Where Did All the Data Entry Candidates Go?

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If you are struggling to hire data entry roles to help you extract data from documents, please take comfort in the fact that you are not the only one. Businesses and institutions of all sizes, including the IRS, are struggling with an acute labor shortage:

What Happened?

This article was published in April of 2022. To understand why there is an acute shortage of labor for this role, you will need to read another article from the same month:

With the Great Resignation, there has been an acute shortage of technical talent available for entry-level Tech Jobs. To combat this, technology companies have responded by hiring people from lower, minimum-wage jobs and providing them with extensive training and support to perform entry-level tech jobs. 

According to this article, “Many employers from International Business Machines to CVS Health Corp. now say they are happy to help relatively inexperienced new hires get trained in coding, cybersecurity, and healthcare technology to fill positions.”

The problem with this approach is it places the burden of training on operations team managers and executives diverting their attention from their higher-level responsibilities.

So, Now What?

This shortage of data entry workers has put most financial services companies in a bind. While it has been traditionally challenging to retain people in this role, this double whammy of lack of supply of new resources to replace the ones leaving has added to the woes of the operations managers. Independent of this crisis, business continues to grow, and some documents can't wait for processing and revenues are on the table to pick up.

As an operations executive, you must deal with three challenges to tide over this crisis:

1. I cannot hire more people

2. When someone leaves, my productivity takes a hit

3. I need to prepare for more people leaving this role over time.

Let us look at each of these aspects in a little more detail and look at potential solutions.

Challenge: I cannot hire more people.

Solution: Increase work capacity without adding more people.

You may recall working on labor problems if you remember your middle school algebra. They sounded like, “If one person can paint a ⅓ of a room in a day then how many people will it take to paint 5 rooms in 7 days”. You have a similar math challenge here but in this new world, instead of asking “how many people would you need”, you should ask, “if Iron Man were to do this work, how long would he take?”

Iron Man is one of my favorite Super Heroes. I am not sure if you have ever thought about this but out of all the Superheroes out there, Iron Man is the only one that you can aspire to become. You would have to be born on a different planet to become Superman or be bitten by a fictional mutant spider to become Spiderman. But with access to the right engineering talent and capital - you can become Iron Man.

Anyway, I digress.

Coming back to our math riddle, Iron man has access to an exoskeleton that helps him increase his basic human capabilities. Without the iron suit, the iron man can lift 20lbs of weight; with it, he can lift 80lbs. If he can jump over 3 ft without the suit, he can leap over 12 ft with it.

You need something similar for your data entry team. If a data entry team member can process 40 documents an hour, with some sort of an iron suit, you can get him to process 100 documents an hour.

Intelligent Document Processing (IDP) systems are such iron suits for your team. 

When your team starts their work, instead of processing data from scratch, they can focus on reviewing the work of the algorithms, which can exponentially increase the output range or scope. IDP refers to automating your document capture, extraction, and classification process with the help of advanced AI technologies, such as NLP, machine learning, computer vision, and deep learning. As IDP systems are automated, they also help scale faster. When your business volumes pick up, it generally takes a lot of time to increase the size of your team, train the new staff and wait for them to become efficient. Scaling an IDP platform simply requires more infrastructure which is relatively inexpensive and rapidly available.

Challenge: When someone leaves, my productivity takes a hit

Solution: Retain the knowledge when a person leaves

While we are talking about superheroes, let us linger a little longer in the fantasy world and take a look at another interesting artifact - the pensieve from Harry Potter:

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You may recall this from the movies or the books - Pensieve is a bowl where one can store their memories with all the details. You can share your memories with someone else who can enter the pensieve and relive these thoughts and memories. 

What if you had a means to automatically save someone’s knowledge as they were leaving your data entry team? Wouldn’t it be magical to have someone start by reliving these memories and not spend countless weeks training new staff.

Machine Learning is the pensieve that can help you retain knowledge from your old staff by automatically learning from their every action on a data entry job.

Machine learning systems are trained using statistical methods and algorithms and then make predictions, extractions, and classifications about the key insights or high-value data from among a larger chunk of structured and, more often, semi-structured or unstructured data. These algorithms enable IDP systems to exponentially learn from training and corrections each time a document is processed. These self-learning systems rely heavily  on the data fed to them  to interpret and learn from past data sets. Their accuracy improves ach time you give it feedback or corrections..

Challenge: I need to prepare for more people leaving this role over time

Solution: Get on an efficiency increase treadmill

With an Iron Suit and a Pensieve at your disposal, what else can you add to your repertoire to deal with the challenge of ever-increasing demand for efficiency gain? This time, you will find the answer in yet another corner of the fantasyland - inside The Matrix.

In the second part of the Matrix trilogy, Agent Smith figures out that he cannot deal with Neo on his own. He needs far too many agents to stand a fighting chance. This makes him figure out an agent replication algorithm that can create thousands of agents on the fly.

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Feedback-based continuous learning loop is the replication algorithm for the data entry work. A feedback loop is a strategy or mechanism to leverage the current predictions, feedback, and corrections of the machine learning models in an IDP system to retrain and improve the quality and increase the accuracy of future predictions. The feedback loop brings the best results when corrections are integrated with extraction.This mechanism ensures that the IDP system is constantly trained and updated to provide you high accuracy. Like replicated agent Smiths, this loop replicates the intelligence of your data entry team by learning from their actions. So when a new team member joins, they contribute to the learning of all the previous ones.

Do not become an automation casualty

We are going through an existing phase in automation. The world and businesses will change dramatically over the next few years and we will see more and more manual tasks being performed by AI. However, this is not the first time we are going through this change. The industrial revolution put us through a similar transformation years ago. Business teams that see this change and are willing to embrace it will see massive improvements in operations and much happier customers. 

They say, ‘never waste a good crisis’. The labor crisis of today offers a unique window of opportunity to try out AI-enabled automation. Grab this opportunity and let it help you deal with the labor shortage of today.

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