Top 10 Best Practices of Data Entry

Jack Taylor
August 17, 2022
 Mins Read

Data entry is a very important and critical part of the entire data management process. The success of data management depends upon the quality of your data entry process. Anyone who wants to carry out a data entry project must remember that the ultimate outcome of data entry is to create a valid and organized set of data, in a specified format, which can be accessed and used easily. The entire data entry process and its outcome should be attuned to the achievement of this goal.

Here are top ten best practices of data entry that, if practiced well, can help organizations and businesses achieve complete success in their data entry projects. A competent and experienced data entry outsourcing service provider would certainly implement all best practices that contribute to greater than 99% accuracy of data entry tasks.

1. Ensure Data Standards Are in Place:

All data entry projects must have a minimal set of standards for the operators to comply with. These standards should be compatible with the kind of data entry project – the data entry system used and the input sources of data.

2. Data Validation:

A second best practice of data entry is to ensure that you correct the entered data, post data entry. Spreadsheet tools like Google Docs and Excel Files provide data validation tools that allow the user to control the kind of information to enter the database. Use of these tools can provide users with choices and restrict entries to a specific kind. In this regard, use of a data entry form can ensure more accurate data entry as compared to entering data in a spreadsheet.

Through a data entry form, the creator can enforce data entry rules at the time of data entry. The filled data can then be inserted into a spreadsheet. Excel also has comparable data validation tool in the form of dropdown-lists that can help the enforcement of data entry rules.

In comparison, relational databases offer a powerful way of entering and storing data especially in the case of complex data and high volumes of data but they are more difficult to learn. They too have data validation tools that ensure the enforcement of data entry rules.

3. Post Data Entry Storage:

Once you carry out the data entry task, ensure that it is saved in a format that can be read by any application, anywhere. Avoid using a proprietary output format, as the data would be lost when that format becomes obsolete. Good non-proprietary formats include ASCII, Unicode and are open, unencrypted and uncompressed.

4. Familiarization with Poor Data Entry Practices:

Data entry operators should be trained in recognizing and identifying common and uncommon data entry errors. Some of these errors are very often found in data in excel files, where different personnel enter data at different times. These errors include:

  • Inconsistent formats for name, location, contact fields which results in confusion and difficulties in making sense of the data
  • Wrong order of columns
  • Inserting different types of information in one column

These errors should be rectified so that they are not repeated again. Making use of efficient data cleansing strategies can help in this.

5.Descriptive Names:

One very good practice in Excel based data entry is to create descriptive names for files and columns—one that does not have spaces or special characters. The latter may create problems when the data file is used for subsequent analysis. The descriptor may include details such as source, date, version, project etc.

6. Consistency in Column and Row Filling:

Ensure that data is entered consistently in the same way in a single data sheet and not in chunks or blocks located in different places. By tagging different columns with different labels which indicate their alphabet or numeric status and consistently filling them with numbers or letters, it becomes easier to understand that data and work on it-such as transferring them or converting them to another format or for carrying out analysis on it.

7. Missing Data:

Most of the time datasets end up having missing data. These can lead to significant losses if they are not identified and located distinctly during data entry procedures. There are several ways of treating missing data:

  • One way is to leave the field empty i.e. to assign a NULL value or NO value to it.
  • Enter 9999, a distinct value, to indicate a missing number in a numeric field, if you cannot assign other values.
  • In text fields, it is good practice to use NA or Not Available or Not Applicable in the missing data field.
  • Data flags can be placed in a separate column to define the missing value.

8. Complete Lines of Data:

Spreadsheets may be powerful but not entering data properly in a spreadsheet can lead to issues. During sorting columns get sorted independently. Hence, it is good practice to ensure that all cells in a single line are filled completely, without any empty cells, to ensure that sorting gets done properly.

9. Keeping a log:

Keeping a log is vital when you are carrying out data entry. It provides a record of errors and difficulties that you encountered when carrying data entry out. Each project would require an entry log. The log may record the number of fields from which information is missing, in which wrong and inaccurate data has been entered; fields which need clarification; when was the error noted, when was the action taken.

It provides a systematic account of the process efficiency and can be useful in fine-tuning data cleansing and project management. The data entry project manager has to take responsibility for ensuring the completeness and accuracy of the log. The log can be useful in tracing back errors detected later.

10. Automate:

When necessary, use automation to carry out large volumes of data entry but remember that manual validation would need to be done in order to ensure the accuracy.

Following these top ten best practices of data entry shall certainly help increase the accuracy of their data entry projects that will increase the reliability of business information analysis.

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