Data Wrangling

On average, roughly 328.77 million terabytes of data are created each day. In 2020, the total amount of data on the Internet alone reached 64 zetabytes. As massive as those numbers are, the most overwhelming thing about them is that in a few years, they’ll probably seem comparatively small.

In today’s digital economy, nearly everything we do creates some form of data, from our interactions on social media to our office workflows. Although much of that data is little more than noise, there’s a not-inconsiderable volume of it that contains incredibly valuable insights. Provided, of course, that said data can actually be processed.

Therein lies the problem. The majority of modern data sources output raw, unstructured, and unfiltered data. In order to leverage that data for anything tangible, you first need to transform it into a more usable format – part of that process involves something known as data wrangling.

What is Data Wrangling?

Usually taking place immediately after preprocessing, data wrangling restructures, cleans, enriches, and organizes raw data to make it more valuable and digestible for analysis and visualization. It’s typically a manual process, requiring data scientists to manually convert and map raw data into a more business-friendly and usable format. Unsurprisingly, this is incredibly time-consuming – data wrangling may take up as much as 80% of a data professional’s time.

But what exactly do they accomplish with all that effort? Is data wrangling actually worth the significant time commitment it usually entails? The short answer is yes.

Benefits of Data Wrangling

The overarching goal of data wrangling is to make it easier to interpret and analyze information. As you might expect, this comes hand-in-hand with some considerable benefits:

  • When properly wrangled, even the most complex data sets can be easily interpreted, analyzed, and fed into visualization tools.
  • As the name implies, unstructured data tends to be messy and disorganized. Data wrangling eliminates that disorganization, replacing it with an enriched and organized dataset that’s both easier to work with and also provides deeper insights. This also tends to improve the accuracy and precision of data that are being used in machine learning.
  • Consolidating and wrangling data from multiple sources provides an organization with a more complete picture, allowing it to make more informed, strategic decisions as a result.
  • In some cases, data wrangling can actually save time in the long run, as analysts and other personnel won’t have to struggle with poorly organized data sets.
  • Data wrangling greatly enhances usability, transforming data that might otherwise be incompatible with a system.
  • Data consolidated from multiple sources often contains errors that can impede analysis or lead to inaccurate predictions. Data wrangling helps eliminate these errors by ensuring consistency and uniformity between data sets.

Data Wrangling vs. Data Cleaning

Data cleaning represents one of the core data-wrangling techniques, which we’ll discuss in more detail momentarily. Also known as data cleansing, it focuses on identifying and eliminating inconsistencies in a data source or data set. Cleansing can only be performed once data has been properly reviewed and characterized; both serve to make a data set more usable for business purposes. The two processes also occur in close proximity to one another, with data wrangling typically taking place shortly or immediately after data cleaning. With that in mind, it’s important to understand how each works.

Data cleaning – also known as data cleansing – focuses on identifying and eliminating inconsistencies in a data source or data set. In addition to flagging errors, the data cleansing process may also include the removal of missing data, duplicate or redundant variables, and outliers. It can only be performed once data has been properly reviewed and characterized.

The Six Steps of Data Wrangling

Data wrangling typically follows a six-step process, moving from initial discovery through to validation and publication.


Also known as Discovery, data exploration is basically an umbrella term for everything a team does to familiarize themselves with a data set. The main goal at this stage is to identify patterns and trends in the data. From there, you can determine how best to organize, consume, and analyze it.


Raw data lacks anything resembling a uniform model or structure. Particularly if your data set originates from multiple sources, it likely contains any number of different sizes and formats. For that data to be made usable – and for the data wrangling process to progress – it must be restructured to fit an analytical model.

As part of that process, you’ll need to parse the data set, pulling out relevant information and discarding the rest.


With your data set properly parsed and structured, you can now examine it for errors, null values, missing values, and redundancies. Although this sanitization can be done manually, it’s recommended that you find a way to automate it. Most data-wrangling tools should be able to do so.


Before moving to the final stages of the data-wrangling process, you need to decide whether or not to enrich, embellish, or augment the data. This may be necessary if you’re working with an imbalanced categorical data set. Enrichment may also be required if your data set is especially sparse or contains a large number of missing values.

With all that said, if the data set meets the requirements of your use case, you can skip this step.


Occurring immediately before publication, validation uses a series of predefined scripts or algorithms to check a data set for:

  • Accuracy
  • Quality
  • Consistency
  • Security
  • Authenticity

It’s essentially a check to see if you caught all the issues contained within the data set in the previous steps. You may need to repeat the validation phase several times.


At this stage, your data set is ready to be published and leveraged by the rest of the organization. You may need to make a few decisions regarding where and how to store the data, but for the most part, this stage is incredibly straightforward.

Best Practices for Data Wrangling

  • Understand who needs to access and leverage your data and why they need to do so.
  • Make sure you choose data that fits your use case.
  • Know your data, including its database, file format, and key metrics such as data quality.
  • Embrace continuous improvement.
  • Choose the right tools – solutions exist for each stage of the data wrangling process, from processing and organization to the extraction of insights.