How to optimize data cleaning to ensure efficiency and quality results

What is Data Cleaning? Step-by-step Guide

Discover how data cleaning improves datasets and enables reliable analyses central to business growth and success. Learn the methods and best practices.

Table of Contents

                      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.

                      Why do you need to clean data?

                      Data can become messy, incomplete, or inaccurate in various ways—necessitating cleaning.

                      • Human error: Mistakes happen whenever people enter or collect data manually. Typos, accidental duplicate entries, and wrong values often find their way into data.
                      • Multiple data sources: Companies aggregate data from various sources, like CRM platforms, inventory databases, and sales reports. These may have different formats, labels, and descriptors, and combining them can cause inconsistencies.
                      • Equipment errors: Machines such as sensors, meters, and gauges can sometimes malfunction and send faulty readings into the data pipelines.
                      • Outdated information: Data can change over time, including names, addresses, salaries, and inventory labels, and if the old data isn’t updated, it can become untrustworthy.
                      • Software issues: Bugs or limitations in data collection, storage, analysis, or visualization can degrade data integrity over time.

                      Data cleaning vs. data transformation

                      Data cleaning and transformation are two methods often used hand-in-hand to improve and prepare data for usage.

                      Some businesses merge cleaning and transformation tasks into a single step if they’re short on time or resources or don’t have too much data.

                      However, there are some key differences between them.

                      Data cleaning:

                      • Detects and fixes poor data, such as errors, missing values, duplicates, etc.
                      • Focuses on enforcing integrity and consistency
                      • Deals with data at its raw level
                      • Improves data quality

                      Data transformation:

                      • Manipulates correct raw data into the best formats
                      • Focuses on structuring, aggregating, calculating, combining, filtering, and enriching data
                      • Includes post-cleaning activities to prepare the data for analysis
                      • Improves data usability

                      While data cleaning fixes your dataset’s erroneous or anomalous parts, data transformation morphs your clean data into the formats you need for business intelligence (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.

                      The importance of data cleaning

                      Cleaning data provides substantial quantitative and qualitative benefits for business operations and data analytics.

                      Cleaning your data is essential for the following reasons:

                      • Enables reliable analytics: Dodgy data can distort analytics. Cleaning ensures quality data that leads to accurate insights.
                      • Improves decision making: You can make better-informed strategic and operational decisions with clean, trusty data.
                      • Increases efficiency: You waste less time gathering data that could be faulty and useless. Cleaning weeds these issues out.
                      • Saves money: Bad data can lead to costly errors, while good data cleaning practices can help you save money in the long run.
                      • Builds trust: Dependable data that tells the truth about your business performance helps build stakeholder confidence.
                      • Supports automation: Artificial intelligence (AI) and machine learning (ML) driven automation need clean data. Otherwise, they may amplify existing data problems.
                      • Ensures compliance: In regulated industries, meticulous data quality controls help you support compliance.

                      Overall, good data hygiene through cleaning stops false insights in their tracks, giving data engines the fuel they need to keep organizations running smoothly.

                      How to clean data effectively

                      Thoroughly cleaning data can be complex, but it usually involves a set of central tasks.

                      Here are some key ways to transform disordered, unreliable data into high-quality information.

                      Identify quality issues

                      The first step is to scan and identify parts of your data that seem like errors.

                      These might be:

                      • unrealistic values
                      • textual inconsistencies
                      • dubiously high or low metrics
                      • missing entries
                      • duplicated rows
                      • strange outliers

                      Statistics and visualizations can help you spot 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. Master data management 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 validate your data’s cleanliness before you use it. It adds peace of mind and can weed out any lingering issues.

                      Why is manually cleaning data challenging?

                      Manually cleaning your data can be painful for the following reasons:

                      • Extremely time-consuming: Finding and correcting records one by one requires significant analyst time investments.
                      • Risk of human error: You can miss or imperfectly clean records you manually flag for cleaning, meaning some issues might slip through the cracks.
                      • Complex patterns are hard to spot: The human eye may fail to see intricate hidden data patterns that reveal deeper problems.
                      • Difficult to scale: As data volume, variety, and velocity grow, manual cleaning becomes infeasible.
                      • Better uses of human effort: Your skill analysts should spend time on interpretation, analysis, and decision support rather than repetitive data scrubbing tasks.
                      • Temporary band-aid: New data can continue accruing quality problems that need ongoing cleaning, which humans can’t keep up with.

                      Automated assistance can help you avoid manual cleaning bottlenecks and avoid the never-ending chore of data hygiene. Advanced solutions can learn the intricacies of big datasets and support scalable and stable data cleaning.

                      Data cleaning best practices

                      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 governance and enable you to reproduce the cleaning process in the future.

                      Backup 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

                      You can use many techniques to transform untidy data, applying various automated methods and human checks to fix it.

                      Here are some go-to approaches.

                      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 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 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, low, or contradictory. Human data experts can then analyze the flagged issues and determine appropriate corrections.

                      Gap filling

                      When key fields like customer addresses or product prices are missing or blank, your cleaning system can smartly 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.

                      Master data management

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

                      Data cleaning tools and software

                      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.

                      Improve your data quality with Amplitude

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

                      Amplitude offers a user-friendly solution with robust data governance 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 merge data sources
                      • Ensure accurate and consistent user identification
                      • Use anomaly detection features to spot and address unusual events
                      • Keep an eye on data consumption and taxonomy changes

                      Trusted data is foundational to your business. Get in touch today to discover how Amplitude can enhance your data management approach.