Data Cleaning

The process of ensuring that data is correct, consistent, and useable is referred to as data cleaning. You can clean data by finding flaws or corruptions, fixing or eliminating them, or manually processing data as needed to avoid repeating the same mistakes.

It includes editing, revising, and organizing data within a data set such that it is typically homogeneous and ready for analysis. This includes eliminating incorrect or useless data and formatting it in a language that computers can comprehend for best analysis.

Benefits of Data Cleaning

• It helps to remove errors and inconsistencies when a dataset contains data from different sources
• It helps to notify the functions of data and map how it is going to work.
• .Cleaning up data makes the organization's work more efficient and it quickly gives a hint that it is going to help in your business.
• Cleaning data and monitoring errors directly helps to fix incorrect data in the future.
• It saves time for further data transformation processes and enhances productivity.

Steps Involves in Data Cleaning

• Remove All Redundant And Irrelevant Data.

Before any observation and analysis, the goal of the process must be clear. For example, what is the business's requirement? What kind of problem do you want to solve with this data ,Etc.

This question gives a hint as to which data you have to hold and which to clean. So, the first move of the data cleaning process is to remove the irrelevant data, which is not related to the goal of your analysis. Many times, irrelevant observations are recorded in the dataset that has little to do with business. these types of things also need to be corrected because the various time zones will distract from the real goal of the analysis.

• Fix Systematical Errors.

Systematic errors are also known as structural errors. It basically contains typos, spelling errors, incorrect acronyms, strange names, etc. This type of error must be resolved because machines and applications would not be able to find these errors, and these errors may affect the analyzing process. Because the system easily recognizes dates, phone numbers, days, and so on, structural errors are not.

• Deal With Missing Data

When data is missing, various thoughts run through the mind, such as dropping calls, entering data, analyzing, and connecting with data. But any move without reviewing the data structure can spoil the complete chronology of the process. Hence, finding missing data and handling it according to the nature of the data set will lead to better data-driven decision-making. Sometimes, handling missing data does not bother us much if it is not connected to our goal.

• Validate The Data And Make a Conclusion.

After going through all the data cleaning stages, the last stage is to determine whether the cleaned data is as per requirements or not. Is answering the goal of the analysis or not? Is this data enough for business needs? 

This type of cross-checking is a crucial task in the data cleansing process. Hence, once the data starts making sense, then the further process will definitely be on the right path. Following all the processes, they must validate all the data and make a conclusion from that.

Conclusion

A good set of data is always the bread and butter for resolving business problems. But the main concern is that data must be analyzed in the proper way. Try to take advantage of modern tools for all the processes, like data cleaning. Data cleaning may be a lengthy and annoying process for some time, but from a greater perspective, it will help to complete the data analytics journey for smooth operation and to avoid errors.

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