Adobe Experience Platform (AEP) and Customer Journey Analytics (CJA) continue to emerge as the preferred solutions for organizations seeking a unified, 360‑degree view of customer behavior. For organizations requiring HIPAA compliance, AEP and CJA is a necessity. Many organizations are now having discussions about whether they should retool or retire their legacy Adobe Analytics implementations. The transition from Adobe Analytics to CJA is far more complex than simply disabling an old tool. Teams must carefully plan, perform detailed analysis, and develop a structured approach to ensure that reporting continuity, data integrity, and downstream dependencies remain intact.
Adobe Analytics remains a strong platform for organizations focused exclusively on web and mobile app measurement; however, enterprises that are prioritizing cross‑channel data activation, real‑time profiles, and detailed journey analysis should embrace AEP as the future. Of course, you won’t be maintaining two platforms after building out CJA so you must think about how to move on from Adobe Analytics.
Decommissioning Options and Key Considerations
You can approach decommissioning Adobe Analytics in several ways. Your options include: 1) disabling the extension; 2) adding an s.abort at the top of the AppMeasurement custom‑code block to prevent data from being sent to Adobe Analytics; 3) deleting all legacy rules; or 4) discarding Adobe Analytics entirely and creating a new Launch property for CJA. Although multiple paths exist, the best approach almost always involves preserving your data‑collection methods and keeping the historical Adobe Analytics data. You have likely collected that data for years, and you want it to remain meaningful after migration. Instead of wiping everything out, you can update Launch by removing rules you no longer need or by eliminating references to Adobe Analytics.
Recognizing the challenges involved in going through the data to make the right decisions during this process, I have developed a specialized tool – Analytics Decommissioner (AD) — designed to support organizations as they decommission Adobe Analytics and transition fully to AEP and CJA. The tool programmatically evaluates Adobe Platform Launch implementations using several Adobe API endpoints, enabling teams to quickly identify dependencies, references, and potential risks associated with disabling Adobe Analytics components.
Why Decommissioning Requires More Than a Simple Shutdown
One of the most significant obstacles in decommissioning Adobe Analytics is identifying where legacy tracking still exists and where removing Adobe Analytics could potentially break the website or cause errors. Over the years, many organizations accumulate layers of custom code, extensions, and tracking logic that reference Adobe Analytics variables—often in places that are not immediately obvious. These references may include s. object calls, hard‑coded AppMeasurement logic, or conditional rules created over the course of several years. Without a systematic way to surface dependencies, teams risk breaking critical data flows that feed CJA or AEP datasets.
Missing or outdated documentation makes the problem even harder. Many organizations fail to maintain complete or current solution design references (SDRs), especially for older implementations. As a result, teams rely on tribal knowledge, attempts to recall discussions from years ago, or a manual inspection of data collected to understand how the system collects data. This approach moves slowly, introduces errors, and cannot support large‑scale environments. When documentation lacks clarity, teams struggle to identify which rules, data elements, or custom scripts still matter and which they can safely remove. Now imagine repeating this process for every one of your Launch properties.
This is where Perficient and the AD tool provide significant value.
The AD tool programmatically scans Launch properties and uncovers dependencies that teams may have forgotten or never documented. A manual analysis might easily overlook these dependencies. AD also pinpoints where custom code still references Adobe Analytics variables, highlights rules that have been modified or disabled since deployment, and surfaces AppMeasurement usage that could inadvertently feed into CJA or AEP data ingestion. This level of visibility is essential for ensuring that the decommissioning process does not disrupt data collection or reporting.
How Analytics Decommissioner (AD) Works
The tool begins by scanning all Launch properties across your organization and asking the user to select a property. This is necessary because the decommissioning process must be done on each property individually. This is the same way data is set for Adobe Analytics, one Launch property at a time. Once a property is selected, the tool retrieves all production‑level data elements, rules, and rule components, including their revision histories. The tool ignores rules and data element revisions that developers disabled or never published (placed in production). The tool then performs a comprehensive search for AppMeasurement references and Adobe Analytics‑specific code patterns. These findings show teams exactly where legacy tracking persists and see what needs to be updated or modified and which items can be safely removed. If no dependencies exist, AD can disable the rules and create a development library for testing. When AD cannot confirm that a dependency exists, it reports the rule names and components where potential issues exist and depend on development experts to make the decision about the existence of a dependency. The user always makes the final decisions.
This tool is especially valuable for large or complex implementations. In one recent engagement, a team used it to scan nearly 100 Launch properties. Some of those properties included more than 300 data elements and 125 active rules. Attempting to review this level of complexity manually would have taken weeks and the risk would remain that critical dependencies are missed. Programmatic scanning ensures accuracy, completeness, and efficiency. This allows teams to move forward with confidence.
A Key Component of a Recommended Decommissioning Approach
The AD tool and a comprehensive review are essential parts of a broader, recommended decommissioning framework. A structured approach typically includes:
- Inventory and Assessment – Identifying all Adobe Analytics dependencies across Launch, custom code, and environments.
- Mapping to AEP/CJA – Ensuring all required data is flowing into the appropriate schemas and datasets.
- Gap Analysis – Determining where additional configuration or migration work needs to be done.
- Remediation and Migration – Updating Launch rules, removing legacy code, and addressing undocumented dependencies.
- Validation and QA – Confirming that reporting remains accurate in CJA after removal of Launch rules and data elements created for Adobe Analytics.
- Sunset and Monitoring – Disabling AppMeasurement, removing Adobe Analytics extensions, and monitoring for errors.
Conclusion
Decommissioning Adobe Analytics is a strategic milestone in modernizing the digital data ecosystem. Using the right tools and having the right processes are essential. The Analytics Decommissioner tool allows organizations to confidently transition to AEP and CJA. This approach to migration preserves data quality, reduces operational costs, and strengthens governance when teams execute it properly. By using the APIs and allowing the AD tool to handle the heavy lifting, teams ensure that they don’t overlook any dependencies. This will enable a smooth and risk‑free transition with robust customer experience analytics.
