Data is one of the most important resources when working on Data Science and Machine Learning projects. Data is useless if it’s not in a usable form, which means you need to clean it first before using it for your projects. If you’ve ever worked with data before, you know data cleaning can be a long, time-consuming, and extensive task. After all, you don’t want to mess around with some dirty data — it could lead to all kinds of unfortunate events. Fortunately, there are plenty of ways to ensure your data stays squeaky clean! Here I’ll explain why data cleaning is important as part of your data science projects. It doesn’t matter what industry or type of data a company collects; data cleansing is a must.
When working with data, you have to ensure that it is clean and correct when imported into your database. Otherwise, you’ll face many headaches, and it won’t be pretty. But why do we spend so much time cleaning data? Because it’s important! The goal is to clean data to be useful for analysis and decision-making purposes.
So, What is data cleaning?
Data cleaning (also known as data cleansing or data wrangling ) is the process of detecting and reporting errors in data. It is an important part of any “data science” project because it gives you better insights and helps avoid incorrect conclusions.
In other words, Data cleaning is an essential part of data management. It involves analyzing all data in a database to either remove or update complete, incorrect, or irrelevant information. Besides this, it helps find a way to enhance the dataset’s correctness without necessarily messing with the data provided. It is the process of identifying and correcting inaccurate data. Only a small percentage of organizations appropriately handle the quality of their data. Each step of data cleansing is covered in depth in our comprehensive data science course training.
Why is data cleansing important?
We live in a world where more and more data is created every day. Bad data can cost businesses billions when it comes to big data and business analytics. Think about it: a lot of time, money, and resources are spent getting raw data from different sources. Then, people hired for their data analysis skills transform that raw data into meaningful insights. The whole process can take some time, so businesses can’t afford to work with bad data.
A data science practitioner needs raw data to extract useful knowledge and insights from the data. But this raw data is filled with errors, garbage, and other impurities that can cause many problems. Even after analysis, dirty data will continue to produce false and meaningless results unless thoroughly cleaned up. It is impossible to make effective and accurate decisions if the data is of poor quality and dirty. If the data is inaccurate, the results and methods will always be unreliable, even if they appear to be correct. That is why data cleansing is crucial for ensuring data integrity.
Here are the key reasons why data cleansing is essential:
Data cleaning reduces unnecessary costs
Making business decisions with dirty data leads to costly errors. But bad data can incur cost problems. Regular data checks enable you to identify blips sooner. This provides an opportunity to address the issues before requiring a more time-consuming and costly fix. Clean data improves a company’s decision-making ability since management can rely on reliable reporting. If data has been corrupted with irrelevant data, then the accuracy of these reports will be degraded.
Enhance customer acquisition
Companies with well-maintained data are best placed to develop lists of clients using accurate, updated data. Hence, they increase the efficiency of their acquisition and onboarding operations.
Data cleansing is also significant since it increases data quality, which leads to higher productivity. When inaccurate data is removed or updated, the organizations are left with the highest quality information. This reduces the need for their team members to spend valuable time digging through irrelevant and incorrect data.
Boost the decision-making process
The majority of businesses are finding innovative ways to utilize data in every facet of their operations. One of the main advantages is that having access to information allows companies to make better judgments. Hence, they gain an advantage over competitors who do not use the same strategy.
Make data work across different channels.
Organizing and cleaning up consumer data (aka Data cleansing) paves the way for companies to run more effective marketing campaigns and develop better ways to connect with their target audience.
As you have seen throughout this blog, data cleaning and preparation is an important (but often neglected) step in almost every data project. Data cleaning generally involves removing irrelevant, incomplete, and erroneous data. It’s a tedious task, but if done right can have a significant impact on the overall analysis. Typically, it is performed on a dataset before applying statistical models like Linear Regression to fit the data. It’s what allows companies to extract actionable information from their data. If you are looking for more information about data cleaning or data science-related topics, check out Learnbay’s data science course in Mumbai, which offers rigorous data science training led by experienced FAANG data scientists.