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Different Techniques To Treat Data Of Poor Quality

By analyzing the huge reserves of Big, Data Science helps the enterprises across various sectors in addressing their Data Management issues, reduces failures, & improves CIOs and CDOs pipelines. Thus, it helps the enterprises stay competitive. The process of Data Cleaning is a crucial step in the data analysis process as it helps in improving the overall accuracy of the system performance & the output generated.

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Why Data Cleaning Is Crucial?

The data that is collected from the data mining process from various sources may contain several anomalies that will surely show an adverse affect on the output of the process. Missing values from the data sets, missing of data objects or there are redundant/duplicate data objects or even corrupted data are what that contribute to data of poor quality.

Let’s discuss about some of the best strategies for handling missing data.

Eliminate Data Objects or Attributes-

The simple technique to treat data of poor quality due to missing values is by eliminating the data objects that comprises missing values. Before eliminating the data objects make sure that they aren’t of much importance & there absence shouldn’t be affecting the process of analysis.

Estimate Missing Values-

 This is the best recommended if in case you are dealing with the data of missing values. The missing values are replaced with the estimated values & varies factors are taken into account while estimating these values.

Duplicate Data-

One of the most commonly faced problems when it comes to data quality is the presence of duplicate values in the data. While deleting these duplicate values, care should be taken so that you don’t end up combining the data objects that are similar.

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