Why is Straight Through Processing (STP) important? Certainly, it is the gains that STP offers in helping to eliminate data entry, but STP also eliminates the need to manually verify that data entry.
In Part 1 of our Straight Through Processing series, we discussed the two main types of automation, attended and unattended and the appropriate use cases for each. This article focuses on STP, why it is important in process automation and what it can do for your organization.
Leveraging Unattended Automation
Automation of any type is valuable in that it reduces the amount of menial tasks, leaving time for higher value work. However, automation for automation’s sake is only part of the entire equation. Unattended automation is used in cases where the outcome of a task is essentially binary: either the task was completed successfully or it failed with the majority of tasks falling into the former camp.
The ability to complete tasks successfully without the need for manual intervention is often referred to as straight through processing. Tasks can move “straight though” in a fully-automated manner. This capability relies upon a very important factor: the ability to determine with high precision that a task was not just executed, but that it was executed correctly.
Automating Simple Tasks
For simple tasks, we accomplish this through task validation. For example, rather than create a script to provision an email account and presume that everything was done correctly, validation routines can be created to send an email and verify that it was sent and received. We can verify that the email address created matches the established naming convention and that it is for the intended employee. All of these checks are done to identify correctness of a task.
Automating Task Verification
These checks are critical because they verify the actions of an automated task. After all, we certainly don’t want to blindly rely upon the our Robotic Process Automation (RPA) to provision Information Technology resources when things are actually done incorrectly. Without automated task verification, the alternative would be to manually verify the output of each task. Just as you would not expect to “approve” every single action of an autonomous car, organizations clearly do not expect to manually check every single result. Without a high level of STP, true automation is never achieved.
Achieving STP for Complex Tasks
What about more complex tasks that cannot be completely automated (like those discussed in our Part 1 article)? Unlike simple tasks that can be easily automated and verified, the process of locating and extracting 20-30 data elements on a document rarely can be summarized as a simple pass/fail. Even if all of the data on a single document could be located and extracted, what type of validation can be accomplished to determine if it is all correct?
The process of achieving STP on complex document-oriented processes is not as straightforward as simpler tasks. Unfortunately, this complexity has resulted in a significant amount of organizations using advanced capture solutions to automate tasks only to find that they need to verify every single result. The upside is that while the answers to achieving STP are not as straightforward as what you can expect with simpler tasks, it is definitely possible. It just requires a different approach: one based upon data science.
STP Approach Based on Data Science
Unlike rules-based task automation, automation of document-oriented tasks typically involve more sophisticated technologies including computer vision, classifier algorithms and pattern-matching. While none of these technologies can deliver 100% certainty, well-designed systems can achieve predictability. This allows organizations to statistically measure results of particular configurations such that the output is reliable with high levels of tolerance with typically 95% accuracy or greater.
System predictability is based on the careful curation of sample data, which is representative of the larger population of production data. Using that set, the data is analyzed to arrive at the right mix of algorithms used to create a particular task configuration. The results of the configuration are then analyzed against expected output to identify the “signature” of accurate data. Data Science.
From there, the system is then able to determine, at accuracy levels that reach 99%, correct data from incorrect data. All of this means that even more complex “cognitive tasks” can benefit from straight through processing at the discrete task level, saving organizations millions of dollars every year.
To truly benefit from automation, organizations need as much straight through processing as possible.
In order to truly benefit from automation, organizations need as much straight through processing as possible. This means significantly minimizing the need for any review whatsoever. In order to do that, success depends upon reliably identifying correct output from the stuff that requires manual intervention. No one wants to constantly tell a self-driving car what to do.
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