The Most Popular Data Cleaning Techniques Used Today.

Data is a new asset of income. This data is assisting many organisations to accelerate their earnings. Whether integrating the data from a survey or from landing pages data is gardening. Data gardening or collecting raw data comes with multiple challenges. Where soil acts as raw data, plants act as labels, flowers act as a meaningful insight. And the most important season changes are the prediction.

“Data Mining” is a practice where one can dig any sort of data. This data mining brings various challenges with it. The fuzzy/messy data bring non-essential values and terms. To manage this crude/messy data, “Data scouring” is essential. “Data Scrubbing” is otherwise called data cleaning. Data cleaning techniques involves perceiving unessential, inadequate, erroneous and grimy data.

Then, cleaning or replacing these dirty parts with fresh or valuable data. Although some find it boring and time-consuming, nonetheless it generally helps to the efficiency of data analysis. Furthermore, subsequent cleaning, investigation and representation happen to create significant experiences from it. These valuable insights serve the data scientist to develop a Prediction Model accordingly.

So it becomes crucial that cleaned data must be accurate. However, if its accuracy fails then this clean data gives fuzzy results. Which will be of no use. Indeed, to establish the best data cleaning techniques this article covers 7 + one data cleaning steps to assure the best technique in hand.

Why data cleaning is advised? 

Data Cleansing, Data Cleaning or Data Scrubbing is the operation of identifying bad data or any issue in that data, then proceeding to correct this sort of defiance. For some instances data is immutable, it is advised to remove messy/bad elements properly.

Data blending, scraping of data, and human error are the real causes of unclean data. Multichannel or data blending from numerous sources causes inconsistencies in the resulting data set. And this is the expected part of data mining.

Additionally, whenever you are running an ML (Machine Learning) model it really recommended cleaning the data before stepping into an ML model. If not done in a systematic way then it might lead to incorrect prediction or misleading insights. If the practice continues with respect to messy data then it will definitely devastate the business decisions. This is the reason why the data cleaning technique is really important. To quote this term in a short statement it will be “JUNK IN JUNK OUT”.

Basic Data Cleaning Technique used to clean messy data

Foremost, understanding the case study, the purpose behind the data extraction. After cleaning the data, you should comprehend the target of the disseminated data. Indeed, this will help you to establish the relationship between you and your data.

Many practitioners set some criteria or standards or rules before implementing data. For example, Establishing one format for data either for Address or Date. This ejaculates the inconsistencies in the process of data cleaning.

So what are these techniques, Let’s hover over the 7 + one concept of data cleaning techniques:

• Irrelevant data removing/Detaching
• Duplicate data handling
• Missing Values or NULL
• Values Standardize Capitalization
• Clear Formatting Converting Data Types
• Fixing Errors
• Language Translation

Let us roll over on them to gain more.

Data cleaning technique – Detaching or Removing Irrelevant data

In the data cleaning process, irrelevant data always creates confusion and slows down the process of cleaning. So, deciphering what is necessary and relevant, you must set goals before starting the data cleaning process. For instance, If you are figuring out the sex of your customer, then e-mail can be easily neglected.

 Let us seek out other elements that need to be withdrawn as their outcome will nothing cost your data:

• Blank Spaces between the columns or text
• Boilerplate text (Basically in Emails)
• URLs
• PII – Personal Identifiable data
• Tracking Codes
• HTML tags

Data cleaning technique – Detaching or Removing Duplicates

Whenever data is collected from numerous places either by scraping or any other medium, It always carries duplicate entries in it. These sorts of duplications could be a possibility of human error or form filling mistakes done while imputing data. Duplicates data inevitably confuse you with the results or mess up your data badly. Apart from this, it can also cause a hard time in reading data whenever you try to visualize it. The best outcome is “Remove them” without any fear.

Data cleaning technique – Handling Missing Values or NULL Values

In this criteria, while dealing with the missing value you got two options

1. Input Missing Value
2. Remove of missing value

Tackling down these two options totally depends on your analysis of primary goals. Second, what you really want to perceive from that data. Removing or barracking that data might hinder your useful insights for which you pulled that information in the first place.

Therefore, to fill that data either you do some research on your own or ask your senior what should go in that particular field. Even if you do not know what to fill in it, then in the case of numeric replace it “ZERO” and in alphabet fill it with the word that you find most common.

However, if the data got more than expected missing values then that is stated as Not Sufficient for analysis or you can remove the entire section to eliminate the potential risk.

Data cleaning technique – Standardize Capitalization

Indeed, in the data text consistency plays a vital role. For instance, your data contains a mixture or brew of capitalization, this might cause various erroneous categories creation. For Example, let us suppose a person’s name or a feature matches the same – “Bill” can be a financial asset and a name or completely else. To make it simpler just draw everything under lowercase.

Data cleaning technique – Clear Formatting

Heavy formation of information abrupt data to process under the Machine Learning Models. If you are processing your data from a wide range of sources, this will definitely acquire multiple documentation formatting. As a result, this can lead to incorrect data confusion. In this situation of dilemma, you must start from new by removing any sort of formatting in your documents. Indeed, these processes can be easily implemented in excel and google spreadsheets. For this, a standardization function will change the game for you. For Example (=STANDARDIZE(A5,$H$4,$H$5)). While in big data Z-score methodology is a common practice.

Data cleaning technique – Converting Data Types 

Stepping into the data cleaning process with respect to data types, numbers are the most primitive part where you might be converting them in the process of data cleaning. Whereas numbers are always imputed as a text but to process them they need to look like numbers.

If numerical data types emerge as a text then it will state as “Strings” and thus this eliminates the algorithms to work properly. As a result of your analysis, you will not be able to perform mathematical equations.

The same practice applies to the DATES, where the values as text. Then it is recommendable to change all the DATES to numerical values. For Instance, A entry that reads January 10th 2021, then you need to change it to that look like 10/01/2021.

Data cleaning technique – Error Fixing

The headline says it all. It goes without saying, fixing or removing errors from data. Errors might lead you to skip the key finding from the dataset. In fact, these can be mutable and easily tackled by implementing spell-check. Thus error fixing is one of the most important data cleaning techniques used to increase efficiency of any data.

Extra punctuation or spelling mistakes in data are quite common. Let’s suppose you got a mail with extra punctuation – xqr.far@gmail.com. Whereas, it could be xqr_far@gmail.com or xqr-far@gmail.com or xqrfar@gmail.com. This generates problems where it could end in people receiving wrong mail they haven’t signed up.

In addition, errors can lead to incompatibility in formatting. For instance, if you have a currency column with Indian Rupee then you to either convert all currencies into US dollars or into Rupee. The same concept hovers over in other measurements.

Data cleaning technique – Translation of Language

To get consistent data, you need to have everything in the native language. I also face the same defiance while extracting data from Twitter of a well known political personality. To convert language NLP(Natural Language Processing) models works best with it. Although they are monolingual, for it you need to translate all into one language.

Conclusion

Indeed, Data Scrubbing is a time-consuming performance. It might cost more if you forget to pull the trigger above data cleaning techniques. A data analyst faces a lot of hurdles to wrap up the snake in the basket. However, incorrect data and irrelevancies are the common assertions of any data analyst. Therefore, to nitrox, the huge data requires time and patience to clean messy or fuzzy data. To bridle the force of prediction, visual analysis, bringing significant bits of knowledge from data one should procure dominance in cleaning. In a wrap, if data is not cleaned well then it won’t yield better results.