Get Answer: Dirty Data Described Question Guide

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Key Explanation

This topic involves dirty data described. A strong answer should include explanation, application, and examples.

Original Question

Dirty data can be described as inconsistent, incomplete, or inaccurate data that compromises its usefulness, reliability, and integrity. Accurate and complete data is crucial to data analytics and having multiple methods to cross reference and ensure you have accurate data is also vital. Without proper validation and cleansing methods, healthcare data can become inconsistent which can lead to errors in patient care. Dirty data can be caused by many different ways such as human errors, duplicate data, missing date, or outdated data. Human error is the leading cause of inconsistencies in data entry with studies showing that at minimum 1% of entries can be wrong. (Smith, P., 2020, June 3) Duplicate records can often be the result of variations in patient names, demographic details, or record inconsistencies, which can compromise the accuracy of medical histories. Outdated data such as expired insurance details or changes in a patient’s medical condition can also contribute to inaccuracies. Lastly, missing data can be caused by incomplete patient records or system failures, which can also contaminate the quality of healthcare data. One strategy that different companies can put into place is using an automated data-validating system. An automation system can help to prevent real time errors from occurring and stopping them at the initial entry for data. Reply to this discussion, ask a question, cite and give a reference

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