How to Get RPA Right with Unstructured Data Extraction

Posted 6 May 2019 4:00 PM by Taylor Van Beek

A robot hand is holding a check-mark symbol.

From life sciences to banking, energy and insurance, 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 software bots 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 bots are able to access and leverage, the more workflows can be automated, growing the benefits of automation across the organization.

This image explains how unstructured data presents a challenge for RPA systems.

This image explains how unstructured data presents a challenge for RPA systems.

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, for example – 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 organizations can not only improve their RPA output but also ensure that it is scalable and delivering overall operational efficiency.

How can organizations take steps to rapidly scale their data extraction capabilities and improve their automation capabilities? Follow this four-step plan.

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.

2. 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, analyze 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.

3. 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 break-downs in the workflow at an early stage and take steps to correct the process.

4. 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.

Wrap up

RPA has the potential to be a scalable solution that increases operational efficiency, reduces costs and limits manual labor – but not without accessible and high-quality data to fuel the system. By following the steps outlined above, businesses can employ automated data extraction solutions to ensure their automation platform has a steady stream of machine-readable content, in order to automate workloads from end to end.

References:
*Time and cost savings from RPA

Conquer the Unstructured Data Challenge

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