Data has been an inevitable resource for decision-making for businesses. While we frequently discuss the positive effects that data can have on the operationalization of an organization, it is also necessary to discuss bad or dirty data. Such identification is important as this data is likely to do more harm than good.
Why Clean data??
The process of first discovering any errors or faulty data, then systematically addressing these issues is known as data cleaning, data cleansing, or data scrubbing. If the error in the data cannot be fixed, the undesirable aspects should be deleted to fully clean the data. Human mistakes, data scraping, or merging data from numerous sources are all common causes of erroneous data. Before studying this poor data, there is an utmost need to clean it up, especially if data has to be used in machine learning models. This is because it has the potential to provide inaccurate or misleading information. If a business relies on these insights to make critical management decisions, this might be disastrous as well as expensive.
Having clean data helps in completing the research much more quickly. By completing this activity ahead of time, a significant amount of time can be saved. Also, businesses can avoid several problems if they clean data before utilizing it. Moreover, there is a good chance that the organization will have to redo the entire task, which will waste a lot of time.
Is Data Cleaning and Transformation Same?
Data cleansing is the process of removing data from your dataset that does not belong there. The process of changing data from one format or structure to another is known as data transformation. Data transformation, often known as data wrangling or data munging, is the process of changing and mapping data from one data type into another for analysis.
Let us consider a scenario to support the usage of data cleaning steps. Suppose you have put a lot of effort and time into analyzing a specific group of datasets. You are very eager to show the results to your superior, but in the meeting, your superior points out a few mistakes the situation gets kind of embarrassing and painful. Wouldn’t you want to avoid such mistakes from happening? Not only do they cause embarrassment, but they also waste resources. Data cleansing helps you in that regard full stop it is a widespread practice, and you should learn the methods used to clean data.
Data Cleaning Steps:
Following steps should be followed for data cleaning:
- Remove Duplicates: When data is collected from various sources, there is a high possibility that there will be an inclusion of duplicate items. These duplicates could be the result of human error, such as an error committed by the individual entering data or filling out a form.
- Remove Irrelevant Data: Any analysis that you conduct will be slowed and muddled by irrelevant data. So, before cleaning your data, there is a need to figure out what is relevant and what isn't. Some examples are personal identifiable data, URLs, HTML tags, boilerplate text, excessive blank space between text, and many more.
- Standardize Capitalization: It should be ensured that the text within your data is consistent. If a combination of capitalization is used, it can end up with a bunch of distinct erroneous categories. In the text mining approach, it is usually suggested to put everything in lowercase to analyze the data with a computer model.
- Convert Data Types: When cleaning data, the most common data type that needs to be converted is numbers. Numbers are frequently misinterpreted as text, yet they must appear as numerals to be processed. They are classified as a string if they appear as text, and analytic algorithms cannot solve mathematical equations on them.
- Fix Errors: It should go without saying that any inaccuracies in data should be thoroughly removed. Typographical errors, for example, could cause you to miss out on important data insights. Some of these can be prevented with a quick spell-check. Misspellings or unnecessary punctuation in data could result in you losing contact with your consumers. Inconsistencies in formatting are another type of error.
- Language Translation: The Natural Language Processing (NLP) models that underpin data-analysis tools are mostly monolingual, which means they cannot handle many languages. As a result, everything will have to be translated into one language.
- Handle Missing Values: When it comes to missing values, there are two options: delete the observations with the missing value or fill in the gaps. What you do depends on your analytical aims and what you want to do with the data after that. Removing the missing value entirely may result in the loss of vital information from the data. As a result, it might be preferable to fill in the blanks by researching what should belong in that field. If you don’t know what it is, you may use the word missing instead. You can put a zero in the missing field if it's numerical.
Dr. Himanshu Sharma
Assistant Professor
Jaipuria Institute of Management, Ghaziabad
