Data clearing, also known as data scrubbing and data cleansing, is important for any serious organization in creating a quality data decision-making culture. In simple words, if the input is garbage data, the analysis output too would be garbage. Let us look deeper into it and understand data clearing and settlement better.
Data Clearing Explained
Data clearing or data cleansing is a unique process of removing or fixing corrupted, incorrect, incomplete, incorrectly formatted, or duplicate data in a dataset.
It is a general mistake that sometimes data is mislabeled or duplicated while combining from several data sources. The end result with such incorrect data, the algorithms and outcomes become unreliable without actually knowing that the analysis is incorrect. It is sad to say that there is no single or perfect way to the data clearing process can be carried out as things differ across dataset. However, it becomes important to template the process so that it is understood the right way is followed.
Data Clearing vs Data Transformation
In data clearing the unnecessary data is removed from the dataset while in the data transformation the data structure or format is changed and it is also sometime called as data munging or data wrangling.
How Data Clearing is Processed
The data clearing techniques vary across datasets and below are the basic steps discussed for the mapping out of a framework.
Removing of irrelevant, duplicate data
The first step is to remove irrelevant or duplicate observations from the dataset. Such data generally tend to come if the data is received from multiple sources.
Fixing of Structural Errors
Structural errors are noticed when data is transferred or measured. These are like typos, incorrect capitalization or strange naming conventions. These causes mislabeled classes or categories. For example, Not Applicable and N/A may appear in the data and should be rooted in the same category.
Filtering Unwanted Outliers
It cannot be guaranteed that an outlier could be incorrect. It may prove a theory sometimes while working on data clearing. Hence, the filtering step is needed to know the validity of the outlier. If proved to be irrelevant, it can be removed.
Handling Missing Data
Missing data cannot be ignored. There are some ways to deal with it. The first thing to do is dropping observations which are equipped with missing values. It is important to know that the information will be removed by doing so. So it is better to have the mindset before trying it out.
If not, there is a second way. Simply input the missing values based on the observations of other parts. However, here too lossing integrity of data may strike up.
There is a third way. It is suggested to alter the way the data is being used. This may navigate null values.
Validating and QA
Following the data clearing there are some questions to ask.
Does the new data make sense and follow appropriate rules?
Is there any finding of trends to help in the next theory?