To understand the question, the difference between data quality and data integrity should be known. In this article, we answer the question, “How do good data quality methods impact your data integrity?” We will break down the meaning of data quality and data integrity, their differences, and the elements that impact the two.
What is good data quality?
Good data quality, in simple terms, would mean that the data present is accurate and reliable and passes various Data Quality dimensions. In other words, data quality can be defined by the overall positive utility of data that further assists in exercising prudence that has immediate effects on a business.
What is data integrity?
Data integrity goes beyond the simplified version of data quality. Data integrity is the product of data quality, where its efficiency is tested. The data will have to be complete, accurate, and consistent. Data integrity is the holistic version of the integrity of data, quality of data, enrichment, and location intelligence.
How do data quality methods impact data integrity?
Data quality is the key element of data integrity and the pillar that defines its usefulness. Data quality and data integrity are inextricably linked and cannot exist without one another. Since businesses have begun to understand the importance of data quality, companies such as us, uArrow, have found a means through our data Quality products to not just rate the quality of data, but instead define the usefulness of data through data integrity.
Some of the common issues that can be a threat to data integrity are
- Erroneous entry
- Transfer error
- Security issues
How does uArrow stand out from other data integrity solutions?
The added feature that is unique to uArrow is the integrity checks that are run on the data, which is further extended to Foreign Key and Bridge Key.
The user will be able to perform a foreign key on single and multiple columns. The foreign key helps in connecting two columns and provides a cross-reference.
The bridge key, on the other hand, is a multi-column that conducts a referential check between two tables.
Data integrity is a major part of data quality. uArrow understands its importance and strives to achieve integrity during the initial stages, making the data reliable, consistent, and accurate. In this article, we have answered the question, “How do good data quality methods impact your data integrity? We shared the difference between data quality and integrity and have shared the means by which data quality can be achieved with uArrow.
Contact us for more information to understand the importance of data integrity as a key element of data quality.