Comprehensive and accurate data is the most powerful weapon in the arsenal of any business. A high-value database is one of the biggest assets for businesses. It involves countless data elements extracted from multiple resources after extensive research that will help marketers to the device the most appropriate promotional strategies to stand out in the competition. Businesses and organizations can achieve complete success in their data entry projects by adopting the best practices of data entry.
Data thus sourced should be consistent and reliable to be of use for businesses in tailoring strategies. Businesses have started investing in analytic tools that will provide valuable insights into customer behavior, markets trends, and competition, which will enable them to predict future requirements to some extent.
Here are the top 10 best practices of data entry that will ensure its accuracy and completeness. 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.
Best Practices of Data Entry
1. Quality standards:
The first step to ensuring data quality in compliance with the quality standards that the company may have devised. The data input should conform to the standards set by businesses. Establishing rules on data entry will go a long way in ensuring accuracy into your processes. Rules can be implemented to verify the data type and to make sure that the data has been entered in the correct format.
2. Right format:
The data sourced should store in the right format as your business may need. Make sure that the outsourcing partner has the right tools and software to effect data conversions in multiple formats efficiently.
3. Consistent data:
While inputting the data, care should be taken to ensure its consistency throughout the organization and in tune with the formats and validation rules that are in place.
4. Organize Your Data Efficiently:
If the data collected is in a fragmented form, it will be difficult to decipher and could produce more errors during the data entry process. Make sure that the information collected is sorted into fields or categories in a spreadsheet for easy access and sharing across various departments.
5. Complete data:
Data should be in a formatted and complete form to be used in analytics and BI reporting. Incomplete data may project incorrect results and inconsistent analysis.
6. Missing entries:
During data entry, care should be taken not to leave any fields blank and the data should be input consistently across the organization. For instance, if the mobile number is missing, it should be uniformly store as zero across all the databases.
Labeling data indicating the source will go a long way in maki