How to Enhance Data Accuracy with Data Entry Best Practices
Data entry is a routine task for thousands of businesses that collect information from their customers and wish to store, analyze, or otherwise make use of it. When it comes to governing an effective data entry process, accuracy is the name of the game. Depending on the industry and situation, a single inaccuracy in the data entry process could result in as little as a few pennies or as much as thousands of dollars in additional costs.
As with other applications, human involvement in the data entry is giving way to AI and automation technologies that promise to reduce man-made errors. In the meantime, organizations should seek to enhance their data entry procedures by adopting best practices that help to reduce errors, increase overall accuracy and decrease the amount of time spent on data entry activities. Here are seven best practice guidelines that organizations can follow to optimize their processes for data entry.
Organize Your Data Efficiently
A good starting point for a successful data entry project is efficiently and logically structured data. If the data that you collect is disorganized, erratically presented or difficult to read, a data entry clerk is bound to produce more errors during the data entry process. When you collect form data, ensure that the information you collect is sorted into fields or categories that correspond to their appropriate place on your excel spreadsheet. Ensure that data is consistently formatted and complete.
It is sometimes useful to sort forms for data entry before entering the data, as the establishment of a pattern can help reduce errors. For example, if a data entry clerk is to enter form data for 100 applicants that include their birthday, it would be useful to sort forms by the birth year of the applicant to help avoid errors. Humans are good at detecting and following patterns, and organizations should make use of that in designing a data entry process that can be performed efficiently.
Keep a Data Entry Log
Each data entry project should be conducted with the aid of a data entry log. There’s often no need to implement a formalized journal or logging process, but it is important that data entry clerks do not venture to store important information about the data set in their heads when it should be written down.
Data clerks should make notes of instances where data provided was missing or inaccurate, any fields where clarification is required as to the nature of the input and any other errors or difficulties encountered during the project. A data entry log can be used later as a reference tool to facilitate process improvement.
Implement a Standard Data Format
Organizations who wish to promote accuracy throughout their data entry processes should implement some kind of data standard to ensure uniformity throughout the data entry process. Errors are significantly reduced when a standard is introduced, and organizations can either acquaint themselves with a standard that matches their needs or create their own standard if appropriate.
A standard is a system of best practices and guidelines for accomplishing a specific task. If you create a data standard for your own business, it can be customized to be highly specific for the type of data you collect and how you would like it stored and presented. When establishing a standard, you should consider factors such as:
- The type and quantity of data that will be collected
- The most important information contained within that data
- How the data should be formatted for efficient use in subsequent processes
- The optimal order for data columns
Standardization is an important tool within organizations. If you give 100 people the same task, there’s a good chance that each person would perform it slightly differently. When you create a standard, you’ve created documentation that can show 100 people how to perform a single task in the exact same way – the way that’s most beneficial for your organization.
Implement Rules for Data Entry
Establishing rules and restrictions on data entry is an efficient way of building accuracy into your processes rather than simply depending on your data entry clerks to avoid input mistakes and data entry error. Rules can be implemented to verify the data type that has been entered (string, number, integer, etc.) or to ensure that the data has been entered in the correct format.
Data entry rules can be set up to deliver a prompt or error message when data in a given field is entered incorrectly. Suppose your organization collects phone numbers – you can create a rule that says that the field for phone numbers only accepts integer values – no other characters are allowed. You can further require that entered phone numbers are ten digits long to ensure that data clerks enter the area code each time a phone number is entered.
Use your creativity in implementing rules that encourage (or force) data clerks to enter data accurately.
Leverage Labels and Descriptors
Data that has been entered into the system should always be accompanied by a label or descriptor indicating what it is, where it came from and how it was manipulated, if applicable. Data entry clerks should practice creating descriptive names and labels for rows and columns of data, ensuring that labeling is done at the time of data entry. Descriptors should not include spaces or special characters – if the data is being analyzed later with another application, special characters or formatting can create compatibility issues. Useful labels may contain additional information such as the data source, date of collection and, version or project identifier.
Automate Data Capture Whenever Possible
Automation is a growing trend in the world of data entry as organizations seek to process higher volumes of data while minimizing costs and maintaining the best possible standards for data accuracy. When accuracy is the most important factor, however, automated data entry tools can be a double-edged sword. On one hand, organizations can achieve cost reductions and process increasing amounts of data by switching to automation. On the other hand, the absence of a human interface in the process may introduce additional unexpected sources of error.
The health insurance industry processes millions of claims forms each year with the help of a special automation technology called Optical Character Recognition or OCR. This hardware and software tool is comprised of a scanner that can create a digital version of any paper health insurance claim and a software application with error detection that recognizes written characters and encodes the data accurately.
While this technology allows the health insurance industry to process an extremely high volume of claims, data entry conducted with an OCR must still be checked and validated by a human claims processing agent to ensure that the data is entered correctly. In the future, we expect that other industries will begin to adopt automation technologies to help facilitate cost reductions throughout the data entry process.
Until we can teach AI robots to conduct data entry for us, it’s important that we implement and follow best practices to minimize costs and maximize accuracy in data entry. Organizations should implement a standard data format that encourages consistency in data entry and consider building accuracy into the system with data entry rules that ensure the validity of inputs in a given field.
Data entry clerks can further enhance their operational processes by organizing data logically prior to entry, maintaining a data entry log that tracks and difficulties or errors, and using proper labels and descriptors to tag data and ensure that it is logically organized. Finally, organizations should seek to implement automation technologies to reduce costs in data entry while maintaining a human interface for checking and validation.
Many organizations have also found success in data entry by outsourcing the entire process to a trusted partner, as part of a largest trend towards back office business process outsourcing. Outsourced data entry has yielded exceptional results for many organizations – will yours be next?
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