What is data cleaning?

The goal of data cleaning is to help ensure that data is accurate, complete, consistent and usable for analysis or decision-making. Data cleaning processes work to address common data quality issues such as duplicates, missing values, inconsistencies, syntax errors, irrelevant data and structural errors.

Data cleaning is also a core component of effective data management, which helps ensure that data remains accurate, secure and accessible at every stage of its lifecycle.

High-quality or “clean” data is crucial for effectively adopting artificial intelligence (AI) and automation tools. Organizations can also use AI to help streamline the data cleaning process.

Organizations with clean, well-managed data are better equipped to make reliable, data-driven decisions, respond swiftly to market changes and streamline workflow operations.

Cleaning data is an integral component of data science, as it is an essential first step to data transformation: data cleaning improves data quality, and data transformation converts that quality raw data into a usable format for analysis.

Data transformation enables organizations to unlock the full potential of data to use business intelligence (BI), data warehouses and big data analytics. If the source data is not clean, the outputs of these tools and technologies could be unreliable or inaccurate, leading to poor decisions and inefficiencies.

Similarly, clean data also underpins the success of AI and machine learning (ML) in an organization. For instance, data cleaning helps ensure that machine learning algorithms are trained on accurate, consistent and unbiased data sets. Without this foundation of clean data, algorithms could produce inaccurate, inconsistent or biased predictions, reducing the effectiveness and reliability of decision-making.

The key benefits of data cleaning include:

• Informed decision-making
• Improved productivity
• Cost efficiency
• Data compliance and security
• Enhanced model performance
• Improved data consistency

Decisions based on clean, high-quality data are more likely to be effective and aligned with business goals. In contrast, business decisions based on dirty data—with duplicate data, typographical errors (typos) or inconsistencies—can result in wasted resources, missed opportunities or strategic missteps.

Clean data enables employees to spend less time fixing errors and inconsistencies, accelerating data processing. Then, teams have more time to focus on data analysis and insights.

Poor data quality can lead to costly errors, such as overstocking inventory due to duplicate records or misinterpreting customer behavior because of incomplete data. Data cleaning helps prevent these errors, saving money and reducing operational risks.

Clean data can help organizations comply with data protection regulations, such as the European Union's General Data Protection Regulation (GDPR), by keeping data accurate and current. It also prevents the accidental retention of redundant or sensitive information, reducing security risks.

Data cleaning is essential for training effective machine learning models. Clean data improves the accuracy of outputs and helps ensure that models generalize well to new data, leading to more robust predictions.

Data cleaning helps ensure that combined data is consistent and usable across systems, preventing issues that can arise from conflicting data formats or standards. This is important for data integration, where clean and standardized data helps to ensure that disparate systems can communicate and share data effectively.

Data cleaning typically begins with data assessment. Also known as data profiling, this assessment involves reviewing a data set to identify quality issues requiring correction. When identified, organizations might employ various data cleaning techniques, including:

• Standardization
• Addressing outliers
• Deduplication
• Addressing missing values
• Validation

Inconsistencies arise when data is represented in different formats or structures within the same data set. For example, a common discrepancy is the date format, such as “MM-DD-YYYY” versus “DD-MM-YYYY.” Standardizing formats and structures can help ensure uniformity and compatibility for accurate analysis.

Outliers are data points that deviate significantly from others in a data set, caused by errors, rare events or true anomalies. These extreme values can distort analysis and model accuracy by skewing averages or trends. Data management professionals can address outliers by evaluating whether they are data errors or meaningful values. Then, they can decide to retain, adjust or remove those outliers based on relevance to the analysis.

Data deduplication is a streamlining process in which redundant data is reduced by eliminating extra copies of the same information. Duplicate records occur when the same data point is repeated due to integration issues, manual data entry errors or system glitches. Duplicates can inflate data sets or distort analysis, leading to inaccurate conclusions.

Missing values arise when data points are absent due to incomplete data collection, input errors or system failures. These gaps can distort analysis, lower model accuracy and limit the data set’s utility. To address this, data professionals might replace missing data with estimated data, remove incomplete entries or flag missing values for further investigation.

A final review at the end of the data cleaning process is crucial in verifying that the data is clean, accurate and ready for analysis or visualization. Data validation often involves using manual inspection or automated data cleaning tools to check for any remaining errors, inconsistent data or anomalies.

Data scientists, data analysts, data engineers and other data management professionals can perform data cleaning techniques through manual methods, such as visual inspection, cross-references or pivot tables in Microsoft Excel spreadsheets.

They might also use programming languages such as Python, SQL and R to run scripts and automate the data cleaning process. Many of these approaches are supported by open source tools, which provide flexibility and cost-effective solutions for organizations of all sizes.

However, AI can also be used to help automate and optimize several data cleaning steps, including:

• Analyzing source data: AI-powered data cleansing tools can automatically identify patterns, anomalies and inconsistencies in source data. AI can also suggest relevant business rules by analyzing data trends and relationships, reducing manual efforts in defining these rules. For example, AI can identify that a column of phone numbers often has missing area codes, and then suggest a rule for standardization.

• Standardizing data: Natural language processing (NLP) techniques can standardize unstructured text, such as formatting addresses or product descriptions. Machine learning models can also identify and recommend consistent formats for data such as dates or currencies. AI-powered regular expression generators can automate the detection and normalization of inconsistent formats.

• Consolidating duplicates: Rule-based or learned AI models can decide the best record to “survive” when deleting duplicates, considering accuracy, recency or reliability. For example, models can prioritize specific fields based on context, such as keeping the most recent email address in the consolidated record.

• Applying rules: AI models can automate the creation and enforcement of data cleansing rules by learning from historical corrections and user feedback. They can apply these rules dynamically to multiple data sets, helping to ensure consistency across systems. AI systems can also generate custom rules for specific industries or domains, such as value-added tax (VAT) identification numbers in the European Union.