'How to Improve Data Quality in 2022' by thuwarakesh data datascience
Data quality assessment is the continuous scientific process of evaluating if your data meets the standards. These standards may be tied to your business or the project goals.
Data quality assessments play a crucial role in data governance. They help us identify incorrect data issues at various levels in a data pipeline. They also help us quantify the business impact and take corrective measures as soon as possible.Take, for instance, a data quality issue in the healthcare industry. Suppose the data entry person duplicated a patient's record; the patient would receive two doses of the drug instead of one. The consequences can be disastrous.
Form validations and database constraints help a lot in reducing completeness errors. Yet, planning mistakes often make huge impacts on the quality of data. There can be operational issues. The product returns and daily sales calculations are good examples of a lack of timeliness. Duplicate records are often easy to pick. They appear more than once in the same datasets and are relatively straightforward to remove automatically.
Another not so apparent duplication is derived information. Take age and date of birth. One is enough to find out the other. But storing both creates ambiguity.We need to perform data quality assessments for every critical area in our data store. The most granular you can go is to the field level. But you can also check up to a database level.
But in case you own a cake shop and want to send a birthday card every year, your rules can be far more flexible.
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