In an organization without a data quality checklist, there is no guarantee that the data will be high quality, and there might be duplicates and other data redundancies. Let us first explore the term data quality.
What Is Data Quality?
Data quality refers to the class of the data being used. If we say that the data is of high quality then it means the data is fit for its intended uses in the operations of a company, as well as in planning and decision making. It means that the data represents reality in the most effective manner.
Why Is A Data Quality Checklist Needed?
These days many organizations experience data quality issues because different employees in the organizations enter the data differently. When there are third parties involved in the process, the data is likely to become even less reliable. Data duplication and other data quality issues appear throughout the database. In order to maintain data credibility in the organization, it is very important to enhance data quality and so proper steps for data quality management must be taken – this includes creating a data quality checklist. Here are some benefits of creating such a checklist in the organization.
Benefits Of Creating A Data Quality Checklist
1. helps to improve data quality over time
2. assists in defining business rules to which your data should comply
3. ensures that data is compliant with the business rules you defined
4. enables employees to leverage high quality data to improve sales through increased speed and accuracy of data processing
5. ensures an improved level of data quality, including benefits such as deduplication
6. Regular and timely data quality assessment can be performed against a checklist. This will help ensure that no important aspect of data quality is ignored, and that each and every aspect of data is checked for quality levels in the organization.
7. Data quality assessment standards can be implemented throughout the organization and a threshold point can be determined for the tolerance level of dirty data in the system.
8. All the aspects of data quality like data reliability, data consistency, data integrity and data enhancement can be measured with standards and uniformity throughout the organization
9. With periodic updating, the checklist can be made more comprehensive to address changing needs and to cover all factors necessary to ensure continued data quality in the system.