What is Data Wrangling? 6 Key Steps

Introduction to Data Wrangling

Organizations are churning out raw data each day with their multiple business systems. Decision-makers and company leaders rely on these metrics to steer their operations and enhance their growth. But before extracted data can be put into use, it must be accurate.

Otherwise, the business may miss opportunities, take unnecessary risks, or delay operations. Therefore, organizations have been implementing data wrangling tools to ensure their information is dependable and ready to use.

What Is Data Wrangling?

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Data wrangling is the practice of cleansing, restructuring, and enriching raw data. Raw data is complex because it has not been processed or integrated into a system. With data wrangling, these records are transformed into a standard format that helps highlight valuable insights. The process entails consolidating data into one location and rectifying any missing information or errors.

Also known as data munging, data wrangling is considered to be the most time and labor-intensive part of data processing. Data scientists have said that data wrangling takes up approximately 75% of their time to complete. By investing time into the process, teams can focus on ensuring data accuracy. This process is very critical for businesses to perform because it is the only method that makes raw data usable.

Effective data wrangling will help organization leaders foster a bird's eye view of their entire business. It also eliminates instances of data duplication, which would have impacted decision-making. Additionally, since it centralizes disparate data sources, different departments in a business can collaborate and cultivate innovative strategies.

6 Steps for Data Wrangling

Organizations can approach data wrangling in many different ways, from using automation or software to manually entering information into a database. Despite these diverse techniques, the process of data wrangling involves 6 core steps.

1. Discovery

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Before performing data wrangling, business teams need to fully understand what their data is about. It is also important to know why the business needs the data set and how it will utilize it. This knowledge will help give the team clearer direction on how to organize and analyze the information.

2. Re-Structuring

Incoming datasets from multiple sources and systems will arrive in different formats, sizes, and types. Therefore, at this stage, the data must be restructured to a standard format. The data should also be categorized and separated based on its purpose.

3. Data Cleansing

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The data cleaning step entails removing duplicates, null entries, and errors in the database. Teams can also further standardize the information. For example, a rule can be implemented to ensure all dates are written in the MM/DD/YYYY format and that all states are abbreviated with 2 letters.

4. Data Enrichment

Enriching data refers to taking existing data and adding supplementary information to derive more insights. For instance, if a retailer wants to learn more about how the effectiveness of their social media ads, they can enrich their sales data with marketing data.

5. Validation

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The validation process involves authenticating the reliability, credibility, and quality of the cleaned data. Teams need to make sure that all their data is accurate and that the information was not altered during the data wrangling process.

6. Data Sharing

Once the data is validated, the business team can publish and share their reports. Most commonly, the organization will have a shared file where authorized users can upload data analytics. Depending on the circumstance, a meeting will be held with other departments, employees, and stakeholders to share the new insights.

Common Uses for Data Wrangling

Organizations can use data wrangling for many business functions. The following are the two most common uses.

Fraud Detection

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Data wrangling can help businesses detect and prevent fraud and security risks. It does so by completing the following tasks.

  • Assess information from multi-party emails and online chats to detect unusual behavior and corporate fraud.
  • Standardize data structures and quantify datasets to ensure precision and reproducible models.
  • Enact security protocols during data integration to make sure the business is complying with regulations set by the government and industry leaders.
  • Examine and wrangle data daily to support data security.

Customer Buying Behavior Analysis

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Retailers can use data wrangling to gain insight into their consumer base's buying habits. This can help the marketing and sales team understand their shoppers and effectively target their needs. The following elements are how data wrangling can enhance customer behavior analysis.

  • Reduce the time necessary for data preparation.
  • Quickly access meaningful and critical insights about a business's value.
  • Equip users with informative data trends through data discovery and visual profiling.

Key Takeaways - Data Wrangling

  • Data wrangling is the practice of cleansing, restructuring, and enriching raw, complex data into a digestible format.
  • Also known as data munging, data wrangling is the most time-intensive part of data processing because it requires teams to diligently analyze data for accuracy.
  • Effective data wrangling allows businesses to access accurate data, to which they can understand their overall operations and make informed strategies.
  • The 6 basic steps to data wrangling entail discovering, structuring, cleaning, enriching, validating, and sharing.
  • Data wrangling can streamline many business functions, such as fraud detection and customer behavior analysis.