3 Top Challenges Companies Face with Manual Data Entry
iTech Data Services

3 Top Challenges Companies Face with Manual Data Entry

Read Time: 4 minutes

One of the most expensive typos ever was back in 2006 when Alitalia Airlines lost $7.2million because of missing zeros.

Italy’s national carrier announced a promotional offer of $3,900 for business-class flights from Toronto to Cyprus. A significant price reduction considering standard fares in those days. But a manual data entry error made it $39 while entering it into the company’s ticketing system.

Over 2000 customers grabbed the offer before Alitalia noticed the mistake, and the airline had to honor the purchased tickets to protect its reputation.

Such an expensive one-off mistake might grab headlines, but smaller errors can also add up over time.

AT&T found that manual data entry errors in purchase orders and invoices added up to many millions of dollars over time.

Manual Data entry is never fool-proof, but reducing human error is going to save your bottom line. While that sounds like a no-brainer, recognizing the challenges also raises questions surrounding what available solutions exist.

Here’s a quick breakdown of manual data entry problems and how to fix them

  1. High data entry error rate
  2. The cost of quality
  3. Slow turnaround time

So without further ado, let’s see the problems with manual data entry

1. High Data Entry error rate


Is the average data entry error rate for your company higher than 1%?

If the answer is “yes’, then your manual data entry team and software processes require a closer audit.

A high error rate could be due to

• Inadequately trained manual data entry staff
• Manual data entry workload and seasonal spikes causing stress
• Scribbled handwritten forms that are not clear
• Documents with unlabelled fields
• Typos or ambiguous data (12/7/2020 in America is ‘December’ and in Europe is ‘August’)

Humans will make mistakes. Inaccurate data collection will snowball into larger issues that will slow down data entry processes and increase operating expenses.

Many organizations prefer to outsource data entry to experienced companies that are well versed in all aspects of data capture. This saves on operational costs, software upgrades, provides a steady source of trained resources, skilled workforce, and allows organizations to focus on their core business.

2. The cost of quality

The cost of quality
The 1-10-100 rule is what is called the cost of quality.

It essentially says it is better to invest $1 in prevention, which is cheaper than paying $10 in correction or ending up losing $100 in failure.

cost of prevention

The cost of prevention is to get it right at the first stage itself. Whether small, mid-size, or huge conglomerates, all businesses rely on their data – customer data, sales data, invoicing, etc. Healthy data requires the right processes designs to ensure all data adheres to a standard. Verification must happen at the point of data entry. It might take $1 for the company to verify a small record in the overall scheme of things.

Once the data gets into the system, the decay begins.

For example, personal data verified in January 2016 might no longer be accurate in 2020. Names, addresses, phone numbers, and jobs change over the years. Sales data formats often change, invalidating older data. The cost of correction for data quality cleaning will take ten times the effort and ten times more resources, but it is an essential part of data maintenance.

However, if data is not cleansed, the cost of failure for a business is 100 times the initial cost. Data errors can cause a ripple effect through a company affecting decision-makers, data scientists, managers, line workers, and others. Over time, it can increase dissatisfied customers as well as acquisition rates.

Action plans to improve data quality must include continual improvements and maintenance. It cannot be a one-time thing – done and then forgotten. Ignorance of your business’ data quality is too high a price to pay.

3. Slow turnaround time

Manual data entry is time consuming.

The expected manual keystrokes for text documents data entry ranges from 10,000 to 15,000 strokes per hour. The keystrokes may be even higher for capturing text from images. With this as the benchmark, anything less than this range indicates that your company’s manual data entry process needs to be better optimized.

humans and machines

Humans are not machines. They cannot work at the same speed consistently for long periods. Fluctuations in productivity are part of the human factor, and so is the boredom that repetitive work can induce. And of course, they will be periods when no manual data entry work will be done – such as breaks and after business hours.

Data Entry Automation

Automated data entry software is the smart way to go if your organization is spending too much on manual data entry. After all, automated data entry system does not get tired, hungry, or feel bored.

iTech’s automated data entry software is built with the ability to capture and process data from multiple verticals. Depending on the project’s complexity, teams can apply OCR paired Machine Learning services or Robotic Process Automation. They can be used separately, together, or paired with manual processes depending on the input process and output needs.

Our data capture technologies can also help organizations to process unstructured documents automatically. This is important as data can be in different formats.
The results are unmatched quality, faster turnaround time, and lower operational and labor costs.

To solve all your problems with manual data entry, get in touch with our professionals today!

Reach out to our team today!


    IDS Commander iTech2021