Data Wrangling: Meaning, Steps, and Its Relevancy

Taking the best out of data is the core essence of data wrangling. It's more than just data cleansing.

Only with accurate data, in the digital space, innovations happen. Professionals who analyze data always demand fresh data, which is absolutely free from errors. Data cleansing helps up to a certain level. After that, data wrangling is the path to make datasets rational.

Basically, wrangling is the most crucial stage, and it is placed exactly next to the data cleansing process. Let this blog help you understand the core concept behind all these processes in a crisp format. Moreover, if you're looking to know how wrangling works and how you can do that, this is your page.

Welcome you all

Turning raw data into a more readily used format is the main task of data wrangling. It mainly includes three stages – Data Cleansing, Data Remediation, and Data Munging. Please note that there's no perfect example of a data wrangle exists, it actually varies on project to project basis.

Here are some examples of data wrangling;

In big organizations, automated data wrangling prevails. But, automated processes have their own issues. When it comes to ensuring high-quality remarks on data then there's nothing better than manual wrangling. Big organizations as well as small organizations depend on high-quality data for running their projects. Hence, data wrangling has its own need in every organization, to be precise.

It's 2025 where business decisions are taken only based on the data reports. Businesses invest a lot of resources in data analytics to get accurate data insights. Up to the year 2030, the data analytics market will grow at the rate of 27.3 percent per year (Fortune Report). Every sphere of business is switching to data-driven insights ditching their old mechanism.

So, data wrangling exclusively helps businesses get their data in the correct order. It eases data analytics and provides a lot of benefits to businesses. For example, wrangled data helps;

Success of a data project always depends on its underlying strategies. This simply means one successful formula may not ensure success in all data projects. So, you need to frame strategies on specific data projects, without any bias. But nothing to worry about!

Here's the 6-step approach that is common and you must follow it when you wrangle the data for better outcomes.

(conceptualizing how to approach the data)

The first step when analysis starts is data discovery. Here you get familiar with the data types that you are supposed you use in the next step. Like you check your refrigerator every time before cooking your meal. In a similar way, you have to check for your data before doing something out of it.

Refer to this stage as an observing stage where you study the positives and the negatives of your data points. Thereafter, you can prepare a plan for executing your data points better.

(arranging raw data into used format)

After you study the data, now comes the arrangement parts. As we all know, raw data cannot be used for data analysis; obviously, because of its raw state. So, structuring comes into play now when you arrange data in a usable format. The format would depend on your project needs.

(removing errors that distort the analysis)

Removal of errors from the datasets is extremely crucial as it is a direct threat to your analysis. Data cleansing is a comprehensive process that ensures there is no error in your database, which can influence your decision at all. Identifying and removing bad data is an integral part of the data-wrangling process.

For your reference, the terms wrangling and cleansing most of the time are being used interchangeably. But conceptually, both terms have different meanings. You can say data cleansing is a sub-part of data wrangling. In general, the wrangling of data is a broad concept.

(improving data quality by adding new things)

Once you are done with the cleansing of your data, now comes the enrichment. Here you add additional information to your existing data or update it. Data cleansing leaves the dataset blank if it causes an error but enrichment fills the blank space with updated information. It makes your data more valuable for analysis.

(conforming data quality and consistency)

Verification of data ensures accuracy and quality intactness. Through verification, you need to check the consistency of your data and the accuracy of its own. Once you validate the data, it becomes ready for the assessment. Many organizations are now validating their data using automated systems. But a humanized system works better here.

(Sharing data for analysis)

Share your data with your analytics team once you confirm the validation. Having a secure FTP (File Transfer Protocol) suits the best to publish the data. You can share the data in whatever format you prefer but according to your organization's goals.

Have you realized how many ways data wrangling helps your business? Well, first of all, it helps in data analytics; next, it helps make decisions faster. But the most important thing is it reduces data errors or costly data mistakes. Indirectly, it saves your company's costs by cutting off heavy data mistakes. Now to apply data wrangling steps, you need solid support from a data expert.

AskDataEntry is the name that you need here. It's a professional data entry company, working for the last 10 years. The company covers all data-wrangling steps and provides all-around support. If you need a quick consultation, you can contact us here.