Data integrity is a shared goal that both you and your organization strive for, not just because it is required in the contract or the regulations. Data integrity is the basis for results you can effectively quantify, measure, and rely on for critical decision-making.
As you found in learning the steps to optimal data cleaning, the entire survey process is undertaken to make informed, evidence-based decisions from the results of the survey. It isn’t a big leap to see that if the data is incorrect, the analysis will lead to inaccurate conclusions.
Successful reporting and organizational decisions, whether establishing marketing strategies, accurately reporting a data-based story, implementing effective business operations, maintaining accurate patient records and safety, or conveying accurate information depend on data integrity.
Data helps in discovering trends, describing events, documenting achievements, improving performance, controlling outcomes. However, all data is not equal. In the growing flood of data, you may reasonably ask, “How can I know the data I’ve collected is reliable and trustworthy?”
Since data is specific to the time it is collected, it is important to verify the completeness, soundness, and wholeness of stored data and third party data, in addition to new primary data. There are a number of tests to perform in testing data integrity.
These tests verify whether:
- A blank value can be retrieved from the database
- Default values are saved if user input is not specified
- Data is compatible with old data, hardware, operating systems, software interfaces
- Truncation of strings and rounding of numeric values does not occur
- Records are removed that represent defunct, incorrect, or useless data (such as customer records)
- There is a standard for filling in gaps and correcting data records
- Third party data matches your existing data rules before importing
Here is the Datassist Data Integrity Checklist that you can download to keep on hand as you work with data.
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