Data wrangling: Meaning, process and techniques

No matter where raw data comes from — SaaS applications, databases, or CSV exports — it often arrives with missing values, inconsistent formatting, and duplicate entries. Using this data without cleaning it results in inaccurate reports and flawed models, which then lead to bad decisions. And this isn’t an isolated issue: 82% of organizations cite data quality issues as their biggest barrier to AI success.

Data wrangling addresses this problem by cleaning and preparing data for analysis. Learn what data wrangling is, how it works, and why it’s essential for your organization’s data quality and data governance.

What is data wrangling, and why is it important?

Data wrangling is the process of cleaning, structuring, and enriching raw data for analysis, reporting, or machine learning (ML). Some people call it data munging or data preparation, which are interchangeable terms. 

All raw data must go through data wrangling before it’s used in any downstream workflow. You can’t get clean outputs without first cleaning the input. For example, a dashboard built on messy data will produce misleading numbers. 

For businesses, data wrangling means:

• Improved data quality and reliability: Cleaning and validating data before it reaches reports or models means fewer errors in the outputs that drive decisions.
• Faster analysis and reduced manual cleanup: When data arrives in a consistent format, analysts spend less time fixing it and more time working with it.
• Consistency across datasets and reports: Standardizing data formats, units, and naming conventions across sources eliminates the discrepancies that make cross-team reporting unreliable.
• Better support for advanced analytics and ML: ML models are sensitive to data quality. Poor data quality increases the chances of hallucination. 
• Reduced risk of errors in business decisions: Bad data leads to bad decisions. A clean dataset gives decision-makers a more trustworthy foundation.

Data wrangling process: How it works

In data wrangling, each step builds on the previous one. Here’s how it works:

1. Discovery

Before transforming data, it’s important to understand what you’re working with. Discovery involves profiling the dataset to assess its structure and quality. For instance, determining the number of rows and columns, identifying the data types, and locating gaps would be the first step. 

Running summary statistics and scanning for patterns gives you a snapshot of what needs fixing, laying the foundation for the subsequent steps.

2. Structuring

Raw data often arrives in formats incompatible with data analysis tools. That could mean nested JSON from an API, semi-structured log files, or spreadsheets with inconsistent column layouts. 

Structuring reshapes the data into a consistent tabular format with defined rows and columns. It involves flattening nested objects, pivoting tables, and aligning multiple data sources to a single schema.

3. Cleaning

Data cleaning corrects errors, removes duplicates, and handles missing values. For example, a financial dataset might have duplicate transactions because of different data streams. Cleaning resolves those issues to make the data reliable. 

4. Enriching

Sometimes the dataset isn’t enough on its own. Data enrichment adds context by joining the primary data with information from other data sources. For example, a retail company might enrich transaction data with details from an internal catalog or append demographic data based on the store location. The goal is to add columns that make the analysis more useful. 

5. Validating

After the data transformations (structuring, cleaning, and enriching) are applied, validation checks confirm whether the data meets quality standards. These checks include automated scripts to verify that business rules still hold, totals reconcile with source systems, and no records were lost or corrupted during earlier steps. 

6. Publishing

The final step is making the wrangled data available for use by loading it into a data warehouse, pushing it to a BI tool, or exporting it in a format that feeds an ML pipeline. Publishing is where data wrangling pays off: Because the data is now clean and structured, it’s ready for analysis or machine learning workflows.

6 data wrangling techniques

Here are the most common data wrangling techniques analysts and engineers use for cleaning and structuring data.

1. Handling missing values

Nearly every dataset has gaps. The question is whether to drop the incomplete rows, fill them with a reasonable default, or flag them for manual review. That decision matters more than it seems: Dropping too aggressively means you lose data that could’ve been useful, but manual review is time-consuming. The right call depends on how many records are affected and how important the field is to your use case. 

2. Identifying and treating outliers 

A single extreme value in a revenue column can shift the average enough to change a forecast. Outlier detection starts with statistical methods that measure how far a value sits from the rest of the distribution.

Removing outliers is the simplest option. But if the outlier represents a real event, like a large enterprise deal, removing it will cause your model to ignore a valuable pattern.

3. Standardizing and normalizing

When one column ranges from 0 to 1 and another from 0 to 100,000, any model that treats both equally will overweight the larger numbers. Standardization and normalization fix that by rescaling values to a common range. 

4. String parsing and data manipulation

Text fields are where most of the tedious wrangling happens. For example, the same company name might be spelled three different ways and addresses may appear with no consistent format. Parsing breaks up this unstructured text into usable components. Manipulation then trims the whitespace, standardizes capitalization, and splits combined fields into separate columns. 

5. One-hot encoding for categorical data

Most models can’t work with text labels directly. One-hot encoding converts a categorical column into a set of binary columns, one for each category. For example, a “region” field with North, South, and West becomes three columns where each row gets a 1 or a 0. This avoids misinterpretation by giving models a clear numeric input.

6. Binning

Binning takes continuous values and groups them into ranges. Instead of working with exact values, you work with brackets like 18–25 and 26–35. While this reduces precision, patterns become easier to see in reports and dashboards. It’s useful when exact numbers matter less than the general category records fall into.

What are some examples of data wrangling?

Data wrangling shows up anywhere raw data needs to be cleaned before use. Here are a few common examples across business functions:

• Finance and accounting: Consolidate transaction data from multiple systems, standardize currency formats, and reconcile ledger entries before closing the books. 
• Sales and marketing: Deduplicate lead lists, normalize customer names across CRM records, and merge campaign data from different platforms.
• Human resources: Track payroll data, benefits enrollment, and performance reviews.
• Operations and supply chain: Organize vendor data, shipment logs, and inventory records to track fulfillment performance.

Data wrangling challenges

Here are some of the most common challenges teams face with data wrangling:

• It takes longer than anyone expects. Data teams spend 40–60% of their time on data preparation before any actual analysis begins. The ratio gets worse as datasets grow.
• Sources rarely agree with each other. One system stores a date starting with the month, another starts with the year. Inconsistencies like this multiply across hundreds of columns.
• Transformations break when sources change. A data transformation that worked fine last month can silently produce wrong numbers after a source system renames a column or changes a data type.
• Reproducibility is hard without version control. If data wrangling steps live in someone’s local notebook or a one-off script, reproducing results months later becomes a guessing game. Teams that don’t version-control their transformations end up rebuilding logic from scratch when something breaks.

Start your data wrangling with Fivetran

Manual data wrangling works when dealing with a few sources and a small team. But as the number of sources grows, the manual approach breaks down. Pipelines need maintenance, schemas drift, and the time your team spends on plumbing takes away from analysis.

Fivetran automates the extraction and loading, giving your team clean, normalized data in the warehouse without building and maintaining custom pipelines. From there, Fivetran’s Transformations handles the modeling and structuring to turn raw data into something analysts can work with. 

By combining data automation and built-in data curation capabilities, Fivetran reduces the wrangling burden on your team, freeing them to focus on analysis that drives decisions. 

‍Streamline your data wrangling with Fivetran.

FAQ

What is the best software available for data wrangling?

Fivetran is the strongest option for teams that want to reduce wrangling at the source. Rather than cleaning data after it lands, Fivetran automates extraction and loading with built-in schema drift handling, so the data arriving in your warehouse is already normalized and structured. Fivetran eliminates data wrangling at the pipeline level through 700+ managed connectors.

How do you wrangle data?

The standard data wrangling process has six stages: discovery, structuring, cleaning, enriching, validating, and publishing. Depending on your tools and data, that can change — but is consistent with most workflows.

What is the difference between data wrangling and data munging?

There’s no meaningful difference. Data wrangling and data munging refer to the same data process of transforming raw data into a usable format. 

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