What Is Data Wrangling? Steps & Examples

Have you ever tried to make decisions based on data, only to find it’s all over the place? That’s because data isn't just automatically ready for decision-making. If we don’t take the time to prepare it, it can be fragmented, inconsistent, and difficult to trust. 

That's where data wrangling comes in—it transforms your raw, unfiltered information into organized, accurate data sets that help inform better business insights and strategies. The term "wrangling" is used because, kind of like handling a large, unruly herd, dealing with raw data often demands teamwork, thoughtful strategy, and hands-on problem-solving to keep everything under control.

If we ignore data wrangling, we risk making costly mistakes. Poor-quality data can lead to flawed analysis, missed opportunities, and lost revenue. Without proper cleaning and structuring, even the most sophisticated analytics tools won't deliver reliable outcomes.

In this guide, we'll walk you through what data wrangling is, the key steps involved, real-world examples, and the tools that can help streamline the process. By understanding these fundamentals, you’ll be able to turn data a powerful assetrather than a liability.

What is data wrangling?

Data wrangling is about taking the raw, messy data and turning it into something clean and structured we can use for analysis and decision-making. It’s a process where we ultimately discover, clean, structure, enrich, and validate the data, getting it ready to be used effectively. 

Why data wrangling matters

Organizations collect huge amounts of data from different sources, like websites, customer interactions, and internal systems. But raw data rarely arrives in a neat package. Without preparation, it can lead to misleading results, wasted time, or flawed decisions.

The "wrangling" aspect captures the hands-on nature of this work—cleaning, shaping, and managing data so that it behaves in predictable, useful ways. Without it, companies risk making strategic decisions based on incomplete or inaccurate information.

Data wrangling helps:

• Improve data quality and reliability.
• Save time during analysis.
• Uncover patterns and trends faster.
• Create a solid foundation for AI, machine learning, and predictive analytics.

Beyond operational efficiency, clean and accessible data directly impacts a business's bottom line. Unfortunately, 67 percent of organizations say they don’t completely trust their data used for decision-making.

Companies that invest in cleaning, managing, and transforming their data not only reduce waste but also boost customer satisfaction by enabling better personalization, faster response times, and more informed service delivery. In a fast-paced market, the ability to trust and quickly act on data can make the difference between leading the competition and falling behind.

Key steps in the data wrangling process

While data wrangling can vary depending on the project, most workflows follow a similar structure. 

Here are the major data wrangling steps:

1. Discover and understand your data

Before making any changes, take time to explore the data set. Ask questions like:

• What types of data are included (numbers, text, dates)?
• Are there missing values?
• Are there clear patterns, inconsistencies, or outliers?

Common challenges:

• Incomplete or undocumented data sources
• Hidden biases in the data that could affect analysis
• Outdated data fields no longer used in business processes
• Discrepancies between what the data should represent and what it actually contains

Best practices:

• Use automated profiling tools to scan and summarize data quickly
• Interview subject matter experts to validate what fields mean
• Create a preliminary "data dictionary" to track field descriptions and data types
• Flag any data fields that seem suspicious or inconsistent early in the process

Example:
A retail company collects online sales data across multiple regions. During discovery, they realize that some regions store customer addresses in separate fields (street, city, zip), while others combine them into one text block.

• Problem: The company could misinterpret address data or fail to match customer records.
• Solution: Profiling the data set reveals the inconsistency early, allowing the team to plan to split or merge fields consistently before analysis begins.

2. Clean the data

Cleaning involves fixing or removing errors, inconsistencies, or inaccuracies.

Typical cleaning tasks:

• Correct typos and formatting issues
• Fill in missing values where appropriate
• Remove duplicate records
• Filter out irrelevant or outlier data points

Common challenges:

• Deciding whether to impute, delete, or ignore missing values
• Inconsistent data entry formats (e.g., date formats, casing in text fields)
• Discovering systemic errors from upstream data collection systems

Best practices:

• Establish clear rules for handling missing or inconsistent data
• Standardize formats (e.g., all dates as YYYY-MM-DD)
• Use automated scripts or wrangling tools to reduce human error
• Document every cleaning decision for future reference

Example:
An e-commerce company is analyzing customer feedback surveys and noticing inconsistencies in state names, which are sometimes spelled out ("California") or abbreviated as "CA" or "Calif."

• Problem: Without standardization, filtering and grouping data by state would yield inaccurate results, undermining regional analyses.
• Solution: The company creates a cleaning rule to standardize all state fields into official USPS two-letter codes, improving consistency across the data set.

3. Structure the data

Sometimes, data needs to be reshaped or reorganized to meet analytical needs.

Tasks can include:

• Splitting columns (e.g., "Full Name" into "First Name" and "Last Name")
• Aggregating data by key fields
• Pivoting tables for different views

Common challenges:

• Over-normalizing data and making it hard to read
• Accidentally losing granularity that's important for analysis
• Dealing with nested or hierarchical structures (especially JSON data)

Best practices:

• Structure the data to match the needs of your intended analysis
• Keep a raw backup data set in case restructuring needs to be rolled back
• Use ETL (extract, transform, load) pipelines when transformations get complex

Example:
A company wants to analyze customer buying patterns over time, but its sales data stores "Date of Purchase" in a text field instead of a true date format.

• Problem: Without correctly structured date fields, it’s impossible to build time-based reports like "monthly sales trends" or "year-over-year growth."
• Solution: During structuring, the team converts text strings into proper date formats, creates a "Purchase Month" field for aggregation, and rearranges the data set to make time-based analysis fast and reliable.

4. Enrich the data

Enhancing a data set can make it even more valuable for decision-making.

Typical enrichment activities:

• Merging multiple data sets together
• Adding new calculated fields
• Bringing in external data sources (weather, demographics, etc.)

Common challenges:

• Matching records between data sets when IDs are inconsistent or missing
• External data sets might not update at the same frequency as internal systems
• Risk of introducing biased or outdated third-party data

Best practices:

• Use fuzzy matching techniques for merging imperfect data sets
• Validate enrichment fields with business stakeholders
• Document all enrichment sources and update schedules

Example:
A marketing team has a database of customer email addresses and wants to personalize campaigns based on location. However, the current data only includes zip codes.

• Problem: They can’t easily segment customers by region or city without additional context.
• Solution: They enrich the data set by mapping each zip code to its corresponding city, state, and region using an external database. Now, targeted regional marketing becomes easy, and campaign effectiveness improves.

5. Validate the data

Validation ensures that transformations were applied correctly and the data remains trustworthy.

Validation activities include:

• Checking for formatting issues
• Ensuring relationships between fields make sense
• Verifying totals and subtotals after transformations

Common challenges:

• Hidden errors that only emerge during downstream analysis
• Manual spot-checking that misses systemic problems
• Validation rules that are too rigid and block useful edge cases

Best practices:

• Set up automated validation scripts wherever possible
• Use sampling techniques to spot-check critical fields
• Validate both structure (formats) and content (logic)

Example:
After merging sales and customer support data sets, a SaaS company notices that some customer IDs are missing in the merged table.

• Problem: Missing IDs could mean important customer interactions aren't linked to purchase history, leading to incomplete customer profiles and wrong insights.
• Solution: The company implements validation rules that cross-reference customer IDs between systems, flagging and resolving mismatches before using the data for retention analysis.

6. Publish and use the data

Once validated, the cleaned and structured data needs to be made available for analysis.

Publication activities include:

• Exporting data sets to databases, cloud storage, or BI platforms
• Setting user permissions and access controls
• Versioning data to maintain traceability

Common challenges:

• Users accessing outdated or incomplete data sets
• Misunderstandings around which version of the data is "official"
• Security risks if sensitive fields are not properly masked

Best practices:

• Implement strict version control policies
• Set clear data access policies for internal and external users
• Build metadata layers or data catalogs to help users find and understand available data sets

Example:
A financial services firm prepares quarterly reports for leadership. Historically, each team pulled its own numbers, leading to conflicting metrics.

• Problem: Different versions of "the truth" create confusion, wasted time, and strategic missteps.
• Solution: After wrangling and validating the master data set, the firm sets up a secure, published data set with version control. All teams access the same data source for reports, ensuring consistency and trust at the executive level.

Real-world examples of data wrangling

Data wrangling isn’t just a theoretical exercise—it’s a practical process that organizations use every day to turn scattered, messy data into clear, actionable insights. The following real-world examples show how leading companies identified messy or disconnected data problems, applied wrangling techniques, and achieved measurable business results. 

As you review these cases, notice how common steps like cleaning, structuring, enriching, validating, and publishing data made a meaningful impact across industries.

DHL: Simplifying operational complexity through data wrangling 

DHL operates one of the largest logistics networks in the world, generating massive volumes of shipment, delivery, and warehouse data daily. Before wrangling their data, critical metrics were buried across disconnected systems, making real-time operational decisions difficult.

Using Domo, DHL unified its operational data into a single environment, cleaned inconsistent location codes, standardized timestamps, and enriched shipment records with real-time tracking updates. Validation steps ensured consistent reporting across the network. As a result, DHL empowered teams with faster, more accurate insights, leading to improved delivery times and enhanced customer satisfaction.

La-Z-Boy: Democratizing insights through clean, connected data

La-Z-Boy faced challenges managing siloed information across sales, manufacturing, and customer service departments. Each system stored data differently, making it hard to connect the dots across the customer journey.

Through a coordinated data wrangling initiative, La-Z-Boy cleaned and standardized product and customer data, structured historical and live data sets for easier access, and enriched records with customer satisfaction metrics. After validating the unified data sets, they deployed self-service dashboards, giving employees at all levels the ability to act on trusted data. This transformation accelerated decision-making and improved operational agility across the business.

Cisco: Scaling marketing intelligence with stronger data foundations

Cisco’s marketing organization collected extensive campaign, engagement, and sales data. However, inconsistencies in field names, outdated formats, and duplicate records created bottlenecks that slowed global reporting efforts.

With Domo, Cisco automated data cleaning processes (standardizing fields like country codes and customer statuses), structured campaign data to map directly to sales outcomes, and enriched internal data with external firmographic insights. Rigorous validation protocols ensured data quality before publishing dashboards. The result: faster marketing performance insights, better alignment between sales and marketing, and stronger global scalability.

Why Domo is your solution for smarter, faster data wrangling

Data wrangling is a crucial first step in transforming raw data into meaningful insights. By carefully exploring, cleaning, structuring, enriching, validating, and publishing your data, you lay the groundwork for confident decision-making, better strategies, and measurable results.

The term "wrangling" captures the reality: real-world data rarely arrives in perfect form. It often requires taming, cleaning, and reshaping before it can truly drive business value. Organizations that invest in wrangling gain a critical advantage—they spend less time cleaning up messes and more time uncovering growth opportunities.

But you don’t have to tackle data wrangling challenges alone. Domo empowers businesses to connect, clean, transform, and manage data seamlessly—all within a single platform. Whether you're unifying siloed systems, automating cleaning tasks, or scaling real-time analytics, Domo helps you move from messy data to meaningful action faster.

Learn how Domo simplifies data wrangling and drives better insights.

‍