# Data reliability via server-side Data Quality

<figure><img src="https://1613709692-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F0zW0zvGDZaiNuIFweCtu%2Fuploads%2FMyKx5XhbW0ESI2UqxALz%2Fss_DataQuality.png?alt=media&#x26;token=0284d967-43eb-419e-a1dc-8e93d00f1f90" alt=""><figcaption></figcaption></figure>

### 1. Business Value: Guarantee actionable and certified data

The value of your tools (Analytics, CRM, Advertising) depends directly on the quality of the data they receive. Server-side without control creates a ‘black box’, reliability allows you to open this box and guarantee the integrity of each hit.

* **Truth-based decisions:** Eliminate analytical biases caused by poorly formatted or missing data that skew your reports.
* **Frictionless interoperability:** Send unique, standardised data to all your partners. If a supplier changes their format, you can correct it on the server without touching the site code.
* **Reduction of technical debt**: Data cleansing allows you to fix collection errors immediately, avoiding complex correction work on your website or application.

### 2. Implementation methodology

#### Step A: Definition of the expected schema (Event Specification)

Create the repository for your tagging plan at the server level. For each event (e.g. purchase, login, page\_view), the mandatory properties and the expected data type.

* **Action**: Configure your schemas in the Data Quality module.
* **Documentation**: [Event Specification](https://doc.commandersact.com/features/data-quality/event-specification).

#### Step B: Monitoring and Diagnosis (Data Quality)

The system checks each incoming event. The Data Quality report immediately identifies sources that send non-compliant or undocumented data.

* **Action**: Monitor the health score of your server-side sources.
* **Documentation**: [Rapport Data Quality](https://doc.commandersact.com/features/data-quality/data-quality).

#### **Step C: Real-time Correction (Data Cleansing)**

If an anomaly is detected (e.g., a missing currency or an incorrect date format), do not reject the data. Use Data Cleansing to normalise it before sending.

* **Action**: Apply transformation rules to correct, enrich, or anonymise properties on the fly.
* **Documentation**: [Data Cleansing](https://doc.commandersact.com/features/data-quality/data-cleansing).

#### **Step D: Proactive Alerting**

Set up critical alerts. Be notified as soon as a server-side source starts sending invalid data, allowing you to take action before your destinations (Analytics, CRM, Ads) are impacted.

### 3. Typical use cases

1. **Analytics & Ads standardisation**: Ensure that the purchase event sent to GA4 and Meta CAPI contains exactly the same amounts and currencies thanks to a unique server-side cleansing rule.
2. **On-the-fly data enrichment:** When a customer ID arrives at the server, retrieve additional information (segmentation, loyalty) to enrich the hit before its final distribution.
3. **Filtering and security:** Use Data Cleansing to automatically mask sensitive information (PII) detected in your server flows in order to comply with your partners' privacy policies.

### Need help making your data flows more reliable?

Data Quality Server-Side transforms your data flow into a strategic and certified asset. Our experts help you build your schemas and cleansing rules for total control over your data collection. **Contact our support team:** <support@commandersact.com>