What Is Data Wrangling?
What is data wrangling? Also known as data cleaning, data remediation, and data munging, data wrangling is the digital art of molding and classifying raw information objects into usable formats. Practitioners use various tools and methods — both manual and automated — but approaches vary from project to project depending on the setup, goal, and parameters.
Why Is Data Wrangling Important?
It may sound cliche, but it’s true: data is the gold of today’s digital economy. The more demographic information a company can compile about extant customers and potential buyers, the better it can craft its marketing campaigns and product offerings. In the end, quality data will boost the company’s bottom line.
However, not all data is created equal. Moreover, by definition, informational products can only be as good as the data upon which they were built. In other words, if bad data goes in, then bad data comes out.
What Are the Goals of Data Wrangling?
Data wrangling done right produces timely, detailed information wrapped in an accessible format. Typically, businesses and organizations use wrangled data to glean invaluable insights and craft decision frameworks.
What Are the Six Core Steps of Data Wrangling?
The data remediation scaffolding consists of six pillars: discovery, structuring, cleaning, enriching, validating, and publishing.
Before implementing improvements, the current system must be dissected and studied. This stage is called the discovery period, and it can take anywhere from a few days to a few months. During the discovery phase, engineers unearth patterns and wrap their heads around the best way to set up the system.
After you know what you are working with, the structuring phase begins. During this time, data specialists create systems and protocols to mold the raw data into usable formats. They also code paths to distribute the information uniformly.
Analyzing incomplete and inaccurate data can do more harm than good. So next up is cleaning. This step mainly involves scrubbing incoming information of null values and extinguishing redundancies.
Companies may use the same data, but what they do with it differs significantly. During the enriching step of a data wrangling process, proprietary information is added to objects, making them more useful. For example, department codes and meta information informed by market research initiatives may be amended to each object.
Testing — or validating — is the backbone of all well-executed data systems. During this phase, engineers double-check to ensure the structuring, cleaning, and enriching stages were processed as expected. Security issues are also addressed during validation.
The end product of data wrangling is publication. If the information is headed to internal departments or data clients, it’s typically deployed through databases and reporting mechanisms. If the data is meant for promotional materials, then copywriting, marketing, and public relations professionals will likely massage the information into relatable content that tells a compelling story.
Data Wrangling Examples
We’ve discussed the ins and outs of data wrangling procedures; now, let’s review common examples. Data wranglers typically spend their days:
- Finding data gaps and deciding how to handle them
- Analyzing notable outliers in the data and deciding what to do about them
- Merging raw data into a single database or data warehouse
- Scrubbing irrelevant and unnecessary information from raw data
Are you in need of a skilled data wrangler? The development of AI-powered platforms, such as Inzata Analytics, has rapidly expedited the process of cleaning and wrangling data. As a result, professionals save hours on necessary tasks that can transform your data landscape and jump-start profits.