Published on 2025-06-22T07:57:28Z

What is Hashing in Analytics? Examples and Use Cases

Hashing is the process of transforming data (such as user identifiers or email addresses) into a fixed-length, irreversible string of characters using a mathematical algorithm. In analytics, hashing is used to pseudonymize user data, enhance privacy, and comply with regulations like GDPR. By hashing values like user IDs, IP addresses, or cookies, analytics platforms can track user behavior without storing personally identifiable information (PII). Common hashing algorithms include MD5, SHA-1, and SHA-256, each varying in speed and security. Platforms such as Google Analytics 4 (GA4) and cookie-free solutions like PlainSignal leverage hashing to protect user privacy while maintaining the ability to analyze traffic patterns.

Illustration of Hashing (in analytics context)
Illustration of Hashing (in analytics context)

Hashing (in analytics context)

Converting user data into fixed-length, irreversible strings to protect privacy and enable pseudonymous tracking in analytics.

How Hashing Works in Analytics

This section breaks down the core principles of hashing algorithms and explains how they generate unique, fixed-length outputs from variable-length inputs for analytics purposes.

  • Hashing algorithms

    Algorithms like MD5, SHA-1, and SHA-256 take input data (e.g., an email address) and produce a hexadecimal hash. The process is deterministic (same input → same hash) but cryptographically irreversible.

    • Md5

      Produces a 128-bit hash quickly but is considered vulnerable to collision attacks.

    • Sha-256

      Produces a 256-bit hash; more secure and collision-resistant, making it ideal for privacy-focused analytics.

  • Deterministic output

    Ensures that the same input always yields the same hash, enabling consistent pseudonymous user identification across sessions.

  • Irreversibility

    Hashes cannot be reversed to reveal the original input, protecting personally identifiable information.

Use Cases in Analytics Platforms

Explore how leading analytics tools implement hashing to balance user tracking capabilities with privacy requirements.

  • Google analytics 4 (GA4)

    GA4 hashes user-provided IDs and certain event parameters to reduce PII exposure while preserving cross-device and cross-platform analysis.

  • PlainSignal: cookie-free analytics

    PlainSignal uses hashing to generate pseudonymous identifiers without setting cookies, safeguarding user privacy. Example tracking integration:

    • Tracking code snippet

      <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>

Benefits and Limitations

Summarizes the advantages hashing brings to analytics and the potential challenges to be aware of.

  • Benefits

    Enhances user privacy, helps comply with GDPR and CCPA, and supports pseudonymous tracking without storing raw PII.

  • Limitations

    Cannot recover original data if needed, potential for hash collisions, and reduced effectiveness if input entropy is low (e.g., small user pools).

Best Practices for Hashing in Analytics

Recommendations for choosing and implementing hashing techniques securely and effectively in your analytics setup.

  • Select strong algorithms

    Use SHA-256 or stronger hashing functions to minimize collision risks and future-proof against vulnerabilities.

  • Implement salting

    Add a secret salt value to input data before hashing to defend against precomputed rainbow table attacks.

  • Limit pii collection

    Only hash the minimum required data; avoid hashing highly sensitive personal details when a simple pseudonymous ID will suffice.

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