What Is Data Cleaning?

Data cleaning is the process of detecting and correcting errors or inconsistencies in your data to improve its quality and reliability.

Data cleaning might involve:

• Filling in missing values
• Filtering out duplicates or invalid entries
• Standardizing formats
• Cross-checking information
• Adding more details

The goal is to spot and fix problems and ensure you have clean data for better analysis and more accurate business insights.

Data becomes messy through several common sources:

• Human error: Manual data entry can lead to typos, duplicate entries, and incorrect values.
• Multiple data sources: Different systems use inconsistent formats, labels, and structures when combined.
• Equipment malfunctions: Sensors, meters, and gauges send faulty readings to data pipelines.
• Outdated information: Names, addresses, and other details change over time without updates.
• Software bugs: Issues in collection, storage, and analysis tools degrade data integrity.

Data cleaning fixes errors and inconsistencies in raw data. Data transformation converts clean data into usable formats for analysis.

While related, these processes serve different purposes:

Data cleaning:

• Purpose: Fix errors, remove duplicates, fill missing values
• Focus: Data quality and integrity
• Stage: Applied to raw, unprocessed data

Data transformation

• Purpose: Structure and format clean data for analysis
• Focus: Data usability and format optimization
• Stage: Applied after data cleaning is complete

Many businesses combine these processes, but keeping them separate makes each phase more thorough and efficient.

Data cleaning fixes your dataset’s erroneous or anomalous parts, while data transformation morphs your clean data into the formats you need for (BI) or other applications.

Ideally, you clean your data before transforming it for practical usage. Keeping the two processes separate makes each phase more efficient and thorough.

Data cleaning delivers measurable business value across multiple areas:

• Accurate analytics: Clean data produces reliable insights for better strategic decisions.
• Cost savings: Prevents expensive mistakes from bad data, reducing operational waste.
• Increased efficiency: Teams spend less time validating questionable data sources.
• Stakeholder trust: Consistent, accurate reporting builds confidence in business performance.
• AI/ML readiness:  require clean data to avoid amplifying errors.
• Regulatory compliance: Quality controls help meet industry data standards and requirements.

Good stops false insights in their tracks and gives your teams the fuel they need to keep the organization running smoothly.

Effective data cleaning transforms messy, unreliable datasets into trustworthy information for analysis. The process follows eight core steps that address the most common data quality issues.

Identify quality issues

Quality assessment reveals data problems before they impact analysis. Start by scanning your dataset for common error patterns.

Look for these warning signs:

• Unrealistic values: Ages over 150, negative prices, impossible dates
• Text inconsistencies: “NY” vs. “New York” vs. “new york"
• Extreme outliers: Values dramatically higher or lower than expected ranges
• Missing data: Empty cells, null values, blank fields
• Duplicates: Identical or nearly identical rows

Use statistical summaries and data visualization to spot patterns that indicate quality issues.

Correct inconsistent values

Mistakes in your data—like spelling variations, alternate abbreviations, formatting differences, and mismatched convention formats—must be made consistent.

Correcting inconsistent values might involve canonicalization, text normalization, and reference data mapping.

Remove duplicates

Duplicate entries can waste storage and distort your analysis. Deduplication streamlines datasets down to unique, distinct entries only.

Fix structural errors

Problems with the organization, relationships, linkages, hierarchies, and database structures that house your data may need fixing. helps solve these structural issues.

Standardize data

Standardizing different labels, tags, units of measure, descriptors, languages, and characteristics is crucial for a consolidated analysis. Classification, coding, and schema alignment can help.

Spot and remove unwanted outliers

Abnormal data can skew your analysis. It’s best to identify outliers using statistical rules and remove or replace them to improve data stability.

Address missing data

Data input methods enable you to fill empty cells, unclassified categories, and missing entries wherever possible. You can then remove any remaining gaps.

Validate and cross-check

Extra scrutiny, quality control, reasonability checks, accuracy testing, and cross-dataset comparisons help you before you use it. It adds peace of mind and can weed out any lingering issues.

Manual data cleaning becomes impractical as data volumes grow, creating significant resource constraints for teams.

Key limitations include:

• Time-intensive: Analysts spend hours on repetitive tasks instead of strategic analysis.
• Human error: Manual processes miss subtle patterns and introduce new mistakes.
• Poor scalability: Unable to keep pace with growing data volumes and complexity.
• Resource waste: Skilled analysts handle routine tasks rather than generating insights.
• Temporary fixes: New data continuously creates quality issues requiring ongoing attention.

Approaching data cleaning in a standardized, optimized way ensures efficiency and quality results.

Keep these best practices in mind when designing your data cleaning processes.

Document everything

Document each data profiling assessment, every problem you find, the correction details and cleaning steps applied, and any assumptions you make. This will support transparency and , enabling you to reproduce the cleaning process in the future.

Back up original data

Keep original raw datasets intact to compare them during and after cleaning. Archiving messy initial data avoids “cleaning away” actual signals and noise.

Prioritize issues

Focus on cleaning your most impactful data problems before moving to your secondary issues. Go after root causes rather than symptoms.

Automate when possible

Make data cleaning faster and more scalable with automated cleaning methods using statistical calculations, AI flagging, and ML pattern recognition.

Consistently iterate and review

Check in on data quality with dashboards that show metrics and visual insights. Continuously review your cleaning needs as new issues emerge or impacts are flagged.

Data cleaning techniques use automated methods and validation checks to systematically fix different types of data problems. Each technique targets specific quality issues to restore data accuracy.

Typo correction

Spell check catches typos when writing a document; data cleaning tools scan for typos and other formatting problems. Typo correction ensures all your information matches and reads correctly, with no random letters or numbers.

Parsing

Parsing helps break large text fields into distinct components. It applies punctuation, spaces, camel case, semantics, and other techniques.

For instance, you could parse a name and address from a single text string into first name, last name, street, city, state, and ZIP code fields for better structure.

Duplicate removal

When the same data is accidentally entered more than once, tools can spot and remove duplicate copies. This declutters databases and gives you a clearer picture.

This method also applies “fuzzy matching”, where you use ML to find similar (but not identical) elements in datasets.

Error flagging

Sophisticated algorithms can learn data patterns and flag outlier numbers or statistics that seem too high, too low, or contradictory. Human data experts can then analyze the flagged issues and determine appropriate corrections.

Gap filling

When key fields, such as customer addresses or product prices, are missing or blank, your cleaning system can intelligently fill those gaps. It does this by cross-checking historical data and detecting the most likely values from contextual clues.

Data enrichment

Data enrichment involves supplementing existing data by adding information about the same people, products, places, etc.

For example, you might merge in third-party info about where your customers live to give you more context.

Data management

Use to align data from multiple departments, tools, locations, and formats into a unified dataset. It identifies, structures, and labels all the mismatches between siloed data.

Manual data cleaning processes are typically insufficient and infeasible as data volumes and complexity grow.

Thankfully, advanced tools and software can help automate the heavy lifting.

These are some fundamental capabilities to look for:

• Intelligent detection: With machine learning, the system can spot errors in your data without users having to configure every type of issue.
• Bulk actions: Once found, users can quickly fix whole batches of hundreds (sometimes thousands) of problems simultaneously with a single click.
• Built-in integration: Certain tools can seamlessly connect to all your databases and systems to import data. That means no hassle exporting, moving, and uploading files yourself.
• Collaboration tools: Your team can tag each other on questionable data findings, discuss fixes, review cleaned datasets, and approve them for usage downstream.
• Interactive visualization: Dynamic graphs and dashboards enable you to visually explore your data and identify errors that numbers alone might not reveal.

Using the latest automation and ML capabilities, you can save massive amounts of analyst time and resources while improving data accuracy. These tools do most of the work, while humans provide the finishing touches.

This enables you to clean vast and complicated datasets quickly and accurately without getting bogged down in technical details.

Poor-quality data frustrates, leads to poor decisions, and wastes time. Fixing all those mistakes by hand isn’t feasible, especially when your data piles up daily.

offers a user-friendly solution with robust features and tools to help you clean and maintain reliable data.

With Amplitude, you can:

• Set data validation rules to check if incoming information meets specific criteria
• Regularly review data for inconsistencies, then hide or drop invalid data and events
• Create alerts for data-quality issues to receive notifications when data deviates from expected patterns
• Add context to your data through properties and merging data sources
• Ensure accurate and consistent user identification
• Use features to spot and address unusual events
• Keep an eye on data consumption and changes