A Brief Guide to Intelligent Data Processing

A Brief Guide to Intelligent Data Processing

Data is the foundation of any organisation. It’s critical to the organisation’s growth and day-to-day operations. Despite having a wealth of data, the main problem firms face today is utilising information in a sensible way that is also relevant to their future. The human worker finds it difficult to deal with large amounts of inbound data.

However, technological advancements have made it feasible for computer-trained algorithms to scan, analyse, and comprehend digital or paper documents in the same way that people do. Intelligent Data Processing, or IDP, is a technique that is gaining traction in a variety of fields.

Data is the foundation of any organisation. It’s critical to the organisation’s growth and day-to-day operations. Despite having a wealth of data, the main problem firms face today is utilising information in a sensible way that is also relevant to their future. The human worker finds it difficult to deal with large amounts of inbound data.

However, technological advancements have made it feasible for computer-trained algorithms to scan, analyse, and comprehend digital or paper documents in the same way that people do. Intelligent Data Processing, or IDP, is a technique that is gaining traction in a variety of fields.

Intelligent data processing is the technique of intelligently gathering specific data and streamlining document processing tasks, as the name suggests. An IDP’s purpose is to extract data from any type of document, whether long-form or electronic, organised or unstructured.

Information is crucial to a company’s workflow, and it must be adequately structured. That’s why data collection software is critical to the company’s success. When a technology like IDP was first introduced, it was clear that adopting it would be a game-changer for many businesses.

Working on Intelligent Data Processing

Intelligent Data Processing or IDP software converts unstructured and semi-structured data into useable information. Unfortunately, 80 percent of all company data is stored in unstructured formats such as business papers, emails, photos, and PDF documents, making digital transformation difficult.

IDP is the next level of automation, capturing, extracting, and processing data from a wide range of document formats. It classifies, categorises, and extracts important information using AI technologies like computer vision, NLP, ML, and deep learning and validates the extracted data.

Top Benefits from IDP Solutions

  • Savings on direct costs: Reduces costs by drastically lowering the cost of processing big amounts of data.
  • Increases straight-through processing (STP): Reduce the need for knowledge workers to process documents manually.
  • The ease with which it can be used: It Allows organisations to get up and running more quickly and automate more procedures.
  • The effectiveness of the process: End-to-end automation of document-centric operations is possible.
  • Increased precision: With the usage of AI, you’ll see a huge rise in data accuracy right away.
  • Increased strategic objective attainment: Automated data processing helps businesses achieve goals like increasing customer service.

Benefits of Combining IDP and RPA

Intelligent automation necessitates the use of IDP. Automation can only go as far as the amount of data available to deal with. Setting up data extraction to run automation is generally a separate 3rd-party project with standard RPA solutions, adding continuing expenditures and unstable integration points.

Extracting data and organising information effectively is the first step toward automating most business activities that currently rely on manual inputs and involvement. Intelligent Document Processing, which is embedded within the RPA platform, allows business users to automate processes from start to finish. When IDP and RPA coexist in the same platform, you have the two most important parts of the automation puzzle working in perfect harmony.

  • Begin analysing the data: IDP technologies that are RPA-native and integrated are simple to set up and often 5-10x faster than other alternatives.
  • Reduce your processing expenses: AI-driven IDP + RPA increases straight-through processing (STP) by continuously learning from human feedback.
  • User-friendly for business: With pre-packaged use cases for the most typical document processing applications, the built-in IDP makes it simple to get started.
  • Automation at the corporate level: Integrate your IDP software with other aspects of your business to create a fully integrated RPA solution that doesn’t require costly upgrades.
  • Developers will benefit from this: Modify your AI workflows with the flexibility to incorporate custom logic to improve document extraction (Python scripting)
  • Any document can be processed: Combine the capability of IDP, which can handle structured and unstructured documents in practically any format, with automation to accelerate digital transformation.

Growing Demand for Outsourcing Intelligent Data Processing

Previously, manual labour was used to complete this type of work. The paperwork required an increasing number of staff, and the task itself was time-consuming and tiresome, resulting in errors and resource waste.

When the bulk of the workforce was required to execute this type of labour, organisations became concerned that they wouldn’t be able to use their personnel for the more challenging aspects of the job. Furthermore, a large amount of data was stored in a disorganised manner, which hampered the daily routine.

As a result, as per the studies, numerous businesses developed solutions to improve the problem, and businesses were able to outsource data to them. The companies benefited in two ways: first, they could minimise the number of employees to handle data manually. Second, the businesses could examine more time-consuming aspects of the workflow and redeploy employees to those areas.

Internally handling such a big copious amount of data can become tough in cases where a corporation has data intake from numerous places and sources and in varied forms. Outsourcing the same to process data, on the other hand, intelligently saves time and enhances overall performance.

Conclusion

According to studies, unstructured data makes up roughly 80% of a company’s information resources. When data accumulates over time, it will become unmanageable, and many redundant data will be stored. Important and helpful information is trapped as a result of this procedure. We must remove this trapped info from the documents to liberate it and utilise it efficiently. Services like Klearstack help achieve the same with their top intelligent data processing solutions

Ashutosh Saitwal
Ashutosh Saitwal
www.klearstack.com/

Ashutosh is the founder and director of the award winning KlearStack AI platform. You can catch him speaking at NASSCOM events around the world where he speaks and is an evangelist for RPA, AI, Machine Learning and Intelligent Document Processing.