Data entry is a key ingredient in the data management process because its success is largely dependent upon the quality of your data entry process. The ultimate outcome of a successful data entry operation 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 attune to the achievement of this goal. A competent data entry outsourcing service provider will ensure the best data entry practices.
Rather than wasting time and energy to detect data entry errors during downstream stages of data processing when it has already caused damage, it is best to incorporate practices during the data entry stage itself, which would help to track errors and rectify them. An expert online and offline data entry service provider would ensure that these practices are in place.
Here are some actions that can help to ensure high-quality image data entry as well as data entry from other sources such as forms, product data catalogs, invoices, and other documents.
Data Entry Best Practices to Enhance Data Accuracy
Here are some best practices of data entry that will enable organizations to achieve complete success in their data entry projects.
1. Compliance of Data Standards:
All data entry projects should have a basic set of standards for the data entry operators to comply with. These standards should ideally be compatible with the data entry system and the source of data.
2. Data Validation:
Make sure to correct the data entered to remove errors at the source. It will help to save a lot of time and effort in fixing errors post data entry. Spreadsheet tools like Google Docs and Excel Files ensure simple data validation tools that allow the user to have a track on the information to enter the database. Using a specific data entry form is another way to ensure the accuracy of data elements in a spreadsheet. Relational databases make an effective way to store and complex and high volume data though they are more difficult to learn.
3. Post Data Entry Storage:
The data entered should be saved in a format that can be read by any application, anywhere. Make sure you use the current format as the data would get corrupted or even lost when formats become obsolete. There are many open and unencrypted formats including ASCII, and Unicode that can be considered.
4. Familiarization of Common Data Entry Errors:
Data entry operators should be trained to identify common data entry errors like inconsistent formats for the name, location, contact fields or wrong order of columns among others. These errors can avoid easily by taking adequate care and by using some efficient data cleansing strategies.
5. Descriptive Names:
Make it a practice to use descriptive names for files and columns, which makes it easy for data entry operators to use them. It is recommended not to use spaces or special characters in the names as it may create problems when the data file is used for subsequent analysis. Useful information like date, version, project, etc can also be included.
6. Consistency in Column and Row Filling:
The data should enter consistently in the same way in a single data sheet and not in columns in different locations. Labels can use to tag different columns to understand the data and various functions like how to convert them to another format.
Missing data items can lead to data loss, which may be difficult to find out at a later stage. So, make sure the data omissions are identified and located distinctly during data entry procedures. You can leave the column blank or assign a null value to denote columns where data is missing. In text fields, make it a practice to use NA or Not Available in the missing data field.
Make sure to fill all the cells in a single line completely, without any empty cells, to ensure proper sorting.
8. Maintenance of Log:
Keeping a data entry log is another good practice. It will ensure a reliable record of errors and difficulties that you encountered when carrying out data entry. The log can have the number of fields where data is missing or where inaccurate data has been entered. It will ensure a transparent and systematic account of the process efficiency and can be useful in fine-tuning the data entry process and project management. It is also highly useful in tracing back errors detected later.
9. Manual Validation:
When you need to carry out large volumes of data entry, you can also resort to manual validation to ensure absolute accuracy.
These are some of the best data entry practices will ensure the accuracy and completeness of data entry projects and increase the reliability of business information analysis. If you wish to comply with the best data entry practices in your data management. Get the professional services of well-experienced data entry outsourcing partner like Data Entry India Outsource with a brilliant track record of handling all types of data entry tasks.
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