Time to drop traditional data processing and adapt “intelligent” solutions

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These days, no organization can function without data. Data is the fuel that powers businesses since enormous volumes of data are produced every second from corporate transactions, sales numbers, customer logs, and stakeholders. All of this information is compiled into a sizable data set. It has its own unique difficulties.

To improve decision-making, this data needs to be examined. However, businesses still face certain difficulties with large volumes of data. These include issues with data quality, storage, a dearth of data science experts, validating data, and gathering data from various sources, this will lead to bottlenecks in business processing and challenging to take decisions based on the data for further growth of the company.

According to various estimates, 80% of enterprise data is semi-structured or unstructured, making it challenging to automate processes using conventional automation technologies. Enterprises are increasingly required to process massive amounts of semi-structured and unstructured materials more accurately and quickly. RPA can automate data from legacy, third-party, and web apps (surface automation), but it does not work well with unstructured data sources (e.g., documents, emails, and attachments).

Simply put, unstructured data is information that cannot be easily stored in a standard relational database and is not organized in accordance with a pre-established data model or schema. So how do businesses deal with processing unstructured data in processes that are document-centric? Despite the fact that optical character recognition (OCR), whose accuracy with legacy OCR is only about 60%, aids in the digitalization of paper-based information assets, its inherent quality problems are difficult to ignore. For further processing by RPA or other downstream systems, intelligent document processing (IDP) solutions can process semi-structured & unstructured data and transform it to structured format in this situation.

Data comes in various formats:

  • Structured
  • Semi-structured
  • Unstructured
  • Volume: this data is generated constantly
  • Velocity: you need to process them quickly
  • Variety: many sources and data types are used
  • Veracity: data must be of good quality

 The IDP software market is expanding quickly

All vertical industries’ organizations continue to rely heavily on papers as a source of data input. The unstructured data in these documents necessitates knowledge workers for manual data entry, exception management, and quality checks, which makes document processing labor-intensive, time-consuming, and expensive. Large, small, and medium-sized businesses all spent roughly $400 million on IDP software in 2018, and that amount rose to about $550 million in 2019. According to Everest Group projections. It is simple to see how the unorganized document processing market for machine learning (ML) solutions is big enough for bundled IDP solutions to gain traction.

Some of the main use cases for IDP solutions are Know Your Customer (KYC), invoice processing, insurance claims, patient onboarding, patient records, proof of delivery, and purchase forms. IDP software is useful in business-specific procedures including trade financing, mortgage processing, customer onboarding, and the preparation of legal papers. Given its high volume and proneness to error, accounts payable and accounts receivable are frequent use cases for IDP in the financial and accounting industry.

Document automation is slowed significantly by the need to create templates

In general, users of IDP software should only require a minimal amount of training for template updates. However, businesses who work with hundreds to thousands of vendors each month are aware that updating invoice templates is a time-consuming procedure. The amount of consultation time required to set up and use templates for different sorts of documents can drastically increase total costs. In such circumstances, it is simple to see how an IDP without templates can drastically lower total cost of ownership (TCO) and enable a quicker time to automation. There is no need to wait for months to create templates, let alone actual documents.

IDP solutions serve as an intelligent automation tool with a specific purpose

Simply said, intelligent automation integrates RPA and document capture and processing capabilities with artificial intelligence (such as natural language processing, machine learning, and computer vision). IDP solutions are utilized to ingest unstructured data into workflows for end-to-end automation, and AI/ML capabilities are leveraged to increase straight-through processing (STP) with accuracy.

Automated data verification and validation as well as ongoing learning and improvement based on AI/ML algorithms and user inputs are made possible by pre-built AI/ML capabilities and business rules. IDP automates the retrieval, comprehension, and integration of documents needed for carrying out a business process by combining OCR, data capture, and AI/ML. End-to-end process automation is possible when RPA, IDP, and APIs are utilised in conjunction. IDP enables data-led automation of documents including unstructured and semi-structured data, in contrast to RPA’s focus on processes.

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