Quality of data plays a vital role in getting insights and results of a certain outcome. Data can be of high quality or low quality that are required for planning, decision making, operations, etc. in any business or organization. However, to get accurate insights and profitable outcome, it is important to seek high-quality data. So, to get good and reliable data, Garbage (garbled data) is to be removed ensuring that the remaining data does not contain any kind of inaccurate information. Now to obtain high-quality data, Data Cleaning or Data Cleansing starts playing the major role.
After the data collection, the first step to analyze the data is data cleaning. It is one of the most essential steps that is involved in data analysis. Data cleaning is the process of modifying the data by removing or modifying data that is irrelevant and comprises incorrect information in order to use the obtained data for data analysis. Hence, in data cleaning, either the Garbage data are replaced or cleaned.
Some individuals also have misconceptions about data cleaning. Most of them believe that data cleaning only involves the process of removing unwanted data. However, that is not the only thing done in the process of data cleansing rather it can also involve processes such as fix spelling and syntax errors, finding various duplicate data, etc. So, due to various processes involved in data cleaning, it can sometimes become a very tedious and time-consuming task.
Why is data cleaning required?
The data collected can have various problems such as empty fields, spelling errors, syntax errors, etc. which can lead to unsatisfactory or incorrect results. Therefore, data scientists are responsible for ensuring that the data which is to be analyzed is well-formatted and ensure that the unrequired fields are removed from the data. For the Data Cleaning process, data scientists can use various tools in order to clean the data for carrying out their tasks efficiently.
Methods/Approaches for Data Cleaning
The various method or approaches for data cleaning has been explained below:
Removing Irrelevant Data
Irrelevant data can be considered to be those data that are not required and do not actually fit the problem that is to be solved. For example: Let us assume that data regarding the grade of students needs to be analyzed. Also, let us consider that the data comprises a column which consists of the phone number of their parents. In this case, the phone number of parents of the students might not be necessary for analyzing the grade of students.
Eliminating Duplicate Data
In general terms, duplication is known as the replication of something. Hence, data is known to be duplicate, if the same data are repeated more than once in the dataset. The problem of duplicate data can occur when a data scientist has to combine data from various sources. Also, there can be duplicate data if a user accidentally submits their form multiple times. So, in these types of cases, the duplicated data are should be removed from the dataset with the Data Cleaning Process.
Fixing Structural error
The structural error mostly occurs either due to the slight mistake of humans or due to the incompetence of the data entry personnel. Typos, naming conventions, grammatical errors, incorrect capitalization, are some of the structural errors that can exist in the dataset. For example, The dataset can contain N/A and Not Applicable in some of the records, where N/A and Not Applicable should be considered as the same category.
Filtering Unwanted Outliers
Outliers refer to the data points which significantly differ from the other observations in a dataset. However, removing an outlier can be tricky. An individual should not remove an outlier just because it consists of a huge number. In, in some situations, those numbers can also provide insightful information in the model. Hence, the proper reason should be provided before removing the outlier. Besides, the outliers can be problematic in the case of some of the models whereas in other models it can be very informative.
Dealing with missing values
In the dataset, there can be various missing values that are to be handled before continuing the further task. There are various approaches to deal with missing data. Dropping the record containing the null value can be the first approach for dealing with the missing value. However, this can result in a loss of information. So, individuals should know well about the impact before removing the record consisting of the missing value.
Another approach for dealing with the missing values can be done by inputting missing values based on the observations. Here, an individual should keep in mind that providing the data based on observations can lead to a loss of integrity. Similarly, the final approach for dealing with missing values is done by altering the approach in which the data is being used to navigate the null values.
Verifying and Reporting
An individual should be responsible for validating the data to avoid invalid decisions that can be made after the data analysis. For instance, if an individual tends to fill the null value with a biased mindset, it can most likely violate the rules and constraints. Furthermore, in this situation, the outcome obtained from analyzing the data might not be valid. Thus, after the data is verified, a report should be developed mentioning how healthy is the data. While reporting, various software packages and libraries can be used to identify the rules violated in the process and also identifies the number of times it had been violated. Then, the cause of the errors should be considered and logged in the report.
Data is considered one of the most valuable things today. All the data which are collected might not be correct and can be full of irrelevancies. Hence, before starting the data analysis, data should be cleaned in order to obtain valuable insights from the data. Although data cleaning is considered to be a tedious task, it plays a vital role in data analysis and building models. Therefore, if the data are not cleaned properly, the outcome of data analysis might not be beneficial. So, despite being a time-consuming task, data scientists should invest quality time in data cleaning which will eventually help to gain an accurate outcome.