When entering and storing information, an error may occur and the database becomes occupied by wrong data. Therefore, during a process, it is tried to clear all the errors and incorrect data from the data set.
Most of the errors that occur are unavoidable and mistakes may be made even during data transfer and copying. For this reason, data cleaning is very necessary and helps the data coherence. Failure to clean the data will have a negative impact on the data analysis and wrong results will be obtained.
You can also read the preprocessing and data preparation article.
Data cleaning is actually a process during which worthless and wrong data are found and if necessary corrected or deleted. Usually, this process is done by scripts in the database, after cleaning and modification, the data set should be consistent with other data.
The purpose of data cleaning is to obtain an accurate, valid and complete data set that will help you in data analysis. “For a data to be complete and accurate, it should be the closest to real data and its information and records should be fully recorded”.
– Data cleaning has a positive effect on the process of correct data analysis and has many advantages in improving the decision-making process.
– Data cleaning will increase the quality of data and it will facilitate the right decision-making process for the organization, thus saving the organization’s time and energy.
– Data cleaning increases productivity and provides you with valuable insights.
– By data cleaning, all the needs of the customers will be identified and it will speed up the process of responding to the customers. And this acceleration in the process of responding to customers will achieve favorable results.
The first technique in data cleaning is to remove duplicate and irrelevant data. Duplicate data is the result of recording information in different parts of the database, and you can clean the data by merging the relevant parts and removing duplicate information. Removing irrelevant data from the database makes analysis easier and brings you closer to the main goal, thus minimizing data and making it easier to control.
The second technique is to fix structural errors. Structural errors include typos and wrong names. To determine the validity of the dataset, it is necessary to filter outliers from the database. Outlier data is data for which there is no logical justification. For example, a child who is eight years old cannot have a bachelor’s degree. The above information is outliers.
The third technique in data cleaning is to deal with variables for which no value has been recorded. In such cases, you should either ignore this category of variables or enter their values based on other variables. Of course, entering information based on defaults will disrupt the integrity of the data. For this reason, empty variables are ignored; Most algorithms do not accept missing values and are not executed.
Finally, you must validate the data in the database and ensure the quality of the data. In this section, you must answer several questions related to the data set in the database. Does the data make sense? Does the data in the database follow the rules? Does the data help in the process of forming the next theories? The answers to the above questions can determine the quality of the data.
The methods that cause the data cleaning process include the following:
1-Monitoring errors, which identify and prevent errors.
2- Data validation, in this method, the accuracy of the data is checked to ensure their correctness.
3- Using functions to update data, which will save time.
4- Using data cleaning software and tools, which is the best solution for those who do not have enough knowledge to do this. It should be mentioned here that Bigpro1 tools facilitate the process of data preparation and cleaning and allow people to analyze a large amount of data online and achieve a favorable result.
The Bigpro1 platform has practical tools for collecting, discovering, enriching and cleaning data, which facilitates the decision-making process and helps to advance your business goals. By registering on the site of this collection, you can benefit from the amazing services of Big Pro1 and get valuable results from the data set in the shortest possible time.
These results can help your organization’s progress and identify existing potentials and opportunities. In this way, you can also save time and energy and the data cleaning process is done without errors. It doesn’t matter how big or small your business is, the only important thing is to use up-to-date and accurate data that will make your progress faster. But this is not easy to do, and to access accurate data, they must be cleaned.
Data cleaning is the removal or correction of wrong data from the database, which will help in data analysis. In this article, in addition to the description of data cleaning, the importance of data cleaning, its impact on the business success process and the introduction of the amazing data cleaning tool Bigpro1, as a part of data pre-processing and preparation in the Bigpro1 dashboard, were mentioned. But the final decision-maker for using the tools and achieving the peaks of success is you.