What Is Data Cleaning

What Is Data Cleaning?

What Is Data Cleaning

So you have decided to board the Data Science journey. Lucrative salaries, high demand, good prospects for the future must be some of the reasons you must have considered.

Data science is a multidisciplinary field that focuses on transforming raw data into actionable insights. If you have good programming skills, know statistics, pay attention to details, and love finding trends from large data sets, then this might just be the field for you.

When you take the first step of enrolling in a Data Science Bootcamp Online, you will come across various important concepts. One of the interesting topics that you will love to explore is data cleaning.

To give you some background, every company gathers a massive amount of raw data each day from multiple resources and converts them into meaningful insights that can help in better decision making.

Data engineers, data analysts, and data scientists have to do a lot of work from the beginning to the final step of uncovering hidden trends and showing them to business leaders.

A lot of entry-level professionals who start working on a data science project often face a lot of difficulties dealing with data.

This is because, unlike during training where they are directly given cleaned data, they are exposed to messy data in a real-world project which cannot be analyzed directly. This is the time they learn about data cleaning. So, let us know what data cleaning is all about.

Data Cleaning Explained

There are disparate sources from where an organization gathers data, like social media platforms, industrial equipment, sensors, payment orders, storage records, and so on.

And do you think all that data would come in the same format and directly usable? Probably not! Moreover, there would be a lot of chances for data to be corrupted or mislabeled.

So, data cleaning is the process where incorrect, duplicate, incomplete, or incorrectly formatted data is fixed.

Also known as data cleansing, this process creates standardized and uniform data sets to enable data analytics and business intelligence tools to access them easily and identify appropriate data for each query.

Once you start collecting the data, you’ll notice some common inaccuracies like missing values, typographical errors, or misplaced entries which can affect the analysis stage and lead to false conclusions.

Ultimately, it will affect your organization’s ability to make better decisions and devise effective strategies.

Now when you are aware of the problem, you may be interested in knowing the steps followed to cleaning the data. Lately, there hasn’t been one established way of cleaning data as the type of data collected by each organization is different.

But, certain basic steps can be followed by each company to clean the data effectively. Mentioned below are some of them:

  • Getting rid of duplicate observations can be the first step in data cleaning. If there are irrelevant observations that do not fit into a specific problem, you may need to remove them.
  • After following the first step, you can recognize where most of your errors come. So, you can next keep a record of trends to identify and fix corrupt data at an earlier stage easily.
  • Take note of missing data and decide what to do with it. You can either remove it if you are sure you will not lose information or fill in the missing value based on your observations.
  • You can measure data accuracy in real-time using some advanced data quality tools. Some tools are even equipped with AI and machine learning that can further enhance your accuracy test.
  • Finally, when you have performed all these steps and cleaned the data, you can decide when to review the data cleaning process. It can be done weekly or monthly, depending on the number of issues, bugs, or glitches occurring during the process.

Benefits of Data Cleaning

A study by IBM highlighted the fact that the US incurred losses amounting to 3.1 trillion dollars due to poor data quality. The later you recognize the presence of dirty data, the more it will cost your company.

Improved quality data can result in a wide range of benefits. Organizations that take data cleaning seriously are successful in streamlining their business practices.

Instead of finding errors quite late and making changes all over again, data cleaning allows individuals to focus on key work areas and increase their productivity.

Organizations that adopt data cleaning tools can quickly offer reliable, complete insights so as to identify evolving customer needs and stay on top of emerging trends.

They can, in fact, leave their competitors behind by producing faster response rates, generating quality leads, and enhancing the overall customer experience.

Xplenty, Tableau Prep, Talend Data Quality, Oracle Enterprise Data Quality, TIBCO clarity, IBM Infosphere Quality Stage, and OpenRefine are some of the top data cleaning tools available in the market.

So, if you are interested in learning data science, get started with data cleaning. Understanding data quality and the tools required to create, manage, and transform data is a crucial step towards making effective and efficient business decisions. The time is ripe for exploring your options now!

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