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It’s only human
If you work in manufacturing, banking, or insurance (or so many other industries), my wild guess is that you rely on both paper and digital documents to operate your business and to keep things moving.
You may also rely on people to physically rekey data from these documents into tools built for analyzing and understanding information. With all this manual data entry comes a host of problems — most notably, human error. Simply put, manual data entry could be costing your business time, money, and opportunity.
The price of manual data processing is greater than just the time and resources it takes for employees to complete these tasks. In a report about the insurance industry, Accenture says that claims professionals spend nearly 50% of their time on activities that don’t impact the outcome of the claim. You can imagine the mistakes and errors that fall through the cracks on a regular basis with people having half of their time consumed with things like manual data entry and administrative busywork.
Creating opportunities for your most talented employees to put down busywork and pursue more important projects can create a thriving work environment. Free up your knowledge workers to take on jobs that have greater business impact and you’ll see a healthier bottom line, improved employee morale, and a more scalable business.
Data entry: How it’s always been done
Internal processes are difficult to change at organizations of any size, so it’s not surprising that a lot of businesses still rely on human data entry to compile and analyze information. A quick Indeed search shows that roughly 75,000 open data entry jobs in the United States. While it might seem more economical to hire a part-time or contract role to handle the extraction of data from documents, there are costs associated that go above and beyond just headcount and tooling.
The true price of data entry and bad data
You might calculate data entry costs purely on tools, equipment, and headcount. But what if we looked deeper at the expenses associated with bad data? Humans are prone to error and this is especially true when people are completing highly repetitive activities. The Data Warehouse Institute estimates that bad, inaccurate, or missing data causes U.S. businesses to lose $600 billion annually.
The hidden price of people-powered data entry can be thought of in a couple of different ways. The first cost is quantifiable — you can understand how much you spend on reworking, rekeying, or wasted time analyzing bad data as long as you know the time spent and payroll cost. In a Forrester report, it’s estimated that analysts spend 40% of their time vetting, verifying, and fixing data before it can be used for strategic decision-making. This signals a widespread lack of trust in a company’s own data.
The second hidden cost lies within opportunities for the business. Bad or inaccurate data can slow down product releases and skew strategic analyses. Operational data such as supplier information, material use projections, and a business’s financial performance are used to make strategic decisions. If this data is incorrect or partial, it can have big impacts on the company’s ability to serve its customers, maintain supply levels, and ultimately compete in the marketplace.
Bad data is also a compounding issue. For example, if you rely on a team of people to input data from your incoming purchase orders in order to forecast manufacturing needs and that data is incorrect, you could be short on supply orders which then results in a slower fulfillment time for customers.
Another real-world scenario is taken from one of our customers here at Impira. A financial services company was manually pulling data from multiple sources to analyze opportunities and risks for their clients. This was holding them back from making faster decisions for their clients, and it hampered their ability to take on more clients to grow their practice. Like with all organizations of any industry, mass data collection with little oversight is a risky method and can have a direct impact on customers and on profitability. So, on top of having to deal with the manual aspect of data entry — because the quality of their consultation has a direct correlation with their data quality — this company spent more time up front ensuring they avoided the pitfalls of bad data. Using Impira allowed them to go from financial statement files to analysis and consultation in less time, resulting in more agility and the ability to scale their business. All of this was done without disrupting their current systems and workflow.
Embracing automation to reduce data errors
Automating manual data entry by leveraging intelligent document processing (IDP) technologies that use machine learning and optical character recognition (OCR) can take the task of extracting and organizing data at scale and decrease data error risks.
In this study by Smartsheet, employees estimate that a quarter of their workweek is spent doing data entry work, like collecting, copying, and cleaning data. In this report, 66% cited automation as being key in reducing data errors. In our own survey of operations professionals in the United States, we found that 50% of respondents were looking for a data entry solution to reduce errors. Automation of redundant tasks like data entry allows the organization to have deeper trust in their data along with other benefits like helping team members focus on more meaningful project work.
While automation has a reputation for being costly and time consuming to implement, there are ways to leverage machine learning to increase efficiency and confidence in data for specific tasks, such as data entry.
Impira, the solution to bad data
Impira has developed multiple technologies that work together to do all the data entry chores that once weighed you down, so you can have full control and visibility of your automation processes from end-to-end. On top of this, Impira is continually adding integrations with platforms like Zapier, Amazon S3, Dropbox, and other partners to allow you to easily build true automation right into your existing systems.
Having a no-code automation software that any of your team members can use is the key to creating an automated data processing workflow at your company. In some standard artificial intelligence (AI) and machine learning solutions available today, a user may need coding skills or a data science background to utilize the capabilities of these technologies in a meaningful way.
Nascent technologies like automated machine learning (AutoML), allow access to optical character recognition technology that’s flexible, interactive, and easily controllable. This is a refreshing take on standard OCR tech that’s often limiting and non-interactive — the OCR results that you get are the one’s you’re stuck with. With Impira, if OCR is the “eyes” of the whole operation, then AutoML is the brain that’s continuously learning and applying new lessons.
This is what Impira can do for you — you no longer have to manage a relationship with an outsourced OCR provider for your data entry, nor do you need to waste your team members' time with menial tasks. They can automate data entry and train machine learning models in real time with minimal effort and no specialized skills.
Businesses are increasingly dependent on data from multiple sources to make decisions, which means data errors can have costly consequences — some of which are hard to determine or track down. If businesses want to compete in this increasingly competitive and digital space, they should embrace automation to reduce human input on redundant data entry so they can increase confidence in their analyses.