• Data cleaning improves the overall quality by removing disruptions that could lead to a flawed analysis.
  • A well-organized and updated data  improves efficiency and reduces expenditure as well. Cleaning data requires multiple tasks like fixing structural and duplication errors, maintaining data consistency, and matching predefined validation rules. 

A relevant first step in data analytics is data cleaning, also known as data cleansing or data wrangling. Preparing and verifying data is a vital step that should be taken before diving into the meat of your analysis. 

Although removing inaccurate information is an integral part of data cleaning, that is not its only purpose. The bulk of the effort is spent looking for inconsistencies in it and fixing them if at all possible. You learn the tactics of data cleaning by pursuing the data science course in Pune.

The term “rogue data” is used to describe information that is in some way flawed. ‘Deduping’, or the process of removing duplicates, is also performed. The equivalent action in this process would be to combine or eliminate duplicates.

The Demand for Data Analysis has Increased Over the Years.

Why is Cleaning Data Important?

When working with data, the phrase “garbage in, garbage out” is often repeated. Data analysts use this maxim GIGO. Then what? GIGO means that its poor quality will lead to flawed analysis. If your data is messy, following every other step of the data analytics process won’t help.

Thus, data cleaning is necessary. It’s like building a strong foundation. Incorrect architecture will collapse your building. This is why good data analysts spend 60–80% of their time cleaning data. And, in this regard, a good Data Science Course embellishes you with certain strategies and hacks that come in handy while scouring your data.

The Main Advantages of Cleaning Data 

We’ve established that clean data is necessary for reliable insights when conducting data analysis. Having clean data also has many other advantages:

  • Keeping Things Organized: These days, businesses amass vast troves of data from their clients, customers, product users, and others. Maintaining order in this data requires regular cleaning. Then, it can be safely and efficiently kept away.
  • Skip Mistakes: It’s not just data analytics that suffers when data is dirty. Everyday activities are also impacted. As an example, many marketing groups maintain databases of past and present customers. If it is well-maintained, they will be able to access useful, up-to-date data. The wrong name might be used in mass mailings if things are disorganized.
  • Increasing Efficiency: By regularly updating and cleaning data, inaccurate or outdated records can be removed promptly. This eliminates the need for groups to sift through obsolete files and databases to find the information they need.
  • Cost Reduction on Unnecessary Expenditure: Bad data can lead to costly mistakes in business decisions. However, there are other costs associated with it. Processing errors and other seemingly minor issues can quickly escalate into major ones. If you keep a check on a regular basis, you can spot fluctuations earlier. This will allow you to fix them before they become more complicated and expensive.
  • Better Mapping: There has been a recent uptick in efforts to upgrade existing data systems within companies. This is why many businesses today employ data analysts to handle data modeling and application development. A good data hygiene plan is a practical solution because clean data makes it much simpler to combine and map.

How Can I Clean My Data?

5 Steps in Data Cleaning

There are many data-cleaning practicalities to discuss now. We’ll focus on high-level activities since there are multiple ways to complete each task.

Remove Unwanted Observations

Removing unwanted observations (or data points) is the first step in any data-cleaning process. This includes irrelevant observations. Combining datasets, scraping data online, or receiving it from third parties often results in duplicate data.

Also Read: What is Artificial Intelligence with Examples

Fix Structural Errors

Poor data housekeeping is a common cause of structural errors. Common examples of such errors occur during manual data entry and include typos and inconsistent capitalization. 

Maintaining uniform capitalization throughout the data improves readability and usability. Mislabeled sections should also be double-checked. 

Keep an eye out for stray punctuation as well, such as underscores and dashes.

Make Your Data Consistent

Fixing structural errors are related to, but not the same as, standardizing your data. While fixing typos is relevant, so is making sure all cell formats are consistent. 

For instance, when you choose lowercase or uppercase values,  stick to that case throughout your dataset. 

When we talk about standardizing, we also mean making sure that all numerical data are expressed in the same way. 

Putting together a dataset that includes both miles and kilometers is an example of a bad idea. In the United States, the month comes before the day, while in Europe it comes before the day.


Data cleaning has become relevant in all sectors of society including public health. Incorrect medical records can disrupt the treatment process which can impact people’s health even causing deaths.

Eliminate Errors in the Data

Another common issue to watch out for is contradictory (or cross-set) data errors. To have a record full of data that is incompatible with itself is an example of a contradictory error. 

A record of race times is one such example. A cross-set error exists if the sum of the times in each race does not equal the total time in the column showing the total time spent running. 

Another instance would be a student’s grade being linked to a field with only the two choices of “pass” and “fail,” or an employee’s tax liability exceeding their gross pay.

Validation of Your Data Set

Validation follows dataset cleaning. Validating data means verifying that corrections, deduplication, standardization, etc. are complete. 

This often involves scripts that check if the dataset matches predefined validation rules (or “check routines”). Validation means checking data readiness for analysis. 

You’ll need to fix any remaining errors…Data analysts spend so much time cleaning data for a reason!

In Conclusion

In data analytics, cleaning the data is a relevant step. However, maintaining and regularly updating your data is good practice regardless of your use of data analytics. The importance of having error-free information cannot be overstated in data analytics or data science. So what are you waiting for? Join data science course today and master the skills of data cleaning.