Companies and organizations use data for research, strategic planning, lead generation, education, and more. The way we deal with data has changed industries. For instance, insurance companies are using automated methods to process 93 fields of data from ACORD 25 forms. When it comes to important data, speed and accuracy are important.
As more data accumulates for business use, the challenge is that entities now struggle to handle that data. As a result, the US Labor Bureau predicts a 3% growth in data entry jobs going into 2026. But there are still issues with manual data entry, and companies still want to prevent data entry errors.
How can managers or data encoders themselves ensure more accurate data entry? Here are some best practices.
1. Practice, practice, practice
The first tip is undoubtedly non-negotiable practice. Data entry is a skill that has a learning curve. And the more people that do it, the better they will become.
One significant factor in data entry accuracy is typing accuracy and speed, which various online tools can measure. A good example would be TypingTest.Com, which can count a person’s words per minute and typing accuracy.
When hiring data entry staff, getting someone with a track record of reliable data typing or entry is the golden standard, experience is always a big plus in any job, and it’s no different with data entry.
2. Provide training
For data encoders who want to get better at data entry and encoding, there’s good news—it’s a highly trainable skill. Many people have mastered all kinds of encoding practices that provide better results and have translated those learnings into easy-to-learn programs.
For people who want to get into medical encoding, it’s always a big plus to get online medical coding training before applying for a job. For companies with data encoders, providing your incoming and existing staff with this training can benefit the company just as much as the employees. People development is always an excellent investment to make.
3. Understand the context
A lack of understanding of what’s being encoded can often lead to a lackluster performance. Many data entry error examples have to do with not understanding that the context can be grammatical. In other cases, it’s also cultural. For example, getting a non-Spanish speaker to encode Spanish names, addresses, and other content will be more prone to error than getting someone who knows the language inside and out.
Always hire people who get the context of what they’re encoding. For example, when hiring data encoders for hospitals, look for people with relevant education or background. Context is king—and that applies in data entry too.
4. Provide layers of checking
Of course, to err is human, so it’s hard ever to think that data encoders will never make mistakes. That’s why providing layers of checkers helps avoid or limit those errors.
In the case of literary-based encoding, get a proofreader to look through your content before sending off anything for deployment or publishing.
Checking numbers can be more complicated, but it’s not impossible. Getting someone with a keen eye for detail to check the output before sending it out should be a best practice. In most cases, there is also AI scanning software available to look through the content and check for any errors in data entry.
5. Run through grammar checkers
A common way to automatically check written content is to use a grammar checker. This age of digital transformation has highlighted the benefits of software services to help minimize human resource costs and get better results on repetitive work. Grammar checking belongs in that category.
Grammar checkers, mind you, are also not 100% accurate, so keeping those human layers of checking will still be necessary. Although with grammar checkers, you might not need as many editors as you would if you had to do it manually.
6. Use text extraction software
One of the many reasons to have data entry personnel is to manually encode handwritten or hardcopy files into soft copy versions. In these use-cases, you can save time and limit errors by using data extraction software. OCR data extraction has dramatically improved over the years. And while dealing with handwritten notes might be an issue, AI-powered extraction software continues to become refined and will only get better with time.
Artificial Intelligence Empowering Data Entry
Artificial intelligence applications are undoubtedly improving the way we work. It’s no wonder that the artificial intelligence market will likely grow to $360.36 billion in 2028 at a CAGR of 33.6%. For now, many companies are enjoying the benefits of Human-In-The-Loop (HITL) as they become more comfortable with proven AI-powered solutions like Base64.ai doing the heavy lifting. As more people and industries pivot towards automation, the results will get more accurate and we’ll all be able to get work done faster.
