How do you avoid common data entry errors?

Make data entry and collection digital. Then, don’t allow null values and have predefined coded values whenever possible for strings and min and max for numerical values.

Slow down. It is cheaper to do it correctly than to go back and make corrections. "There is never any time to do it right, but plenty of time to do it over" may be true, but it is not productive or economical.

It's 2023; nothing needs to be hand-written anymore. When we create digital forms, we can control the input, the format, the acceptable entries. Every step after that is digital, so the only error can be in creating the process, but the process will work every time when it is completed. Eliminate human error by utilizing the resources available and always take data straight from one source to another. If you are having to hand key anything, it may be time to look into a new software, or RPA to bridge the gap between your integrations.

I disagree respectfully with Mr. Michael Annis. There are still workplaces out there that rely on paper forms to log data, which is entered later into a spreadsheet. These organizations should consider designing their log forms for readability: large data entry lines and plenty of white space. Furthermore, if an organization gathers and inputs data into a spreadsheet, it should consider developing a graphical user interface (GUI) to facilitate data entry. The GUI can handle much of the error checking before the data is entered into its designated cell.

Checking data source many time but error is still getting then as I know if you can calm your mind then it really help caught that error in data entry. For making mind little bit calm you have to avoid the distraction like people gupshup, other controversy, and your phone notification of social media.

Using tools to validate datasets may require some programming, such as macros in Microsoft Excel, or languages such as Python and R.

I agree with you, use an Excel form. Excel also comes with other options to help such as: Entering data quickly Data view becomes more easily

Read-back, Repeat-back. With complaints and grievances, I often write statements down from patients to elevate to leadership. I always read back to the patient the summary of their statement to ensure they feel heard while also confirming the accuracy of the data I’ve collected. I always repeat back vital information such as name spelling, correct DOB, date and time stamps to ensure accuracy. I also proof read my emails and then say them out loud to ensure accuracy and that my content easy to understand.

Consider breaking your data entry tasks into chunks, and then setting aside five minutes every hour to review your data entry. This method goes a long way to reducing errors. Furthermore, just like beta-testing software, consider having employees check each other's data entry efforts.

It is better to read your own writing as your are writing it, rather than fixing all of your attention on the provided material — and relying solely on muscle memory to carry the task. This makes input slower but speeds up the double-checking phase of data entry.

Yes, double check can improve data quality and also keep eye on error which is in the any form if we double check carefully with patience and peacefully mind then we can get better results.

Taking break and avoiding distraction that is in any form like calls, mails and also from surround people will help to make your mind little bit calm to found any error and making your data reliable .

Not enough rest will drain a worker’s potential. Too much rest will cause workers to take shortcuts to catch up. Either scenario will erode the quality of work over the course of a day. Determining how much rest is required is a balancing act that requires trial and error.

Establishing data entry standards needs to be done before any work begins. Each team member must understand both the content and the format of the data needed, and what permissions they have to the dataset. You may consider a set of rows at the beginning to explain the dataset, and appending checksums at the bottom of the columns to verify data validity when porting. Like software, datasets need to be documented and managed via version control. My experience has been that you need to design your dataset to incorporate more items that you might need, because it can be more difficult to fit additional details to your datasets at a later date.

As a human, errors and mistakes when it pertains to data entry, CMMS, data management, etc. Is inevitable. It is bound to happen to the best of us. Being able to learn from your mistakes, and acknowledgement of why those mistakes were made in the first place only leaves room for you to grow as a professional. There is absolutely nothing wrong with having an error in your data acquisition or write-up, and there is nothing wrong with taking the time to identify factors in which why that mistake was created in the first place. What could be deemed "Not Okay" in the workplace would be, not having a open mind as a professional to sit down, evaluate, analyze, and work towards bettering yourself as a professional within your data entry skillset.

Don't forget about timeline. Try to break the data task to even pieces that you can easily do everyday. Suppose you have 3 days for data entry. And you know that you can do precisely amount of data a day. This way you can finish your data entry task in time and avoid last minute rush.

Where possible, reduce rekeying data Now that there are more digital options for retrieving data, reduce the need for rekeying data by requesting digital formats that are easy to copy and paste. There is also Optical Character Recognition (OCR) technology that can also extract data from files. This still needs to be reviewed but may help in preparing it for input into the system. Additionally, when there are multiple points to key in data it is wise to automate the process or discuss changing the business process to reduce the redundancy. The fewer manual moves or copying of the data the less likely it is for errors.