Best Practices of Data Entry
Data entry is a common task for numerous businesses that collect information to store, analyze, and make use of customers or clients. When it comes to overseeing an efficient and effective data entry process, accuracy is of utmost importance. Depending on the industry, inaccuracy in the data entry process could cost a company a fortune.
Rather than waste time and energy in detecting and correcting data entry errors after the damage, firms should focus more on incorporating practices to prevent these errors during the data entry stage.
Artificial Intelligence (AI) and automation technologies are gaining ground in the data entry industry. Artificial Intelligence (AI) to reduce human-made errors. Organizations should try to enhance their data entry processes. In the meantime, embracing practices that help decrease errors, improve accuracy, and reduce the time spent on data entry processes. Below are some practices that can help ensure high-quality data entry, such as product data catalog, invoices, forms, and other documents.
Organize your Data Efficiently
An excellent point to start a successful data entry process is a logical and efficient structured data. If the collected data is not organized, difficult to read, or erratically presented, the data entry personnel can produce more errors while carrying out the data entry process. Ensure you sort the information you collect into categories or fields corresponding to their appropriate tab on the excel spreadsheet when you collect form data. Ensure that data is regularly formatted and complete.
The establishment of an outline can help reduce data entry errors. That is why it is sometimes vital to sort data entry forms before entering the data. For example, if a data entry personnel is to enter data form for 200 applicants that include their birthday, it would be convenient to classify types by the applicant’s year of birth to help elude errors. Studies have shown that humans are good at identifying and following patterns. Firms should take advantage of that in developing a data entry process that can be efficiently performed.
Keep a Data Entry Log
Every data entry assignment should be performed with the aid of s data entry log. There’s usually no need to impose a logging process or formalized journal. Even so, the data entry personnel mustn’t store information about the data set in their heads rather than write it down.
Data entry personnel should take down notes of instances where they encounter any error or difficulty, anywhere they need clarification about the type of input and where data provided is inaccurate or missing during a project. They can later use the data entry log to facilitate process improvement as a reference tool.
Implement a Standard Data Format
Firms who seek to promote accuracy throughout their data entry process should implement a standard data format to make sure there’s uniformity through the data entry process. With a standard introduced, errors become significantly reduced, and firms can either create a measure of their own or familiarize themselves with a rule that meets their needs.
A standard is a guideline and best practices for achieving a specific task. If you develop a standard data format for your business, you can design it to be specific for the type of data and how you’ll prefer to stored and present it. When creating a standard, you should take into consideration the following factors:
- The quantity and type of data you’ll collect.
- The most critical information within the data collected.
- The preferred way to format the data for efficient use in subsequent processes.
- The ideal order for data columns.
Standardization is a salient tool within a firm. When you give 200 people, more or less the same job to do, there’s a probability that each person would carry it out slightly differently. With the help of standard documentation that can show a large number of people how to perform a particular job in the same way. The most beneficial way for the firm.
Implement Rules for Data Entry
An efficient way of building accuracy into data entry processes is by creating rules and restrictions on data entry processes, rather than depending on data entry personnel to prevent data entry errors and mistakes. Firms can put rules and regulations to check that the data has been imputed in the right format or to verify the type of data attributed.
These rules can be designed to give notifications when data in a given field is entered incorrectly. For example, if a firm collects phone numbers, a rule which accepts only integers for that field can be created. They can further develop a standard that ensures the numbers entered into the log don’t exceed ten digits or required to provide the data personnel inputs the area code each time.
It is advised to be creative in implementing regulations to motivate data personnel to input data correctly.
Labels and Descriptors
Data entered into the system should always carry a label or descriptor indicating its origin and how it was manipulated if applicable. Data entry personnel should instill the habit of developing descriptive labels and names for rows and columns of data, ensuring it is done at the time of data entry. Descriptors should not include special characters or spaces.
Compatibility issues can arise due to formatting and special characters formed if the data is later analyzed with another application. The labels may contain other information like date of collection, version or project identifier, and data source.
Automate Data Capture when Possible
Automation is a trend finding its footing in the world of data entry. As firms look to process higher volumes of data while maintaining the best data entry standard accuracy and minimizing costs. Automated data entry tools can be twofold, especially when accuracy is the most critical factor. Firms can increase the amount of data processed and reduce costs by switching to automation on the one hand. The lack of a human interface in the data entry process may bring about unexpected sources of errors, on the other side.
This technology has helped the health industry in data processing. In the future, other industries are expected to adopt automation technologies. It helps facilitate cost reductions during the data entry process.