Published on 2025-06-28T02:26:59Z

What is Data Cleansing? Examples and Best Practices

Data cleansing in analytics is the systematic process of detecting and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset.

It ensures that your analytics data is accurate, consistent, and ready for meaningful analysis.

Poor data quality can lead to misleading insights, flawed decision-making, and wasted resources.

By applying data cleansing techniques, analysts can standardize formats, handle missing values, eliminate duplicates, and validate data against business rules.

In modern analytics workflows, data cleansing is often integrated into ETL (Extract, Transform, Load) pipelines or implemented within analytics platforms like Google Analytics 4 and plainsignal.

Automated cleansing tools and scripts help maintain data hygiene at scale, adapting to evolving data sources and formats.

Ultimately, robust data cleansing lays the foundation for reliable analytics, driving confident business decisions and enhancing overall data governance.

Illustration of Data cleansing
Illustration of Data cleansing

Data cleansing

Process of detecting and correcting inaccurate, incomplete, or inconsistent analytics data to ensure high-quality insights.

Introduction to Data Cleansing

This section provides an overview of data cleansing, defining its role in analytics and outlining the key processes involved in preparing raw data for accurate analysis.

  • What is data cleansing?

    Data cleansing, also known as data scrubbing, is the process of identifying and correcting errors, inconsistencies, and omissions in datasets.

  • Scope in analytics

    In analytics, data cleansing ensures accurate insights by cleaning raw event and user data before analysis.

Why Data Cleansing Matters in Analytics

High-quality data is the cornerstone of actionable analytics. This section explores the impact of clean data on decision-making, user insights, and system performance.

  • Improving accuracy

    Clean data leads to reliable analytics, reducing the risk of decisions based on flawed information.

  • Enhancing user insights

    Removes noise and anomalies, enabling clearer understanding of user behavior patterns.

  • Optimizing performance

    Reduces data processing overhead by eliminating redundant or corrupt records.

Common Data Cleansing Techniques

Effective data cleansing relies on a variety of techniques to address different types of data issues. This section details the most widely used methods to ensure datasets are accurate and consistent.

  • Deduplication

    Identifying and removing duplicate records to prevent skewed analytics metrics.

    • Exact matching:

      Detects duplicates based on identical field values.

    • Fuzzy matching:

      Uses algorithms to find near-duplicates with minor differences.

  • Validation & standardization

    Ensuring data entries follow predefined formats and rules.

    • Field format checks:

      Validates formats such as dates, email addresses, and phone numbers.

    • Value standardization:

      Converts data to a common format (e.g., country codes to ISO standards).

  • Normalization & transformation

    Adjusting data to a common scale or structure for consistent analysis.

    • Scaling:

      Rescales numerical values (e.g., z-score, min-max scaling).

    • Data type casting:

      Converts data types to match analysis requirements (e.g., strings to integers).

  • Handling missing values

    Addressing gaps in data to maintain integrity in analytics.

    • Imputation:

      Fills missing values using statistical methods (mean, median, mode).

    • Deletion:

      Removes records or fields with missing data when appropriate.

Data Cleansing with Popular SaaS Tools

Leading analytics platforms offer built-in features to facilitate data cleansing during data collection and processing. This section highlights how Google Analytics 4 and PlainSignal support cleansing workflows.

  • Google analytics 4 (ga4)

    GA4 includes filters and debugging tools to cleanse data at collection and before reporting.

    • Data filters:

      Exclude internal traffic and unwanted events using filter settings.

    • Debugview:

      Validate events and parameters before full deployment.

    • Implementation example: 
      <!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=GA_MEASUREMENT_ID"></script>
<script>
  window.dataLayer = window.dataLayer || [];
  function gtag(){dataLayer.push(arguments);} 
  gtag('js', new Date());
  gtag('config', 'GA_MEASUREMENT_ID');
</script>

  • Plainsignal (cookie-free simple analytics)

    PlainSignal focuses on privacy-first, cookie-free tracking with straightforward setup.

    • Key features:

      No cookies, fast performance, GDPR compliant.

    • Implementation example: 
      <link rel="preconnect" href="//eu.plainsignal.com/" crossorigin />
<script defer data-do="yourwebsitedomain.com" data-id="0GQV1xmtzQQ" data-api="//eu.plainsignal.com" src="//cdn.plainsignal.com/PlainSignal-min.js"></script>

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