Unliberated Data – The Achilles Heel of Automation

written by Soofi Safavi

Many companies within the financial space are actively working to reduce their cost structure, gain strategic insights from their data and reduce errors. Robotics Process Automation (RPA) promises to achieve all three. The companies that are able to take advantage of RPA are gaining significant strategic advantages over their industry peers. Unfortunately, the vast majority of companies within the space are unable to use RPA because they do not have liberated data.

What is Liberated Data?

Liberated data is simply data that is easily:

  • Accessible – Liberated data is digitized data that is accessible to authenticated and authorized applications and/or users. Most financial companies have distributed online and paper data repositories that are not readily accessible to RPA applications.
  • Usable – Liberated data must be digitized and machine-readable. Documents must be saved in the proper format. For example, a scanned mortgage RESPA document is unusable as a .jpeg and usable as a .txt or similar file format. In the example of the mortgage RESPA, a .jpeg has to be manually reviewed by a human. It is not in a format that is machine readable. A digitized RESPA document that has undergone optical character recognition (OCR) and is saved in a .txt or .rtf format would be usable.
  • Understandable – Liberated data needs contextualization through metadata. Metadata frames the data adding useful descriptor information. Examples include file parameters, creation dates, security authorization level required to view data contents, author names, etc. With metadata, information is searchable and computers and users can make necessary correlations between files.

What is Robotic Process Automation?

Robotic Process Automation is the use of artificial intelligence to complete monotonous and/or time consuming tasks.

Why is liberated data important to RPA?

While computers and artificial intelligence grow more advanced every day, they cannot analyze data they cannot access or read. In order for RPA to be effective, all relevant company data must be accessible, usable, and understandable, i.e. liberated.

For example, in order to get a home mortgage, a mortgage applicant needs to complete and execute well over a dozen different documents. Nearly all of those documents exist in paper format or a hybrid of paper and digital. In order to determine whether the application can be underwritten, a mortgage processor then has to either spend hours going over the paper documents to ensure that they are correct, or spot check them and hope he or she does not miss anything. Either way, it is time consuming and has a risk of human error.

However, if all the documents were digitized and had all the appropriate meta-information, the mortgage processor could spend less time reviewing them. RPA takes it a step further and automates that manual review process. In this example, both the mortgage applicant and mortgage underwriter benefit greatly. The mortgage applicant gets a faster mortgage turnaround time with higher quality, and the mortgage underwriter is able to reduce costs, improve efficiency and provide better service. In addition, because the data is digitized with meta-information, it can be used by both data analytics and predictive analytics applications.

Liberating Data

To make the above example a reality, companies must do the following with their data:

  • Get it out from behind a firewall and make it accessible to RPA applications
  • Digitize it, where applicable
  • Append meta-data to it
  • Add correlations between documents

For decades, business have treated information as something to be held prisoner. However, in order to take full advantage of RPA and all of the savings and efficiency it can provide, financial services companies must open the cage and liberate their data. Luckily, HeavyWater’s Virtual Assistant and Capgemini’s Cognitive Document Processing program can offer a scalable cloud-based solution for all data liberation needs. Click here to learn more.

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