Things You Should Know About Data Cleaning and How to Do It

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Data is a powerful tool that can be used to make important decisions. It also needs to be organized and clean for it to have the most relevance. Data cleaning, also known as data scrubbing, is the process of removing errors, duplications, and inconsistencies from data sets. Along with protecting servers that hold your data, data cleaning is an essential component for any data-driven company today.

In this article, we will talk about what you should know about data cleaning, as well as how to do it.

Why is Data Cleaning Necessary?

Many companies today rely on models or algorithms to drive important decisions. The data they use to build these models must be accurate, relevant, and up-to-date.

Data cleaning not only refers to excising irrelevant, erroneous, or incorrect data but also includes repairing incorrect information within the dataset. This is a key step before any form of analysis can be made on it and should not be skipped.


Data cleaning is an essential step before any form of analysis can be made; without properly cleaned and organized data you won’t get accurate insights or predictions to help drive your business forward.


The dangers of allowing erroneous datasets to pass through your pipeline are high. Not only do you risk developing inaccurate models, but it will also make decisions based on this information harder to trust and rely upon.


While a performance monitoring tool can be useful to check whether your apps are functioning smoothly, it won’t help if you train your models on raw datasets. When the noise is consistent throughout the training and testing sets, this can lead to accurate predictions during that time—but fail when new, cleaner data arrives later on.


How Do I Clean My Data?

Make no mistake about it: data cleaning is a tedious and time-consuming process. However, it’s a necessary step to ensure the accuracy and usefulness of your data. Data cleaning means manually poring over large data sets to eliminate irrelevant information and to analyze columns and rows for inconsistencies.

Before getting into the nitty-gritty, these are the five things you want in your data:

  • Accuracy
  • Consistency
  • Validity
  • Uniformity
  • Completeness

In effect, you are cleaning any data that doesn’t meet these requirements.

We go over the five key steps you must take to clean up your data. Using these tips will help you better understand the quality of your data before performing any analysis on it. They are all essential steps to take when preparing for a machine learning project, so don’t skip them!


Identify and excise irrelevant data

Irrelevant or duplicate data must be identified and removed before any analysis can be done. Duplicates can appear in your dataset for several reasons. They may be the result of someone participating in a survey more than once or because the questionnaire includes several questions on a similar topic, resulting in many people giving comparable responses.

Standardize the syntax

Grammatical and syntactic issues often crop up when working with natural language processing. For example, you may find someone who wrote their age as 12 years old or a date that includes text (“July 22nd”). There can also be issues related to spelling and word choice (e.g., “analytics” vs. “analitics”)

There are some tools out there that help identify and address these issues automatically, but it’s best to run them through multiple tools before settling on one. Automated syntax cleaning is an important step in the data cleaning process because most algorithms are not programmed to understand natural language or context.

Eliminate outliers

Outliers need to be removed from the dataset before you can analyze it. Outliers in a given column can skew results or throw off predictions made by algorithms using that column as input. It can be hard to spot outliers because they don’t always look the same.

To determine whether or not a data point is an outlier, you’ll need to know what the distribution of data in that column should look like. A good way to figure this out is by looking at several examples and finding a pattern or commonality between them all.

Decide how to approach missing data

Missing data is inevitable for badly designed or randomly sampled datasets. How you deal with missing data is critical to the success of your analysis.

It’s easy enough to identify missing data but it can be a pain knowing how to fill in the blanks. A common way to fill in missing data is by using a mean or median value of the column, but this method can prove inaccurate when predicting behavior with machine learning algorithms.

You must determine how your dataset was collected and what kind of information it contains before making any decisions about dealing with missing values. If multiple data points have missing data for identical attributes, you may just have to drop the entire column.

Validate your data’s accuracy

Cross-checks within data frame columns can ensure that the data being processed is as accurate as possible. However, ensuring the accuracy of data is difficult to assess and may be achieved in only a few places where a specific notion of the data is defined.

Restrictions on what data can be entered and validated are fairly minor. For example, you might think of countries, continents, and addresses as being restricted to a small number of predefined options that can readily be verified. Cross-checks across sources may serve as an additional method to verify the accuracy of your data when it’s created from multiple sources


After completing these five steps, you’ll have a much better understanding of the quality of your data and be in a much better position to start your analysis. Remember, data cleaning is not a one-time activity, and you’ll need to revisit your data on an ongoing basis. If new information becomes available or the rules governing what can be entered change at any point in time, you may have to go back and reexamine your dataset for accuracy again.


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