Data has become an essential part of our lives. Most of the aspects of lives are intertwined and driven by data. There are times when our efforts go wasted due to the holdup caused by dirty data. This could arise when there is a mistake in spelling, arrangement, formatting, or construction which has contributed to making the data unclear. This is why we are in need of data cleaning.
Data cleaning might appear to be an out-of-the-box idea to a few. However, it’s a fundamental piece of data science. Utilizing various strategies to clean data will help with the data analysis process. It additionally further improve communication with your teams and with end-users, as well as preventing any further IT issues along the line.
Unfortunately, data cleaning can take up a tremendous chunk of time for data stewards. However, as having poor or wrong data can be unfavorable to a task, it’s something imperative to do. It’s not all terrible. Top-notch data that has been cleaned can make your work simpler.
This is why data professionals must know the most used techniques to properly clean and create a data deposit that is efficient. Different types of data require different types of cleaning. However, there are some general approaches that can be considered as a starting point. This article speaks of these essential techniques.
What is Data Cleansing?
Before we go further into the techniques it is important to understand what data cleaning actually is. It is a process of classifying and removing or fixing “dirty/bad” data. The data is usually an unreliable, inaccurate, or unfinished form of data for databases or tables.
The data then would require removing, restoring, or remodeling. There are times when data cleaning is crude and would need to be removed completely.
Let us take an example, say you are the one who handles the data of an eCommerce website. If you put out data that is incorrect, it can lead to problems and the site can incur losses monetary and reputation. Like an item was advertised next to the unmatching description.
What are the Benefits of Data Cleaning?
It doesn’t matter if you working on developing a site or deep learning, the following ways are in which data cleaning can help you.
Efficiency – Clean data can facilitate faster analytics. Clean data mean there are no multiple errors and ensures accurate results. Therefore you don’t have to rework the whole task due to false results.
Error Margin – It doesn’t matter that how eager you are to get the results, if the data is not clean, the results won’t be accurate. This means that when your work is evaluated it may not be completely accurate. Therefore getting used to clean data means that you will have to adopt the practice of slowing down and fixing data before your present it to anyone. Leaving less room for errors.
Accuracy – Since data cleaning is a time-consuming process, you will soon learn to be most accurate with the entry of data in the first place. Data cleaning would be needed for other reasons as well but doing it would eventually make you better at data in the first place.
Data Cleaning Techniques:
Remove Unwanted Observations
Removal of unwanted observation is the first thing you need to do while setting up data cleaning. This process includes removing irrelevant or duplicate observations.
Duplicate observations usually arise during the process of data collection. This usually occurs when data from multiple places are combined or scraped. It can also occur when data is received from different departments or clients. For example, a client might have entered their data twice accidentally. Duplicate usually increase the amount of data accumulated and ends up wasting your time.
Irrelevant observations are those issues that don’t fit with other issues that you are trying to solve. For example, you are building a virtual office phone service. You will collect data relating to phone numbers here. But you don’t want any information relating to social media. Focusing on cleaning this type of observation would prevent any problems that may arise down the line.
One has to make sure the data is irrelevant and that you won’t need it further down the line, say for something like correlated values. Once you are sure of that, get rid of it!
Filter Unwanted Outliers
It is vital that you remove the unwanted outliers as they can cause a lot of problems with certain models. For example, linear regression models are less robust to outliers than any decision tree models. Removing outliers can help you with enhancing the model’s performance, however, has to be a valid reason to remove them.
Let’s say that you are creating a database connected to a digital handbook maker and there are multiple figures and facts related to them. Just because there is a big number to input, it doesn’t make it an outlier. Large numbers tend to become informative to your model at some point in the process.
If there is a valid reason that says that an outlier should be removed then it is vital that you do so. This could be a false measurement. Like if you entered a phone number as 012873839283228343273, you know it’s not the true value and is an outlier that you can remove.
Also Read: 8 Best Practices for Data Cleaning
Typos are the most commonly occurring issues and it is quite easy to make to. With having a tool to spellcheck, they can easily go unnoticed. It is important to fix these as different models treat different values differently. For example, “Strings” rely a lot on spelling and letter cases.
Data stewards have to be extremely careful to fix these typos. Errors can be mapped and converted into correct spellings.
However, when a computer is involved it doesn’t think like a human being. For example, there is a difference between Robert and Robert. The capital can have a significant impact on the data.
Another example could be, the spelling of “optimize” and “optimize. They are the same word but spelled differently.
Likewise, “Mice” and “Mike”. They have the same number of letters but are spelled differently.
One more thing to consider is the string size. You might have to change them to be sure that you have kept them in the same format.
It could be that the dataset has only 5 digits only. So if you have 3332, then you will have to put a zero in the front of the number. This will keep your data uniform. You would also have to remove the whitespace for the same reason.
Missing values shouldn’t be ignored. Knowing how to handle them will clean your data. You could be facing problems with missing values in a column. This happens when there is not much data to work with, so it could be easier to delete the column.
There are ways to put in the missing data value. This is done by guessing what the missing data might be. Linear regressing or median could help you in calculating this. But the catch is that it won’t be the real value so it won’t be accurate.
Another method is to copy data from a similar dataset, but this might also record inaccurate results. So, you can always inform the algorithm that the data is unavailable or ‘missing’. You may have to select ‘0’ in some cases.
Otherwise, you can copy the data from a similar dataset, however, it could also record inaccurate results.
Convert Data Types
All data types need to be consistent across the board. A numeric can’t be a Boolean and a string can’t be numeric.
When converting data, numeric values need to e kept as numeric values. Numerics shouldn’t be entered as strings and data that can’t be converted should be entered as N/A.
Make sure that all data converted helps anyone in the company who has to deal with data. It helps experts in cybersecurity to encrypt and protect data properly if there aren’t errors that can be exploited by hackers.
Knowing how to do data cleaning properly is all part of being a great data steward. Getting data cleaning correct prevents any issues from occurring in the future. Data cleaning helps you to do your job properly and, in turn, allows you to do the best job you can to help companies move forward with their goals. If you are interested in data cleaning services then be sure to book a free, no-obligation consultation with us.
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