What is Data Wrangling?
It’s something you’ll eventually do as a data scientist, but what exactly is data wrangling? Learn the answer to this question, and find out how it works below. Also, discover what skills you need to kick-start your career in data science.
Data Wrangling Meaning
Data Wrangling is known as “data cleaning” or “data remediation,” but these all mean the same thing. In the simplest of terms, it is the process of transforming raw data into a more appropriate format for data analysis.
For instance, if you want to analyze a raw data set that includes missing values or outdated information. Analyzing this incomplete and irrelevant data would be a waste of time, so data wrangling will be beneficial. It lets you structure the data and transform it into a more useful format for your analysis.
You can perform data wrangling manually, but this takes a lot of resources. So, like many data scientists, you might use a data wrangling tool to do part of the heavy lifting. However, you do need to learn how to use these tools and understand their capabilities and limitations.
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How Does Data Wrangling Work?
There are six steps involved in the data-wrangling process:
- Discovering data
- Structuring data
- Cleaning data
- Enriching data
- Validating data
- Publishing data
Learn more about each step:
Discovering data involves understanding all the data you will “wrangle.” Take a closer look at the data set you want to analyze and identify any issues that will impact your analysis, such as data outliers and empty cells in spreadsheets.
Structuring data is a crucial step in the data-wrangling process. It entails transforming raw data into the correct format for analysis by cleaning, enriching and/or validating it.
Cleaning data involves taking actions such as standardizing data input variations and removing outliers to rid data sets of errors. This part of the process can be time-consuming, but it’s essential for ensuring your data is in the best possible condition for analysis.
Enriching (or augmenting) data means determining whether a data set is ready for analysis or needs to be incorporated with other data sets for more successful outcomes.
Validating data confirms your data is consistent and of the highest possible quality before you analyze it.
The final step of data wrangling is publishing, where you share your data with other people via a report, dashboard or another method.
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Benefits of Data Wrangling
Here are some advantages of data wrangling:
Improves Data Usability
Data wrangling makes your data more usable. It’s as simple as that. You can remove outliers, inconsistencies, errors, outdated information, duplicated data sets and other factors that impact analysis, so your data projects are more successful.
Errors, inconsistencies and outdated data can all skew analysis, making it difficult for you, as a data scientist, to identify trends and patterns and generate valuable insights. With clean, consistent and compatible data, you’ll get more value from raw data and improve business intelligence.
Improves Data Governance
By wrangling raw data, you can ensure it complies with data governance guidelines like GDPR and CCPA before moving it to another system for analysis. This can prevent expensive government fines for not complying with regulations.
Drawbacks of Data Wrangling
Here are some drawbacks of data wrangling:
- Manual data wrangling is time-consuming and involves a great deal of coding.
- Data wrangling tools can be expensive.
- The wrangling of data requires extensive preparation (see discovery stage in the benefits section).
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We’re always here to guide you through your journey in data science. If you have any questions about the application process, consider contacting our admissions team.