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Key tenets of automating processes with unstructured data

Exploring how organisations can rapidly scale data extraction capabilities, improve their automation capabilities and generate maximum ROI .

From life sciences to banking, energy and insurance, AI-driven Robotic Process Automation – RPA is increasing operational efficiency by reducing costs and the number of manual tasks that require high levels of knowledge-worker input. This, in turn, provides even greater benefit by freeing up key resources to drive greater business value.

But in order to get the most value out of RPA, enterprises need to leverage automated data extraction solutions to improve the speed and efficiency with which they transform unstructured data into high-quality structured content that fuels software bots, in order to automate workflows and extend the benefits of automation across the organization.

How Better Data Extraction Fuels RPA?

RPA is the programming of digital workers to perform specific repetitive, manual tasks, such as populating customer information into databases, automating approvals or invoice payments based on predetermined criteria, or other functions. The more data these digital workers can access and leverage, the more workflows can be automated, growing the benefits of automation across the organization.

Unstructured data – which includes content locked in document formats such as email threads, scanned files and images that are part of mortgage and loan applications and claims forms presents a key obstacle to RPA. Computers are limited in their ability to extract key values from these formats. Without an automated data extraction solution in place, RPA efforts will slow or stall altogether as organizations are forced to perform manual extractions to mine key data. Effective data extraction is a key way organisations can not only improve their RPA output but also ensure that it is scalable and delivering overall operational efficiency.

How can organizations rapidly scale their data extraction capabilities, improve their automation capabilities and get the best ROI of their investment? Following are the 4 models for successful deployment of RPA technology

  1. OCR

Challenges with document formats themselves are a key reason why so much organizational content remains unstructured – and thus unusable when it comes to boosting RPA scalability. When information has been stored as images – as is typically the case when you scan paper documents into TIFF files, as one example – it cannot be read by machines.

But optical character recognition (OCR) solutions convert these formats into text-based data that is machine compatible, and therefore able to be ingested by bots. Instead of applying manual processes to such documents, enterprises can boost their automation efforts by employing OCR as the first step, converting such formats into machine-consumable data.

  1. Classify

Getting automation right doesn’t only depend on giving machines data they can use – it’s also about ensuring they have the right data, specific to the task at hand. Before data can be used for automation, enterprises need to know what their documents contain by taking steps to identify, analyse

and classify their documents. Document classification can help speed this process – and drive faster results from automation – because instead of requiring countless human hours to review documents, it leverages machine-based processes and rules to crawl and intelligently classify the information within.

  1. Extract

Now that you have a handle on what data you possess, you need to be able to extract the maximum value from it. This includes ensuring that documents are free of ROT (redundant, obsolete, trivial) content and that valuable assets have been grouped, enhanced and are machine searchable. This ensures that key data can be readily identified and extracted as needed for RPA projects.

Process automation – including any projects using RPA tools – is just one tactic in a business’ larger digital transformation journey. If course corrections are not made as needed, the project can break down. In successful projects, monitoring begins as soon as execution starts. For instance, a bank might measure data processing accuracy against well-defined targets—assessing speed and efficiency and implementing the feedback loops necessary to process that information. This enables them to catch systemic breakdowns in the workflow at an early stage and take steps to correct the process.

  1. Deliver

Once the processes of illuminating and structuring data have been put in place, enterprises are almost ready to reap the ongoing benefits of automation. But to garner the full 80 to 90 percent savings in time and costs that automation can deliver,* data extraction must be integrated into workflows on an ongoing and consistent basis so that key values can be mined from documents as they’re created, thereby facilitating a seamless process for end-to-end automation.

Intelligent automation combines the power of artificial intelligence (AI) and machine learning (ML) to deliver digital workers that take away the mundane tasks human workers are overloaded with empowering them to focus on the profit driving initiatives only people can do. Our intelligent automation platform provides everything you need to serve your customers in today’s demanding world with a secure, stable and compliant environment that propels digital transformation. We’re here to help every step of the way from automation specialists, pre-built automations and training and certification to our unrivaled customer support.

A combination of robotics process automation (RPA) with expanded cognitive and AI capabilities makes Blue Prism different from other automation tools on the market. It helps you gain instant access to an already AI-equipped digital workforce, along with the capabilities you need to build, delegate, and control your automation.

Building to this automation foundation, Blue Prism continues to embed the latest cloud, artificial intelligence (AI), machine learning, and cognitive capabilities into its digital workforce. One such example is Blue Prism Decipher IDP, a software tool that makes it possible for users to scan, classify, validate and extract data from structured, unstructured and semi-structured documents – such as invoices, purchase orders, and other documentation, with optional NLP plug in. With Optical Character Recognition (OCR), Decipher IDP enables you to extract text and text layout information from images, too!

Decipher IDP is an ideal intelligent document processing solution that can scan and identify data points—regardless of their format and location– then extract valuable insights from those data points for use within RPA processes. This offering acts as the “eyes” for digital workers in the automation process and puts the power of optical character recognition (OCR), machine learning (ML) and AI in the hands of business leaders to enable greater operational agility.

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