Data validation techniques in server-side tagging

Every day, companies collect gigabytes of data about their customers: what they view, buy, and interact with the brand. This data is necessary to better understand users and offer them the most relevant products and services. However, without high-quality data verification, even the most expensive analytical systems will not help — errors missed at the collection stage can nullify the efforts of marketers, analysts, and managers. That is why data verification has become integral to working with server tracking.

In this article, we will examine data validation in server tracking, the techniques used to confirm information accuracy, the errors that should be avoided, and why correct validation is critical for modern companies. This material will help you lay a solid foundation for building an effective system for working with data and not wasting resources.

Data validation is not just a technical formality, but a critical stage in working with information. At this stage, we make sure that all the collected data is accurate, complete, and consistent even before it enters the systems for storage, analysis, or transmission. The main goal is to ensure that the information meets the required formats, quality standards, and specific business requirements. After all, even a small mistake can be expensive – leading to incorrect conclusions and decisions.

Several basic data validation techniques help maintain the quality of information in the server tracking process:

As in any other process, server-side tracking requires ensuring all data corresponds to a certain format. Imagine a database of contacts where a person indicated just a name or a set of random characters instead of the correct email address. To avoid such situations, the data must correspond to a specific format: for example, the email address must contain the "@" symbol and the domain. Incorrect format can lead to failures in further processing or incorrect analytical conclusions. Object Builder variable for sGTM, for example, provides some simple built-in tools for rudimentary validation such as  Format Value on all variables or regex matching inside triggers.

Some data in the server-side tracking is critical for further processing, so they cannot be left empty. For example, all analytics may lose meaning without user ID, event time, or event type. Therefore, mandatory fields must be monitored carefully — their presence directly affects data quality.

Another important step is ensuring the numerical data has adequate values. Logically, the number of goods in the order cannot be negative, and the user's age hardly exceeds several hundred years. Such a check helps to identify technical errors or inaccuracies even before the analysis.

The uniqueness of some data is the key to the correct operation of systems. For example, each session or transaction must have its own unique identifier. If the same transaction is committed twice, it will distort the accounting results. With the Unique Event ID variable provided by Stape, you also receive a Unique Event ID in many tracking scenarios, which is essential for processes like deduplication.

Here, we are talking about checking the relationship between the data. For example, the order completion date must be later than the order creation date. Such elementary, at first glance, things can avoid serious logical errors.

When data comes from different sources or is stored in several database tables, it is essential to affirm they relate correctly. Integrity checking helps avoid disagreements and compiles all information into a single, understandable picture.

Even in the best systems, errors in data validation occur from time to time. Here's what you should pay special attention to:

For data to be truly reliable, it is essential to build verification into all stages of their processing:

Check the correctness of the information at the moment of its collection— be it a website, a mobile application, or a CRM system.Which is why any decent server-side tagging implementation most of the time starts with a good dataLayer definition, documentation and testing. This secures easier management down the pipeline by avoiding accumulation of errors.

After the data arrives on the server, it is worthwhile to recheck it before sending it to analytics or advertising systems. This is one of the main strengths of a server-side Google Tag Manager. By effectively having a middleware between a collector and storage, you can validate and/or transform data before it feeds your reporting. 

Set up data validation error notification systems to detect and correct problems in time. Use Stape’s Logs and Monitoring features to stay informed about any anomalies or tracking issues as soon as they occur.

For the data validation process to be really effective and maintain the quality of the data at a high level, it is worth following several essential recommendations:

1. Create clear data specifications.

To ensure accurate and reliable data collection, it's essential to define clear rules for each field in your tracking setup. This includes specifying the expected data type, whether the field is mandatory, acceptable values, and the correct format. Many platforms, such as GA4, provide predefined schemas and formatting requirements. These systems differ in how strictly they enforce these rules—some may reject an entire event if even a single schema condition is violated.

2. Automate verification.

Manual verification is only suitable for small amounts of data. For medium and large volumes, implement scripts and automatic validation services. In a GTM environment, most of the time the tag templates (such as ones from Stape) will do the bulk of normalisation and verification of data input, nevertheless always check data-in and data-out in preview mode. Debug as needed

3. Implement multi-step verification.

It is better to catch errors immediately during data collection than to detect them already in analytical reports. The check should be multi-level: on the client side, on the server, and when transferring to other systems. Although the balance of client/server/destination validation can skew either way based on details of any particular situation, the server-sige tagging approach with GTM has both inherently, while of course allowing you to script your own to the needed extent.

4. Test the verification system regularly.

Data validation must be a live process. It needs to be constantly improved, adapted to new business requirements, changes in data formats and changes in legislation.

Here’s what your company gains when you implement proper data validation within your server-side tagging setup:

In today's world, data is the basis for business development. Validation in server tracking helps companies avoid critical errors at the information collection stage: check the correctness of formats, mandatory fields, data logic, and its integrity. Companies are laying a solid foundation for sustainable growth by investing in proper data management.