Data Cleaning: Techniques, Benefits, & Examples

We all know data is a powerful asset, but only when it's accurate, consistent, and usable. Organizations can’t afford to depend on dashboards, machine learning models, or strategies built on unreliable data. That’s why data cleaning is so important. It’s a critical step in the analytics and AI pipeline. It ensures that raw data is corrected, standardized, and structured to support reliable insights. 

What is data cleaning?

Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in data sets. It’s a key part of preparing data, often done before getting into the analysis, reporting, or modeling.

Cleaning involves more than just fixing typos. It includes tasks like removing duplicate records, standardizing formats, filling in missing values, correcting structural errors, and validating data against expected ranges or formats. It may also involve reconciling conflicting entries from different sources or transforming raw inputs into usable formats.

Without proper data cleaning, organizations risk basing decisions on flawed or misleading information. Poor-quality data can skew results, reduce model accuracy, and erode trust.

The goal is to make data accurate, complete, and reliable enough to use confidently in decision-making, reporting, automation, and AI applications. Clean data improves everything from customer segmentation to financial forecasting—and is essential for maintaining operational efficiency and ensuring compliance in regulated industries.

In this guide, we’ll explore data cleaning techniques you can use to provide real business benefits. We’ll look at real-world use cases, and how you can scale your efforts across teams and tools.

Why data cleaning matters

Dirty data leads to poor decisions, lost productivity, and increased risk. From forecasting revenue to targeting marketing campaigns, almost every business function relies on high-quality data to operate efficiently and competitively.

When data is inaccurate, incomplete, or inconsistent, it undermines trust and slows down decision-making. Leaders may hesitate to act on reports if they suspect the underlying data isn’t reliable. Analysts spend valuable time validating or reprocessing data sets instead of generating insights.

Here’s how bad data impacts organizations:

• Decision risk: Insights drawn from flawed data can lead to misinformed strategies and missed opportunities.
• Operational inefficiencies: Teams waste time cleaning or validating data manually instead of analyzing it.
• AI and ML failure: Models trained on inconsistent or biased data deliver poor predictions and outcomes.
• Compliance exposure: Inaccuracies in regulated data (like health or finance records) can lead to audits or penalties.

According to Gartner, bad data costs companies an average of $12.9 million per year. The hidden costs—lost customers, failed initiatives, reputational harm—can be even greater. Investing in data cleaning not only protects business performance but also unlocks the full value of data-driven operations.

Common data cleaning techniques

Every data set has its own issues, but most data cleaning workflows include these foundational techniques:

Removing duplicates

‍Duplicate records inflate metrics, introduce redundancy, and create confusion, especially when data is aggregated from multiple sources or entered manually. Teams typically deduplicate using unique identifiers, key fields, or fuzzy matching algorithms to identify near matches.

Handling missing values

‍Missing data can distort analysis or cause systems to fail. The right approach depends on context, volume, and business needs.

Options to address missing values include:

• Removing rows with missing fields.
• Inputting values with the mean or the median, or using predictive models.
• Flagging incomplete records for manual follow-up.

Standardizing formats

‍Inconsistent formatting, such as different date structures, text casing, or units of measurement, creates friction in data pipelines. For example, “01/12/25” and “2025-01-12” and “12-Jan-2025” should be normalized to a single format for accurate analysis.

Validating data types

‍Fields often contain mismatched types, like storing numbers as text or using inconsistent Boolean values (e.g., “Yes”; “Y”; and “true”). Validation ensures fields follow expected schemas so joins, calculations, and filters work correctly.

Correcting structural errors

‍These include typos, incorrect field mappings, or inconsistent naming conventions, like “CA” and “Calif” and “California”. Standardization and regex-based corrections help unify categories and ensure consistent labeling.

Detecting and managing outliers

‍Outliers can signal either meaningful anomalies or data errors. Techniques like Z-scores or IQR can identify them. Once detected, teams decide whether to exclude, flag, or investigate further based on context.

Tracking changes

‍A good data cleaning process is auditable. Logging transformations, decisions, and corrections improves transparency, aids debugging, and supports compliance. Modern tools often include version control or data lineage features to support this process.

Real-world examples of data cleaning

Data cleaning is a business enabler. When done well, it improves decision-making, streamlines operations, and boosts performance across industries. The following examples show how organizations could use data cleaning to drive real outcomes:

Retail: Inventory accuracy

A global retailer might want consistent product and pricing data from e-commerce, POS systems, and suppliers. Discrepancies between SKU codes and price fields could cause delays in forecasting and replenishment. By standardizing product IDs, deduplicating records, and aligning formats, the company could improve inventory accuracy and reduce out-of-stock events by 15 percent.

Healthcare: Regulatory reporting

A regional healthcare provider could have patient data scattered across different systems. Duplicates, inconsistent coding, and missing fields could make it difficult to meet reporting requirements. Data cleaning would merge duplicate records, standardize ICD codes, and fill in demographic gaps. Results could be better patient outcomes tracking and compliance with reporting mandates.

SaaS: Reducing churn

A SaaS company likely uses data from CRM, support tools, and product logs, but each source may have different user IDs and inconsistent timestamps. They could build a cleaning pipeline to reconcile records and standardize fields. The results would likely be a reliable data set that supported a churn model, which would help the customer success team act earlier to retain accounts.

Finance: Fraud detection

A financial services firm might notice gaps and inconsistencies in transaction data across internal systems and third-party feeds. These inconsistencies would make it hard to detect fraud patterns in real time. By implementing automated cleaning routines—including format normalization, duplicate removal, and outlier detection—they could increase the accuracy of fraud models and reduce false positives significantly.

Manufacturing: Supply chain efficiency

A manufacturer likely has supplier data coming from spreadsheets, emails, and legacy procurement systems. Inconsistent part numbers and location formats could lead to confusion and delays. Cleaning and standardizing supplier records would enable better forecasting and streamlined procurement, resulting in shorter lead times and lower costs.

Marketing: Campaign optimization

A consumer brand would struggle with dirty customer data from various sources—loyalty apps, email systems, and online stores. Misspelled names, outdated emails, and duplicate records would limit segmentation. By cleaning and validating the data, the brand could improve email deliverability, increase open rates, and achieve higher ROI from personalized campaigns.

Benefits of clean data

Clean data produces better outcomes across the business, from faster reporting to more accurate forecasts.

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Clean data leads to greater trust, which makes data-driven initiatives easier to adopt and scale.

Building a scalable data cleaning strategy

As data volumes grow and pipelines become more complex, one-off fixes and manual workarounds no longer cut it. A scalable data cleaning strategy ensures your organization can trust its data consistently, efficiently, and at scale. 

Here’s how to develop a data cleaning strategy:

Audit first

Before fixing anything, assess where your data quality issues are coming from. Use profiling tools to spot duplicates, missing values, outliers, and inconsistent fields. This helps prioritize efforts and identify systemic issues that require structural solutions.

Automate where possible

Manual cleaning doesn’t scale. Automate common steps using transformation tools, scripts, or built-in features in platforms like Domod. Set up repeatable logic for deduplication, type validation, and format standardization to reduce time and human error.

Collaborate across teams

Data cleaning isn’t just IT’s job. Involve stakeholders from analytics, operations, finance, compliance, and marketing to define what “clean” means in context. Business teams often understand the data’s meaning and impact better than technical users alone.

Prevent errors upstream

Apply governance rules, field-level validation, and standardized formats as early as possible in your data flow, ideally at the point of entry. For example, enforcing dropdowns in forms or restricting input formats can prevent issues before they reach your warehouse.

Monitor and iterate

Data quality isn’t a one-time project. Set up observability with tools like Great Expectations or Monte Carlo to continuously monitor for emerging issues. Trigger alerts when anomalies or schema changes are detected and evolve your cleaning logic as new sources or use cases arise.

Document and govern

Establish clear documentation for cleaning logic, ownership, and business definitions. This helps teams onboard faster, stay aligned, and reduce confusion when pipelines break or data requirements shift.

Test and validate regularly

Incorporate data tests into your pipelines, especially for critical metrics or models. Regular validation ensures your cleaning processes work as intended and maintains trust in downstream analysis.

Tools that support data cleaning

Several platforms include data cleaning as part of their core functionality. Choose based on your team’s skill level, budget, and existing stack.

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These tools help standardize and scale cleaning workflows across teams.

What about real-time cleaning?

Most data cleaning is traditionally done in batches after data lands in a warehouse or lake. But as the need for real-time insights grows, so does the demand for real-time data cleaning.

In fast-paced environments like e-commerce, financial services, or logistics, data has to be cleaned and validated the moment it’s generated. For example, e-commerce companies often clean and verify clickstream events, cart interactions, or customer inputs as they stream in. This enables real-time personalization, fraud detection, and operational decision-making without waiting for an overnight batch job.

Real-time cleaning requires a different approach. It depends on streaming data platforms such as Apache Kafka, Apache Flink, or cloud-native tools like AWS Kinesis or Google Cloud Dataflow. These systems support inline data validation, schema enforcement, deduplication, and transformation on the fly.

To make it work, teams must define rules ahead of time, implement robust error handling, and balance data quality with speed. Real-time cleaning adds complexity, but for businesses that rely on instant action, it’s essential for keeping data fresh, accurate, and actionable.

How to evaluate your data cleaning maturity

Ask yourself these questions to assess where you stand:

1. Do we have a documented data quality policy?
2. Are our cleaning steps automated and version-controlled?
3. Is cleaned data traceable back to the source?
4. Do users trust the data in our dashboards?
5. Are we actively monitoring for new quality issues?

If you answered “no” to two or more, it might be time to level up your strategy.

Final thoughts

Clean data is the foundation of reliable analytics, trustworthy dashboards, and high-performing AI models. But cleaning isn’t a one-time event, it’s a continuous practice that scales with your business.

Organizations that invest in automated, collaborative, and well-governed cleaning workflows will spend less time fixing data and more time using it.

If you're looking to simplify your data cleaning process, Domo can help. By combining ingestion, transformation, and visualization in one platform, Domo makes it easier to go from raw data to reliable insights.

‍Watch a demo to see how Domo supports clean, governed, and real-time data at scale.