possible at a single time however, for scrapping data from the website, only a single URL needs to provide from which data is extracted and converted into a spreadsheet through most solutions.
How to automate data extraction?
Preprocessing: Documents are first filtered as to whether they are of good quality so that data can be accurately extracted from them. If there are not of good quality, they are rejected.
Classification: Documents at this stage are classified. Invoices, receipts and other documents are separated one from another.
Data Extraction: At this stage, data from documents are extracted.
Post-Processing: Data of the document is standardized at this stage. For example, if two different documents have two different date formats, KlearStack AI will standardize it to one format at this stage.
Data Validation: One document is crossed with another based on criteria that have been manually entered to match documents. Say for example if invoices worth above USD 600,000 needs to process, then the data validation rule will check if this criterion is not met or not. If it is met, it will proceed to the next stage and if not met, it will be checked manually.
Straight Through Processing: If the data validation criteria are met, documents are then stored digitally on the integrated database platform. SAP, QuickBooks and RPA tools are used to store data at the backend.
Also read: Role of Artificial Intelligence & Machine Learning in Straight Through Processing
What are the advantages of automated data extraction?
Cost effective : Automated data extraction saves the cost of having manual resources and therefore, improves the overall cost efficiency of the organization. As the data extraction process is completely automated, human intervention is hardly required.
Faster Process Rate : Invoices and receipts and other documents are processed at a faster rate and therefore, it enhances the overall productivity of the organization. This allows businesses to focus on core aspects of business and also have the ability to take more load during peak hours.
Reduced Error Rate : As no humans are involved in the data extraction process, the error rates are drastically when compared to manual data entry work done by humans. This increases the level of accuracy when the data is extracted from the documents.
High Satisfaction Level : As documents are processed quickly with a higher level of accuracy, the satisfaction rate among clients and vendor partners is also quite high. This helps in retaining your business and also upskilling your existing services with them as your organization can process documents on time.
What are the challenges in data extraction?
Quality of Documents: One of the key criteria to automate documents is that the documents have to be of decent quality from the get-go. If the text is faded or the pages are quite wrinkled, data extraction from such documents may not be quite accurate.
Size of Unstructured Data: If the volume of unstructured documents is quite high, processing them could be a slight hassle. It may impact the productivity of the company. It is always ideal to try to prioritize the documents and break the large chunk small and process them accordingly.
Handwritten Documents: One of the major challenges is to extract data from handwritten documents. Although some solutions are now available that can extract data accurately from handwritten documents, there is still a lot of scope for improvement left in this area.
Also Read: How to Capture Data Accurately From Poor Quality Documents
Data extraction using AI and ML
Artificial Intelligence and Machine Learning play an important role in data extraction from various types of documents. Contextual meaning is derived through Advanced OCR and data is accurately extracted from the source. The more documents that go through the platform the smarter and more efficient ML models become and documents are processed much faster going forward.
Data extraction is the need of the hour for most organizations across the globe. With advances in AI and ML tools, it has become much easier to automate the entire data extraction process and automate it 100% without the need for any manual intervention.
KlearStack AI makes this task achievable and can offer up to 95% accuracy while extracting data. If you wish to know more about KlearStack AI, visit here.