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Data Quality vs Data Integrity — Brief Guide
In this story, I am going to talk about two uncommon topics in the field of data analytics. Data Quality and Data Integrity.
I would go through each of them and explain the importance of the same in your work as well as the business you are supporting by doing data analysis or machine learning process.
What do you mean by Data Quality?
When I am saying Data Quality it means development and implementation of activities that apply quality management techniques to data in order to ensure the data is fit to serve the specific needs of an organization in a particular context. Data that is deemed fit for its intended purpose is considered high quality data.
Why it is important?
High quality data can be processed and analyzed quickly, leading to better and faster insights that drive business intelligence efforts and big data analytics.
Dimensions of Data Quality
So there are about six main dimensions of data quality: accuracy, completeness, consistency, validity, uniqueness, and timeliness.
- Accuracy: The data should reflect actual, real-world scenarios; the measure of accuracy can be confirmed with a verifiable source.
