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Different Types Of Errors Associated With Data Quality

As a part of the data analysis process in Data Science, data is mined from different sources. In most of the cases, the data collected from the Data Mining process cannot be directly subjected to analysis as it would be having several data quality issues. Addressing the data quality issues at the time of Data Mining is very crucial step which would otherwise lead to undesirable results.

The data quality issues would often arise due to multiple reasons such as human error, limitations of measuring devices, or flawed data collection process.  The most commonly seen data quality issues are missing or null values, data duplication, missing data objects, redundant/duplicate data objects, etc.. So, the process that is used to address the data quality problems is known Data Cleaning.

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Now, let’s know about the different types of errors that are associated with Data Quality.

  • Measurement Error

These are the errors that arise at the time of measurement process. At times, the recorded data values may not exactly match with the actual values. Measurement Error is the difference between the measured and true value.

  • Data Collection Error

The errors that are obtained as a result of omitting data objects or attributes values, or including an unnecessary data objects known Data Collection error.

  • Noise

Whenever we add a distorted value or objects that aren’t required then this is known as noise.

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